Language modelActive

Mistral Medium 3.5

Mistral AI

Released: April 28, 2026
Data date: September 8, 2026
Predecessor: Mistral Medium 3.1 (Official documentation)

Benchmark-Meta-Score: Unavailable
Minimum coverage not met
Speed: 147.8 tokens/s
Output throughput
Price: $1.5 · $7.5
Input · Output / 1M tokens
Input: Text, Image
Output: Text

New in Mistral Medium 3.5 is the combination of 128 billion dense parameters, text and image input, and a 256,000-token context window.

Mistral describes Medium 3.5 as the successor to Mistral Medium 3.1. The Modified MIT license sets the deployment terms.

Specifications and access

Specification	Value and source
Weights	Open weightsSource
Access	API, LocalSource
Parameters	128BSource
Context window	262,144 tokensSource
Notes	Coding-focused current API model; dense 128B modelSource
Provider model name	Mistral Medium 3.5Source
License	Modified MITSource
Release	2026-04-28Source
API model ID	mistral-medium-3-5Source
Parameters	128 BSource
Architecture	dense transformerSource
Input	Text, ImageSource
Output	TextSource
Reasoning mode	YesSource
Tool calling	YesSource
Structured output	YesSource

Benchmark meta-analysis

Overall and area scores

These Gradually indexes place selected published measurements on a 0 to 100 scale. They are not accuracy percentages and do not guarantee results in your application.

Retrieval dates of the sources used 2026-10-15 - 2026-10-19

Overall index

Not enough coverage for a score yet

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 37 %
Test families: 31 / 71
Sources: 3
Mathematical bounds with missing values: 6.1-68.8 / 100

Coding

Not enough coverage for a score yet

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 29 %
Test families: 5 / 15
Sources: 3
Mathematical bounds with missing values: 2.7-73.8 / 100

Reasoning

Not enough coverage for a score yet

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 43 %
Test families: 4 / 7
Sources: 2
Mathematical bounds with missing values: 7.8-64.9 / 100

Mathematics

Not enough coverage for a score yet

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 20 %
Test families: 1 / 5
Sources: 1
Mathematical bounds with missing values: 2.1-82.1 / 100

Knowledge

Index 18.0 / 100

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 67 %
Test families: 2 / 3
Sources: 2
Mathematical bounds with missing values: 12.0-45.4 / 100

Agents

Not enough coverage for a score yet

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 33 %
Test families: 4 / 12
Sources: 1
Mathematical bounds with missing values: 9.1-75.8 / 100

General

Not enough coverage for a score yet

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 40 %
Test families: 2 / 5
Sources: 2
Mathematical bounds with missing values: 13.3-73.3 / 100

Finance

Index 10.3 / 100

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 86 %
Test families: 6 / 7
Sources: 1
Mathematical bounds with missing values: 8.8-23.1 / 100

Legal

Index 16.9 / 100

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 50 %
Test families: 2 / 4
Sources: 1
Mathematical bounds with missing values: 8.4-58.4 / 100

Health

Index 11.1 / 100

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 67 %
Test families: 2 / 3
Sources: 1
Mathematical bounds with missing values: 7.4-40.8 / 100

Multimodal

Not enough coverage for a score yet

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 38 %
Test families: 2 / 4
Sources: 2
Mathematical bounds with missing values: 4.0-66.5 / 100

Education

Not enough coverage for a score yet

provisional

Published configurations differ within this comparison cohort. Treat the index as provisional.

Panel-weight coverage: 50 %
Test families: 1 / 2
Sources: 1
Mathematical bounds with missing values: 9.5-59.5 / 100

Documented method · Included test families and measurements

Method version: benchmark-meta-score-v1

The index includes active measurements from reviewed primary sources when at least five models are compared in the same documented comparison slice across dataset, harness, agent, tools, and trial count. Each benchmark series and evaluator contributes one shared setup slice from the newest capture; when dates match, the wider slice is used. When one source and series include several explicitly documented reasoning tiers, the highest documented tier (none through max) is selected, never the best result value. The explicit source label “Non-reasoning” is treated as none. When selecting between multiple results, unknown or ambiguous tiers exclude the model from that series. Unambiguous single configurations do not require a tier ordering. The best individual result for each model is not selected. Equal raw values share the same rank percentile. Multiple series from the same evaluator within a family receive equal weight; evaluators within a family, families within an area, and areas in the overall index then receive equal weight. Unmeasured series remain visible in coverage and bounds. An overall score requires at least four areas, six test families, and two sources. An area score requires at least two test families. Every overall and area score also requires at least 50% of its panel weight.

Published model-specific parameters can differ. This is not a controlled experiment; comparisons with such differences are marked provisional.

Unmeasured portions are not entered as zero. They reduce coverage and contribute only to the displayed bounds. Scores with different coverage are therefore not equally complete.

The bounds hold observed percentiles fixed and allow missing contributions anywhere from 0 to 100. They are not confidence intervals or predictions of future recalculations.

The source count is not a count of independent experiments.

OECD handbook on composite indicators

All 31 test families and 12 of 33 raw measurements are listed here. The complete calculation for every model is available in the JSON export.

Test family: AA-AnalystAgent
Agents · Family index: 13.2 / 100
Nominal family weight in the overall panel: 0.6 % · Coverage of this test family: 100 %
- Artificial Analysis
  Published configurations differ within this cohort.
  Measurement:
  artificial-analysis-aa-analyst-agent
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  0.6 %
  Raw value:
  12.5 %
  Metric:
  pass^5 reliability
  Version:
  AA cohort: 80 questions
  Participating models:
  39
  Rank percentile within the cohort:
  13.2 %
  Retrieved:
  2026-10-19
  Comparison cohort:
  artificial-analysis-aa-analyst-agent:AA cohort: 80 questions:standalone:reasoning:tokens=2000:suffix=unspecified
  Documented comparison setup
  Publication cohort:
  ["artificial-analysis-aa-analyst-agent","AA cohort: 80 questions","aa-model-page-2026-10-19-8a022369738c"]
  Dataset type:
  artificial-analysis-evaluation
  Harness:
  Artificial Analysis Stirrup
  Agent:
  Stirrup analyst agent
  Tool policy:
  100-turn cap; Python sandbox with source files, URL fetch, image viewing, and answer submission
  Attempts:
  pass^5, 5 independent attempts per question
  Setup and model parameters
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption. 80 quantitative questions across 14 domains are run five independent times. pass^5 counts only questions answered correctly in all five attempts.. Harness: Artificial Analysis Stirrup. Agent: Stirrup analyst agent. Tool policy: 100-turn cap; Python sandbox with source files, URL fetch, image viewing, and answer submission. Attempts: pass^5, 5 independent attempts per question. Reasoning: reasoning enabled
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption. 80 quantitative questions across 14 domains are run five independent times. pass^5 counts only questions answered correctly in all five attempts.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Harness: Artificial Analysis Stirrup. Agent: Stirrup analyst agent. Tool policy: 100-turn cap; Python sandbox with source files, URL fetch, image viewing, and answer submission. Attempts: pass^5, 5 independent attempts per question. Reasoning: reasoning enabled. Score display: 12.5%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Test family: AA-Briefcase
Agents · Family index: 30.5 / 100
Nominal family weight in the overall panel: 0.6 % · Coverage of this test family: 100 %
- Artificial Analysis
  Published configurations differ within this cohort.
  Measurement:
  artificial-analysis-aa-briefcase
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  0.6 %
  Raw value:
  522.97 Elo
  Metric:
  AA-Briefcase Elo
  Version:
  v1.1
  Participating models:
  83
  Rank percentile within the cohort:
  30.5 %
  Retrieved:
  2026-10-19
  Comparison cohort:
  artificial-analysis-aa-briefcase:v1.1:v4.3.2:reasoning:tokens=2000:suffix=unspecified
  Documented comparison setup
  Publication cohort:
  ["artificial-analysis-aa-briefcase","v1.1","aa-model-page-2026-10-19-8a022369738c"]
  Dataset type:
  artificial-analysis-evaluation
  Setup and model parameters
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Reasoning: reasoning enabled
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Reasoning: reasoning enabled. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Test family: AA-LCR
Reasoning · Family index: 24.4 / 100
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
- Artificial Analysis
  Published configurations differ within this cohort.
  Measurement:
  artificial-analysis-aa-lcr
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  1.02 %
  Raw value:
  69.3333333333333 %
  Metric:
  Pass rate
  Version:
  v1.1
  Participating models:
  83
  Rank percentile within the cohort:
  24.4 %
  Retrieved:
  2026-10-19
  Comparison cohort:
  artificial-analysis-aa-lcr:v1.1:v4.3.2:reasoning:tokens=2000:suffix=unspecified
  Documented comparison setup
  Publication cohort:
  ["artificial-analysis-aa-lcr","v1.1","aa-model-page-2026-10-19-8a022369738c"]
  Dataset type:
  artificial-analysis-evaluation
  Setup and model parameters
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Reasoning: reasoning enabled
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Reasoning: reasoning enabled. Score display: 69.3%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Test family: AutomationBench-AA
Agents · Family index: 37.8 / 100
Nominal family weight in the overall panel: 0.6 % · Coverage of this test family: 100 %
- Artificial Analysis
  Published configurations differ within this cohort.
  Measurement:
  artificial-analysis-automationbench-aa
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  0.6 %
  Raw value:
  6.299648656993127 %
  Metric:
  Objectives completed without guardrail violations
  Version:
  unspecified
  Participating models:
  83
  Rank percentile within the cohort:
  37.8 %
  Retrieved:
  2026-10-19
  Comparison cohort:
  artificial-analysis-automationbench-aa:unspecified:v4.3.2:reasoning:tokens=2000:suffix=unspecified
  Documented comparison setup
  Publication cohort:
  ["artificial-analysis-automationbench-aa","unspecified","aa-model-page-2026-10-19-8a022369738c"]
  Dataset type:
  artificial-analysis-evaluation
  Setup and model parameters
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Reasoning: reasoning enabled
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Reasoning: reasoning enabled. Score display: 6.3%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Test family: GDP.pdf
Reasoning · Family index: 19.5 / 100
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
- Artificial Analysis
  Published configurations differ within this cohort.
  Measurement:
  artificial-analysis-gdp-pdf
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  1.02 %
  Raw value:
  2.8000000000000003 %
  Metric:
  All-pass rate
  Version:
  unspecified
  Participating models:
  83
  Rank percentile within the cohort:
  19.5 %
  Retrieved:
  2026-10-19
  Comparison cohort:
  artificial-analysis-gdp-pdf:unspecified:v4.3.2:reasoning:tokens=2000:suffix=unspecified
  Documented comparison setup
  Publication cohort:
  ["artificial-analysis-gdp-pdf","unspecified","aa-model-page-2026-10-19-8a022369738c"]
  Dataset type:
  artificial-analysis-evaluation
  Setup and model parameters
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Reasoning: reasoning enabled
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Reasoning: reasoning enabled. Score display: 2.8%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Test family: GDPval-AA
Agents · Family index: 28.0 / 100
Nominal family weight in the overall panel: 0.6 % · Coverage of this test family: 100 %
- Artificial Analysis
  Published configurations differ within this cohort.
  Measurement:
  artificial-analysis-gdpval-aa
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  0.6 %
  Raw value:
  760.86 Elo
  Metric:
  GDPval-AA Elo
  Version:
  v2.1
  Participating models:
  83
  Rank percentile within the cohort:
  28.0 %
  Retrieved:
  2026-10-19
  Comparison cohort:
  artificial-analysis-gdpval-aa:v2.1:v4.3.2:reasoning:tokens=2000:suffix=unspecified
  Documented comparison setup
  Publication cohort:
  ["artificial-analysis-gdpval-aa","v2.1","aa-model-page-2026-10-19-8a022369738c"]
  Dataset type:
  artificial-analysis-evaluation
  Setup and model parameters
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Reasoning: reasoning enabled
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Reasoning: reasoning enabled. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Test family: IFBench
General · Family index: 63.8 / 100
Nominal family weight in the overall panel: 1.43 % · Coverage of this test family: 100 %
- Artificial Analysis
  Published configurations differ within this cohort.
  Measurement:
  artificial-analysis-ifbench
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  1.43 %
  Raw value:
  68.7755102040816 %
  Metric:
  IFBench prompt-level loose accuracy (pass@1)
  Version:
  AA test set: 294 questions
  Participating models:
  70
  Rank percentile within the cohort:
  63.8 %
  Retrieved:
  2026-10-19
  Comparison cohort:
  artificial-analysis-ifbench:AA test set: 294 questions:standalone:reasoning:tokens=2000:suffix=unspecified
  Documented comparison setup
  Publication cohort:
  ["artificial-analysis-ifbench","AA test set: 294 questions","aa-model-page-2026-10-19-8a022369738c"]
  Dataset type:
  artificial-analysis-evaluation
  Tool policy:
  Single-turn answer evaluated with the official IFBench source code
  Attempts:
  pass@1, 5 repeats per question
  Setup and model parameters
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: reasoning enabled
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: reasoning enabled. Score display: 68.8%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Test family: AA-Omniscience
Knowledge · Family index: 24.4 / 100
Nominal family weight in the overall panel: 2.38 % · Coverage of this test family: 100 %
- Artificial Analysis
  Published configurations differ within this cohort.
  Measurement:
  artificial-analysis-omniscience
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  2.38 %
  Raw value:
  -36.8 points
  Metric:
  AA-Omniscience Index
  Version:
  unspecified
  Participating models:
  83
  Rank percentile within the cohort:
  24.4 %
  Retrieved:
  2026-10-19
  Comparison cohort:
  artificial-analysis-omniscience:unspecified:v4.3.2:reasoning:tokens=2000:suffix=unspecified
  Documented comparison setup
  Publication cohort:
  ["artificial-analysis-omniscience","unspecified","aa-model-page-2026-10-19-8a022369738c"]
  Dataset type:
  artificial-analysis-evaluation
  Setup and model parameters
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Reasoning: reasoning enabled
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Reasoning: reasoning enabled. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Test family: GPQA Diamond
Reasoning · Family index: 1.3 / 100
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 50 %
- Vals AI
  Published configurations differ within this cohort.
  Measurement:
  vals-gpqa
  Series weight within the test family:
  50 %
  Nominal contribution to the overall panel:
  0.51 %
  Raw value:
  34.848 %
  Metric:
  accuracy
  Version:
  1
  Participating models:
  77
  Rank percentile within the cohort:
  1.3 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  vals-gpqa:1:overall
  Reasoning tier:
  high
  Documented comparison setup
  Publication cohort:
  vals-gpqa:1:overall
  Dataset type:
  public
  Setup and model parameters
  Reasoning effort: high. Temperature: 0.7. Top P: 0.95. Maximum output tokens: 80000
  Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Reasoning effort: high. Temperature: 0.7. Top P: 0.95. Maximum output tokens: 80000. Latency in seconds: 256.17. Standard error: 2.39. Cost per task in USD: 0.095489. Source ID: vals-ai. Evidence capture ID: vals-gpqa-2026-10-15-951eb5224097. Source payload hash: 951eb5224097733a9d09d06c14908ae2980e2e640e539f2c284877c5092d7077. Parser version: vals-astro-v3. Imported at: 2026-10-15
Test family: Humanity’s Last Exam
Reasoning · Family index: 19.5 / 100
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 50 %
- Artificial Analysis
  Published configurations differ within this cohort.
  Measurement:
  artificial-analysis-humanitys-last-exam
  Series weight within the test family:
  50 %
  Nominal contribution to the overall panel:
  0.51 %
  Raw value:
  13.7627432808156 %
  Metric:
  Text-only pass@1 accuracy
  Version:
  unspecified
  Participating models:
  83
  Rank percentile within the cohort:
  19.5 %
  Retrieved:
  2026-10-19
  Comparison cohort:
  artificial-analysis-humanitys-last-exam:unspecified:v4.3.2:reasoning:tokens=2000:suffix=unspecified
  Documented comparison setup
  Publication cohort:
  ["artificial-analysis-humanitys-last-exam","unspecified","aa-model-page-2026-10-19-8a022369738c"]
  Dataset type:
  artificial-analysis-evaluation
  Setup and model parameters
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Reasoning: reasoning enabled
  Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Reasoning: reasoning enabled. Score display: 13.8%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Test family: LMArena Code Arena WebDev
Coding · Family index: 2.8 / 100
Nominal family weight in the overall panel: 0.48 % · Coverage of this test family: 100 %
- LMArena
  Published configurations differ within this cohort.
  Measurement:
  lmarena-code-webdev
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  0.48 %
  Raw value:
  1,263.3829384837534 rating
  Metric:
  WebDev Arena Bradley-Terry rating
  Version:
  webdev
  Participating models:
  37
  Rank percentile within the cohort:
  2.8 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  lmarena-code-webdev:2026-10-13:overall
  Documented comparison setup
  Publication cohort:
  lmarena-code-webdev:2026-10-13:overall
  Dataset type:
  public-crowdsourced-preferences
  Harness:
  Arena Code Arena WebDev
  Setup and model parameters
  Arena rating from 2,320 published pairwise votes.. Harness: Arena Code Arena WebDev
  Arena rating from 2,320 published pairwise votes.. Source model ID: lmarena/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Model snapshot: mistral-medium-3.5. Evaluator: LMArena. Dataset type: public-crowdsourced-preferences. Leaderboard date: 2026-10-13. Harness: Arena Code Arena WebDev. Source ID: lmarena. Evidence capture ID: lmarena-webdev-4589dee83b594891961ae5190c508a9288d2c2ae-28f8076e6c27. Source payload hash: 28f8076e6c27f1f89239f3d7243456357dfa175b078646a6bd859935b162cc9c. Parser version: lmarena-dataset-v1. Imported at: 2026-10-15
Test family: LMArena Text, Style Control
General · Family index: 2.8 / 100
Nominal family weight in the overall panel: 1.43 % · Coverage of this test family: 100 %
- LMArena
  Published configurations differ within this cohort.
  Measurement:
  lmarena-text-style-control
  Series weight within the test family:
  100 %
  Nominal contribution to the overall panel:
  1.43 %
  Raw value:
  1,426.6051088690308 rating
  Metric:
  style-controlled Bradley-Terry rating
  Version:
  style-control-v1
  Participating models:
  37
  Rank percentile within the cohort:
  2.8 %
  Retrieved:
  2026-10-15
  Comparison cohort:
  lmarena-text-style-control:2026-10-13:overall
  Documented comparison setup
  Publication cohort:
  lmarena-text-style-control:2026-10-13:overall
  Dataset type:
  public-crowdsourced-preferences
  Harness:
  Arena Text, style control
  Setup and model parameters
  Arena rating from 11,793 published pairwise votes.. Harness: Arena Text, style control
  Arena rating from 11,793 published pairwise votes.. Source model ID: lmarena/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Model snapshot: mistral-medium-3.5. Evaluator: LMArena. Dataset type: public-crowdsourced-preferences. Leaderboard date: 2026-10-13. Harness: Arena Text, style control. Source ID: lmarena. Evidence capture ID: lmarena-4589dee83b594891961ae5190c508a9288d2c2ae-a39cbed99996. Source payload hash: a39cbed99996f4993a4cd66d8322b2edb939f1ffdbd377c9d3769edd01dd78ca. Parser version: lmarena-dataset-v1. Imported at: 2026-10-15
Test family: LMArena Vision, Style Control
Multimodal · Family index: 3.3 / 100
Nominal family weight in the overall panel: 1.79 % · Coverage of this test family: 100 %
Test family: MMMU-Pro
Multimodal · Family index: 25.0 / 100
Nominal family weight in the overall panel: 1.79 % · Coverage of this test family: 50 %
Test family: SWE-bench Verified
Coding · Family index: 15.3 / 100
Nominal family weight in the overall panel: 0.48 % · Coverage of this test family: 50 %
Test family: Terminal-Bench
Coding · Family index: 17.2 / 100
Nominal family weight in the overall panel: 0.48 % · Coverage of this test family: 83 %
Test family: Code Migration
Coding · Family index: 9.7 / 100
Nominal family weight in the overall panel: 0.48 % · Coverage of this test family: 100 %
Test family: CorpFin v2
Finance · Family index: 25.7 / 100
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
Test family: EMB
Finance · Family index: 3.4 / 100
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
Test family: Finance Agent (v2)
Finance · Family index: 10.0 / 100
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
Test family: Harvey's Legal Agent Benchmark
Legal · Family index: 24.6 / 100
Nominal family weight in the overall panel: 1.79 % · Coverage of this test family: 100 %
Test family: Legal Research Bench
Legal · Family index: 9.2 / 100
Nominal family weight in the overall panel: 1.79 % · Coverage of this test family: 100 %
Test family: MedCode
Health · Family index: 14.9 / 100
Nominal family weight in the overall panel: 2.38 % · Coverage of this test family: 100 %
Test family: MedScribe
Health · Family index: 7.4 / 100
Nominal family weight in the overall panel: 2.38 % · Coverage of this test family: 100 %
Test family: MMLU-Pro
Knowledge · Family index: 11.7 / 100
Nominal family weight in the overall panel: 2.38 % · Coverage of this test family: 100 %
Test family: MortgageTax
Finance · Family index: 0.0 / 100
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
Test family: ProofBench
Mathematics · Family index: 10.3 / 100
Nominal family weight in the overall panel: 1.43 % · Coverage of this test family: 100 %
Test family: SAGE
Education · Family index: 19.0 / 100
Nominal family weight in the overall panel: 3.57 % · Coverage of this test family: 100 %
Test family: Tax Agent Bench
Finance · Family index: 5.5 / 100
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
Test family: TaxEval v2
Finance · Family index: 17.1 / 100
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
Test family: Vibe Code Bench v1.1
Coding · Family index: 5.6 / 100
Nominal family weight in the overall panel: 0.48 % · Coverage of this test family: 100 %

Benchmark sources at a glance

Each evaluator measures different capabilities under its own conditions. Test series, composite indices and their components stay separate. The detailed results below retain their scales, configurations and source dates.

Evaluator	Test series	Composite indices	Recorded active measurements
Artificial Analysis	11	1	14
Vals AI	20	1	166
LMArena	3	0	40

Coverage is not a quality ranking. Overlapping tests and multiple settings do not count as independent experiments. Vendor claims, historical and disputed results are excluded from this overview and remain labelled in the detailed evidence.

Published benchmarks

Taste-Bench

Overall

502 technical decision points from engineering and research workflows. Each choice is presented in both answer orders. The overall score averages engineering and research and requires both answer orders to be correct. Unparseable responses count as wrong. Reasoning settings and output budget are part of the setup.

Note (Mistral Medium 3.5): 24 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

1.GPT-5.6 Sol59.7%

Note: 0 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

2.GPT-5.559.5%

Note: 0 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

3.Claude Opus 555.5%

Note: 0 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

4.Grok 4.554.6%

Note: 10 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

5.GPT-5.6 Terra54%

Note: 0 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

6.GLM-5.253.9%

Note: 17 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

7.Claude Sonnet 551.6%

Note: 0 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

8.GPT-5.6 Luna49%

Note: 0 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

9.MiniMax M345.3%

Note: 3 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

10.DeepSeek-V4-Flash43.3%

Note: 2 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

11.GPT-5.4 mini40.1%

Note: 112 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

12.Mistral Medium 3.537.7%

13.GPT-5.4 nano36.6%

Note: 93 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

14.Grok 4.20 Reasoning15.7%

Note: 459 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

higher is betterAs of September 26, 2026Scale: 0% - 100%

Source: Taste-Bench paper

|

gradually.ai

Methodology and context

Comparison cohort: taste-bench-paper-v2-502
Metric: both-orders-correct, engineering/research macro average
Scale: 0-100
Participants: 14
Methodology and context: Same prompt for all models. Each of 502 questions is shown twice with the options reversed. Average of engineering and research success rates, requiring both orders to be correct. 65,536-token output budget; unparseable answers count as wrong.. Source model ID: curated/mistral-medium-3. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Taste-Bench paper. Dataset type: public. Leaderboard date: 2026-09-23. Attempts: 502 questions, two option orders each. Reasoning effort: high. Maximum output tokens: 65536. Source ID: taste-bench-paper. Evidence capture ID: curated-taste-bench-paper-2026-09-26-38449b5fe794. Source payload hash: 38449b5fe794c44f4d44ddeacafc18597a8b641bb10d36ba132573673d74f285. Parser version: curated-v1. Imported at: 2026-10-15

Source: Taste-Bench paper

Health

MedCode

Overall

1.Grok 4.644.71%

2.Grok 4.543.29%

3.Mistral Medium 3.533.75%

higher is betterAs of October 15, 2026Scale: 0% - 100%

Source: Vals AI

|

gradually.ai

Methodology and context

Comparison cohort: vals-medcode:1:overall
Metric: accuracy
Scale: 0-100
Participants: 3
Methodology and context: Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-13. Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000. Latency in seconds: 34.879. Standard error: 1.148. Cost per task in USD: 0.05337. Source ID: vals-ai. Evidence capture ID: vals-medcode-2026-10-15-988a6ae1e07e. Source payload hash: 988a6ae1e07e33ce031572ed4a804be11c36ee97a1dd75cbe4740fff3b534f8c. Parser version: vals-astro-v3. Imported at: 2026-10-15

Source: Vals AI

MedScribe

Overall

1.Grok 4.586.88%

2.Grok 4.686.53%

3.Mistral Medium 3.567.73%

higher is betterAs of October 15, 2026Scale: 0% - 100%

Source: Vals AI

|

gradually.ai

Methodology and context

Comparison cohort: vals-medscribe:1:overall
Metric: accuracy
Scale: 0-100
Participants: 3
Methodology and context: Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-13. Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000. Latency in seconds: 104.053. Standard error: 2.011. Cost per task in USD: 0.157641. Source ID: vals-ai. Evidence capture ID: vals-medscribe-2026-10-15-271fc122ddc2. Source payload hash: 271fc122ddc2b0602bd6c7947f65a9a7f0d8cab94f6dc27d62a9380a9317f9c0. Parser version: vals-astro-v3. Imported at: 2026-10-15

Source: Vals AI

Education

SAGE

Sub-task	Mistral Medium 3.5 (This model)	Grok 4.5	Grok 4.6
Automata	30.16%	32.54% (best value in this row)	32.14%
Calculus	2.78%	59.57%	61.73% (best value in this row)
Linear Algebra	41.41%	46.96% (best value in this row)	41.06%
Overall	37.61% (best value in this row)	34.97%	28.9%
Partial Differential Equations	41.27% (best value in this row)	25.4%	13.49%

higher is betterAs of October 15, 2026Source: Vals AIbars scaled within each row, not across rows

Methodology and context

Metric: accuracy

Vals Multimodal Index

SWE-bench

1.GPT-5.6 Sol99.02%

2.Claude Fable 597.06%

3.Claude Opus 595.1%

3.Kimi K395.1%

5.GPT-5.6 Luna93.14%

6.Claude Opus 4.889.22%

7.Qwen 3.8 Max 090287.26%

8.Grok 4.586.28%

9.GPT-5.582.6%

10.Claude Opus 4.782.35%

11.Claude Sonnet 4.677.45%

11.Gemini 3.6 Flash77.45%

13.Claude Sonnet 575.49%

13.Gemini 3.5 Flash75.49%

13.Inkling75.49%

13.Inkling Small75.49%

17.Kimi K2.674.51%

18.GPT-5.6 Terra73.53%

19.Gemini 3.1 Pro Preview72.55%

20.Grok 4.370.59%

21.MiniMax M369.61%

22.Gemini 3 Flash Preview68.63%

23.GPT-5.4 mini67.65%

23.GPT-5.4 nano67.65%

25.Grok 4.20 Reasoning66.67%

25.Qwen 3.7 Plus66.67%

27.MiMo-V2.564.71%

27.Mistral Medium 3.564.71%

29.Gemini 3.1 Flash-Lite57.84%

higher is betterAs of October 15, 2026Scale: 0% - 100%

Source: Vals AI

|

gradually.ai

Methodology and context

Comparison cohort: vals-vals-multimodal-index:1.2:swebench
Metric: benchmark score
Scale: 0-100
Participants: 29
Methodology and context: Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-11. Latency in seconds: 854.891. Standard error: 4.732. Cost per task in USD: 4.288697. Source ID: vals-ai. Evidence capture ID: vals-vals_multimodal_index-2026-10-15-077b22575f6e. Source payload hash: 077b22575f6e075b7b78157d000ec8f94d7ead409c7dcbcf04887630ed572a2d. Parser version: vals-astro-v3. Imported at: 2026-10-15

Source: Vals AI

Measurements without matching peer values

These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.

AA-AnalystAgent: Overall (Show measurement, test conditions, and source)

Source value: 12.5
Score: 12.5
Metric: pass^5 reliability
Unit: %
Benchmark version: AA cohort: 80 questions
Category: agentic
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: artificial-analysis-aa-analyst-agent:AA cohort: 80 questions:standalone:reasoning:tokens=2000:suffix=unspecified
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: active
Retrieved at: 2026-10-19
Methodology: Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption. 80 quantitative questions across 14 domains are run five independent times. pass^5 counts only questions answered correctly in all five attempts.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Harness: Artificial Analysis Stirrup. Agent: Stirrup analyst agent. Tool policy: 100-turn cap; Python sandbox with source files, URL fetch, image viewing, and answer submission. Attempts: pass^5, 5 independent attempts per question. Reasoning: reasoning enabled. Score display: 12.5%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Comparison methodology: Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption. 80 quantitative questions across 14 domains are run five independent times. pass^5 counts only questions answered correctly in all five attempts.. Harness: Artificial Analysis Stirrup. Agent: Stirrup analyst agent. Tool policy: 100-turn cap; Python sandbox with source files, URL fetch, image viewing, and answer submission. Attempts: pass^5, 5 independent attempts per question. Reasoning: reasoning enabled
Context: Questions, source files, and reference answers are private. pass^5 measures reliability across all five attempts, not pass@1.

Source Artificial Analysis

AA-LCR: Overall (Show measurement, test conditions, and source)

Source value: 69.3333333333333
Score: 69.3333333333333
Metric: Pass rate
Unit: %
Benchmark version: v1.1
Category: reasoning
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: artificial-analysis-aa-lcr:v1.1:v4.3.2:reasoning:tokens=2000:suffix=unspecified
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: active
Retrieved at: 2026-10-19
Methodology: Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Reasoning: reasoning enabled. Score display: 69.3%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Comparison methodology: Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Reasoning: reasoning enabled

Source Artificial Analysis

AutomationBench-AA: Overall (Show measurement, test conditions, and source)

Source value: 6.299648656993127
Score: 6.299648656993127
Metric: Objectives completed without guardrail violations
Unit: %
Benchmark version: unspecified
Category: agentic
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: artificial-analysis-automationbench-aa:unspecified:v4.3.2:reasoning:tokens=2000:suffix=unspecified
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: active
Retrieved at: 2026-10-19
Methodology: Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Source model ID: artificial-analysis/dd059b25-d82a-4ead-82a4-4adceaaec48b/mistral-medium-3-5/mistral-medium-3-5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral. Model snapshot: Mistral Medium 3.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Reasoning: reasoning enabled. Score display: 6.3%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-10-19-8a022369738c. Source payload hash: 8a022369738cbe2a0e2f9bf486a2882865dbb0c6f1396dfec84059a802560e3c. Parser version: artificial-analysis-rsc-models-v2. Imported at: 2026-10-19
Comparison methodology: Exact AA configuration: Mistral Medium 3.5 (release mistral-medium-3-5, row mistral-medium-3-5, UUID dd059b25-d82a-4ead-82a4-4adceaaec48b, reasoning=true, source reasoning-token configuration budget=2000, suffix=not specified). This configured value is not measured token consumption.. Reasoning: reasoning enabled

Source Artificial Analysis

Recorded measurement details

Taste-Bench: Overall (Show measurement, test conditions, and source)

Source value: 37.7
Score: 37.7
Metric: both-orders-correct, engineering/research macro average
Unit: %
Benchmark version: arxiv:2609.25804v2
Category: agentic
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: taste-bench-paper-v2-502
Source type: independent-evaluator
Evaluator: Taste-Bench paper
Status: active
Retrieved at: 2026-09-26
Methodology: Same prompt for all models. Each of 502 questions is shown twice with the options reversed. Average of engineering and research success rates, requiring both orders to be correct. 65,536-token output budget; unparseable answers count as wrong.. Source model ID: curated/mistral-medium-3. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Taste-Bench paper. Dataset type: public. Leaderboard date: 2026-09-23. Attempts: 502 questions, two option orders each. Reasoning effort: high. Maximum output tokens: 65536. Source ID: taste-bench-paper. Evidence capture ID: curated-taste-bench-paper-2026-09-26-38449b5fe794. Source payload hash: 38449b5fe794c44f4d44ddeacafc18597a8b641bb10d36ba132573673d74f285. Parser version: curated-v1. Imported at: 2026-10-15
Comparison methodology: Same prompt for all models. Each of 502 questions is shown twice with the options reversed. Average of engineering and research success rates, requiring both orders to be correct. 65,536-token output budget; unparseable answers count as wrong.
Context: 24 of 1,004 responses were unparseable and counted as wrong. Not a Gradually test.

Source Taste-Bench paper

MedCode: Overall (Show measurement, test conditions, and source)

Source value: 33.752
Score: 33.752
Metric: accuracy
Unit: %
Benchmark version: 1
Category: health
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: vals-medcode:1:overall
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-13. Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000. Latency in seconds: 34.879. Standard error: 1.148. Cost per task in USD: 0.05337. Source ID: vals-ai. Evidence capture ID: vals-medcode-2026-10-15-988a6ae1e07e. Source payload hash: 988a6ae1e07e33ce031572ed4a804be11c36ee97a1dd75cbe4740fff3b534f8c. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000

MedScribe: Overall (Show measurement, test conditions, and source)

Source value: 67.728
Score: 67.728
Metric: accuracy
Unit: %
Benchmark version: 1
Category: health
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: vals-medscribe:1:overall
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-13. Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000. Latency in seconds: 104.053. Standard error: 2.011. Cost per task in USD: 0.157641. Source ID: vals-ai. Evidence capture ID: vals-medscribe-2026-10-15-271fc122ddc2. Source payload hash: 271fc122ddc2b0602bd6c7947f65a9a7f0d8cab94f6dc27d62a9380a9317f9c0. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000

SAGE: Automata (Show measurement, test conditions, and source)

Source value: 30.159
Score: 30.159
Metric: accuracy
Unit: %
Benchmark version: 1
Category: education
Task: automata
Task label: Automata
Direction: Higher is better
Comparison cohort: vals-sage:1:automata
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-08. Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000. Latency in seconds: 54.582. Standard error: 5.701. Cost per task in USD: 0.066288. Source ID: vals-ai. Evidence capture ID: vals-sage-2026-10-15-cc5fb3fecdb0. Source payload hash: cc5fb3fecdb0382d492ff167a42ad84b9c5c3883373c78ed87baaa972b8b6f7c. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000

SAGE: Calculus (Show measurement, test conditions, and source)

Source value: 2.778
Score: 2.778
Metric: accuracy
Unit: %
Benchmark version: 1
Category: education
Task: calculus
Task label: Calculus
Direction: Higher is better
Comparison cohort: vals-sage:1:calculus
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-08. Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000. Latency in seconds: 73.75. Standard error: 2.66. Cost per task in USD: 0.073682. Source ID: vals-ai. Evidence capture ID: vals-sage-2026-10-15-cc5fb3fecdb0. Source payload hash: cc5fb3fecdb0382d492ff167a42ad84b9c5c3883373c78ed87baaa972b8b6f7c. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000

SAGE: Linear Algebra (Show measurement, test conditions, and source)

Source value: 41.411
Score: 41.411
Metric: accuracy
Unit: %
Benchmark version: 1
Category: education
Task: linear_algebra
Task label: Linear Algebra
Direction: Higher is better
Comparison cohort: vals-sage:1:linear_algebra
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-08. Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000. Latency in seconds: 46.749. Standard error: 5.535. Cost per task in USD: 0.072413. Source ID: vals-ai. Evidence capture ID: vals-sage-2026-10-15-cc5fb3fecdb0. Source payload hash: cc5fb3fecdb0382d492ff167a42ad84b9c5c3883373c78ed87baaa972b8b6f7c. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000

SAGE: Overall (Show measurement, test conditions, and source)

Source value: 37.613
Score: 37.613
Metric: accuracy
Unit: %
Benchmark version: 1
Category: education
Task: overall
Task label: Overall
Direction: Higher is better
Comparison cohort: vals-sage:1:overall
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-08. Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000. Latency in seconds: 55.844. Standard error: 3.356. Cost per task in USD: 0.072775. Source ID: vals-ai. Evidence capture ID: vals-sage-2026-10-15-cc5fb3fecdb0. Source payload hash: cc5fb3fecdb0382d492ff167a42ad84b9c5c3883373c78ed87baaa972b8b6f7c. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000

SAGE: Partial Differential Equations (Show measurement, test conditions, and source)

Source value: 41.27
Score: 41.27
Metric: accuracy
Unit: %
Benchmark version: 1
Category: education
Task: PDEs
Task label: Partial Differential Equations
Direction: Higher is better
Comparison cohort: vals-sage:1:PDEs
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Provider reported by source: Mistral AI. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-08. Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000. Latency in seconds: 66.2. Standard error: 6.185. Cost per task in USD: 0.079624. Source ID: vals-ai. Evidence capture ID: vals-sage-2026-10-15-cc5fb3fecdb0. Source payload hash: cc5fb3fecdb0382d492ff167a42ad84b9c5c3883373c78ed87baaa972b8b6f7c. Parser version: vals-astro-v3. Imported at: 2026-10-15
Comparison methodology: Reasoning effort: high. Temperature: 1. Top P: 0.95. Maximum output tokens: 30000

Vals Multimodal Index: SWE-bench (Show measurement, test conditions, and source)

Source value: 64.706
Score: 64.706
Metric: benchmark score
Unit: %
Benchmark version: 1.2
Category: aggregate
Task: swebench
Task label: SWE-bench
Direction: Higher is better
Comparison cohort: vals-vals-multimodal-index:1.2:swebench
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-10-15
Methodology: Source model ID: mistralai/mistral-medium-3.5. Source model name: Mistral Medium 3.5. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-11. Latency in seconds: 854.891. Standard error: 4.732. Cost per task in USD: 4.288697. Source ID: vals-ai. Evidence capture ID: vals-vals_multimodal_index-2026-10-15-077b22575f6e. Source payload hash: 077b22575f6e075b7b78157d000ec8f94d7ead409c7dcbcf04887630ed572a2d. Parser version: vals-astro-v3. Imported at: 2026-10-15

Page 1 of 19

Predecessor and successor

Sources and data date

Type	Evidence and data date
Speed	Mistral, Reasoning. Output speed after the first streaming chunk, not total response time. Retrieved September 7, 2026. Source: Artificial Analysis · Speed (Artificial Analysis)Speed measurement methodology
Price	Base API prices without caching or batch discounts. Higher context tiers and other rates are listed under Costs. Source: Pricing · API pricing
Input	Documented modalities. Retrieved September 7, 2026. Source: Model documentation · Model metadata (retrieved September 8, 2026) · Mistral Medium 3.5 model documentation · Editorial description reviewed October 2, 2026
Output	Documented modalities. Retrieved September 7, 2026. Source: Model documentation · Model metadata (retrieved September 8, 2026) · Mistral Medium 3.5 model documentation · Editorial description reviewed October 2, 2026
Research status	Research date September 8, 2026. 2 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Unresolved	canonical-id-alias Original wording Inventory ID mistral-medium-3 maps to display/API Medium 3.5; preserve both identities in downstream data.
Not found in the inspected sources	knowledge-cutoff Original wording No exact cutoff in checked docs/card.
Not found in the inspected sources	training-tokens Original wording No token count found.
Not found in the inspected sources	max-output-tokens Original wording No model-specific maximum output found.
Additional source	Context window data
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Artificial Analysis (retrieved October 19, 2026)
Additional source	Taste-Bench paper (retrieved September 26, 2026)
Additional source	LMArena (retrieved October 15, 2026)
Additional source	LMArena (retrieved October 15, 2026)
Additional source	LMArena (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Vals AI (retrieved October 15, 2026)
Additional source	Mistral Medium 3.5 model card (retrieved September 8, 2026) · Mistral Medium 3.5 model card · Editorial description reviewed October 2, 2026
Additional source	Mistral Medium 3.5 announcement · Editorial description reviewed October 2, 2026
Additional source	Mistral model overview · Editorial description reviewed October 2, 2026
Additional source	OpenRouter: Popularity index (derived), CC BY 4.0
Additional source	Vercel AI Gateway: Popularity index (derived), CC BY 4.0
Additional source	Ollama: cumulative downloads for the popularity index
Additional source	Replicate: cumulative runs for the popularity index
Additional source	Google Trending Now: limited feed selection for Germany and the US
Additional source	Creative Commons Attribution 4.0