Mistral Large 3
API request failed. The model was not evaluated.
Mistral AI
New in Mistral Large 3 is Mistral’s first mixture-of-experts checkpoint since the Mixtral line. Mistral describes 675 billion total parameters and 41 billion active parameters with a 256,000-token context window. Text and images are inputs, and the weights use Apache 2.0.
Large 3 is released as an open checkpoint. Ministral 3 14B belongs to the same multimodal Mistral 3 release alongside smaller variants.
| Specification | Value and source |
|---|---|
| Weights | Open weightsSource |
| Access | API, LocalSource |
| Parameters | 675BSource |
| Active parameters | 41BSource |
| Context window | 262,144 tokensSource |
| Notes | Sparse MoE with 675B total, 41B activeSource |
| License | Apache-2.0Source |
| Release | 2025-12-02Source |
| Parameters | 675 BSource |
| Active parameters | 41 B (HF card header describes 673B language-model parameters and 39B active plus vision encoder; source descriptions differ.)Source |
| Architecture | sparse mixture-of-experts language model with vision encoderSource |
| Input | Text, ImageSource |
| Output | TextSource |
| Training hardware | 3000 H200 GPUsSource |
| Tool calling | YesSource |
Benchmark meta-analysis
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
Not enough coverage for a score yet
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Not enough coverage for a score yet
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Not enough coverage for a score yet
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Not enough coverage for a score yet
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index 22.0 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Not enough coverage for a score yet
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Not enough coverage for a score yet
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Not enough coverage for a score yet
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Not enough coverage for a score yet
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Not enough coverage for a score yet
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Index 5.8 / 100
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
Not enough coverage for a score yet
provisional
Published configurations differ within this comparison cohort. Treat the index as provisional.
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 indicatorsAll 24 test families and 12 of 26 raw measurements are listed here. The complete calculation for every model is available in the JSON export.
Nominal family weight in the overall panel: 0.6 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 0.6 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 0.6 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 1.43 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 2.38 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 50 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 50 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 0.48 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 1.43 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 1.79 % · Coverage of this test family: 100 %
Published configurations differ within this cohort.
Nominal family weight in the overall panel: 1.79 % · Coverage of this test family: 100 %
Nominal family weight in the overall panel: 0.48 % · Coverage of this test family: 50 %
Nominal family weight in the overall panel: 0.48 % · Coverage of this test family: 67 %
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
Nominal family weight in the overall panel: 0.48 % · Coverage of this test family: 100 %
Nominal family weight in the overall panel: 1.79 % · Coverage of this test family: 100 %
Nominal family weight in the overall panel: 2.38 % · Coverage of this test family: 100 %
Nominal family weight in the overall panel: 1.43 % · Coverage of this test family: 100 %
Nominal family weight in the overall panel: 2.38 % · Coverage of this test family: 100 %
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
Nominal family weight in the overall panel: 3.57 % · Coverage of this test family: 100 %
Nominal family weight in the overall panel: 1.02 % · Coverage of this test family: 100 %
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 | 10 | 1 | 13 |
| Vals AI | 15 | 0 | 76 |
| LMArena | 3 | 0 | 41 |
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.
Page 1 of 11
| Sub-task | Mistral Large 3 (This model) | Gemini 2.5 Pro | Qwen 3.8 Max 0902 | Gemini 2.5 Flash-Lite | Qwen 3.7 Plus | Claude Sonnet 4.5 | Llama 4 Scout |
|---|---|---|---|---|---|---|---|
| Automata | 11.91% | 43.65% (best value in this row) | 39.68% | 33.33% | 32.54% | 20.24% | 14.68% |
| Linear Algebra | 44.42% | 51.94% | 69.64% (best value in this row) | 28.63% | 51.88% | 51.42% | 61.25% |
| Overall | 24.6% | 41.92% | 51.25% (best value in this row) | 27.53% | 39.25% | 32.88% | 34.83% |
| Partial Differential Equations | 17.46% | 30.16% | 44.44% (best value in this row) | 20.64% | 33.33% | 26.98% | 28.57% |
higher is betterAs of October 15, 2026Source: Vals AIbars scaled within each row, not across rows
Calculus
higher is betterAs of October 15, 2026Scale: 0% - 100%
These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.
Page 1 of 11
One SVG prompt and fixed output settings. Captures are shown only with 0 reported reasoning tokens or documented lack of reasoning controls. Identical test conditions do not make the models' internal compute identical.
API request failed. The model was not evaluated.
A community classic for free-form SVG drawing.
Mistral Large 3

Reasoning (model default): no reasoning mode Source
Visually reviewed. This individual test does not establish overall model quality.
Generate an SVG of a pelican riding a bicycle
Token limit including reasoning: 8,192 tokens
Task origin (Simon Willison)32 / 100
| Type | Evidence and data date |
|---|---|
| Speed | Mistral. Output speed after the first streaming chunk, not total response time. Retrieved September 7, 2026. |
| Price | Base API prices without caching or batch discounts. Higher context tiers and other rates are listed under Costs. |
| Input | Documented modalities. Retrieved September 7, 2026. |
| Output | Documented modalities. Retrieved September 7, 2026. |
| Research status | Research date September 8, 2026. 3 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication. |
| Unresolved | profile-benchmarks Original wording The official Instruct card publishes no exact score table. The separate Base card has a labeled benchmark image, but those Base results cannot be imported into this Instruct record; no exact Instruct score source was found. |
| Not found in the inspected sources | knowledge-cutoff Original wording No exact cutoff found. |
| 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. |
| Unresolved | parameter-discrepancy Original wording Release post says 675B/41B active; HF card header says 673B/39B language model plus vision encoder. Do not collapse these into one unqualified figure. |
| Additional source | Model metadata |
| Additional source | Context window data (retrieved September 8, 2026) · Mistral Large 3 model card · Editorial description reviewed October 2, 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 | 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 | Model metadata (retrieved September 8, 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 |