Mercury 2.5: Specifications & Sources

Mercury 2.5 benchmarks

Overall index (Gradually Meta-Score v4)

20 of 91 models

5 models in this view are measured at a lower reasoning effort than max in the overall index. Dashed column: lower reasoning effort than the top.

Claude Opus 5.5max126.1

Gemini 4 Carbonhigh125.7

Claude Fable 5.1max123.8

Claude Sonnet 5.5max122.8

Gemini 4 Argonhigh122.3

GPT-6 Astramax121.3

GPT-6.1 Solmax119.2

Muse Wonder Fullmax118.3

Claude Opus 5max117.2

Muse Wondermax113.4

Kimi K3.1 Promax112.9

Claude Fable 5max112.5

GPT-6 Solmax110.6

GPT-5.6 Solmax108.7

Muse Spark 1.3max107.4

Gemini 3.7 Flashhigh107.1

DeepSeek V4.1 Flash 1017max106.6

Gemini 3.8 Flashhigh106.5

GPT-6.1 Terramax105.2

Mercury 2.5high44.7

DeepSeek V4.1 Flash 1017

Gemini 3.8 Flash

GPT-6.1 Terra

Mercury 2.5

Speed

20 of 100 models

Mercury 2.5576tokens/s

Gemini 3.1 Flash-Lite336tokens/s

Gemini 3.5 Flash-Lite299tokens/s

Nemotron 3.5 Lightning245tokens/s

Claude Haiku 5.5236tokens/s

o3-mini230tokens/s

Ling 3.0 Flash199tokens/s

DeepSeek-V3 0324195tokens/s

GPT-5.4 mini191tokens/s

Gemini 3 Flash Preview191tokens/s

Gemini 3.5 Flash184tokens/s

Gemini 3.6 Flash182tokens/s

Command A Plus177tokens/s

DeepSeek-V4.1-Flash176tokens/s

DeepSeek V4.1 Flash 1017172tokens/s

Qwen 3.7 Max165tokens/s

GPT-5.4 nano153tokens/s

GPT OSS 20B152tokens/s

GPT OSS 120B151tokens/s

Claude Sonnet 5.5151tokens/s

Mercury 2.5

Gemini 3.1 Flash-Lite

Gemini 3.5 Flash-Lite

Nemotron 3.5 Lightning

Gemini 3 Flash Preview

DeepSeek V4.1 Flash 1017

Output tokens per second, measured by Artificial Analysis

Mercury 2.5 by task area

Points, 100 = reference group average · higher is better

CodingRank 88 of 9057.3

Mathematicstoo little data52.2

LegalRank 70 of 6650.1

HealthRank 69 of 6843.4

AgentsRank 89 of 8340.8

FinanceRank 97 of 9633.4

Light bar: too few results for a rank yet. No tests yet: Reasoning, Knowledge, General, Multimodal.

Show all 243 published benchmark results

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.

CritPt v2: Overall (Show measurement, test conditions, and source)

Source value: 0
Score: 0
Metric: pass@1 accuracy
Unit: %
Benchmark version: v2
Category: reasoning
Task: overall
Task label: Overall
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: disputed
Retrieved at: 2026-11-02
Methodology: Exact AA configuration: Mercury 2.5 (release mercury-2-5, row mercury-2-5, UUID 765c9f31-95ec-4f14-900a-80614c3b0777, reasoning=true, effort=not specified, suffix=not specified). AA source value for average reasoning tokens before the answer: 0. Its methodology averages 60 varied prompts and assumes 2,000 when no average is available. This is not a token cap. The full CritPt v2 run covers 70 questions with five repeats. The official grading server reports pass@1.. Source model ID: artificial-analysis/765c9f31-95ec-4f14-900a-80614c3b0777/mercury-2-5/mercury-2-5. Source model name: Mercury 2.5. Provider reported by source: Inception. Model snapshot: Mercury 2.5. Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Python functions, symbolic expressions, and numerical answers; official grading server. Attempts: 70 questions, five repeats per question, pass@1. Reasoning: reasoning enabled. Score display: 0.0%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-11-02-67d46be20a5b. Source payload hash: 67d46be20a5b412db6cfb60ad190441fcaa5513500b8fd47d033168d0e97b6b2. Parser version: artificial-analysis-rsc-models-v3. Imported at: 2026-11-02. Source cohort: artificial-analysis-critpt-v2:v2:standalone:reasoning:effort=unspecified:suffix=unspecified
Comparison methodology: Exact AA configuration: Mercury 2.5 (release mercury-2-5, row mercury-2-5, UUID 765c9f31-95ec-4f14-900a-80614c3b0777, reasoning=true, effort=not specified, suffix=not specified). AA source value for average reasoning tokens before the answer: 0. Its methodology averages 60 varied prompts and assumes 2,000 when no average is available. This is not a token cap. The full CritPt v2 run covers 70 questions with five repeats. The official grading server reports pass@1.. Tool policy: Python functions, symbolic expressions, and numerical answers; official grading server. Attempts: 70 questions, five repeats per question, pass@1. Reasoning: reasoning enabled
Context: This AA source field contains the original CritPt score. The earlier CritPt v2 attribution was incorrect; the value and former label remain as disputed historical evidence.. The source dataset marks this result as disputed. It remains visible as documented evidence, but is not reliable comparative proof.

Source Artificial Analysis

Code Migration: CLI (Show measurement, test conditions, and source)

Source value: 6.023
Score: 6.023
Metric: accuracy
Unit: %
Benchmark version: 1
Category: coding
Task: cli
Task label: CLI
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-11-06
Methodology: Source model ID: inception/mercury-2.5. Source model name: Mercury 2.5. Provider reported by source: Inception. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-29. Reasoning effort: high. Maximum output tokens: 65536. Latency in seconds: 760.46. Standard error: 1.398. Cost per task in USD: 0.522043. Source ID: vals-ai. Evidence capture ID: vals-code-migration-2026-11-06-29583d3e44f7. Source payload hash: 29583d3e44f7f84f9585638967cbc19a412a65265d07cbe16ce4bc09b685241c. Parser version: vals-astro-v4. Imported at: 2026-11-06. Source cohort: vals-code-migration:1:cli
Comparison methodology: Reasoning effort: high. Maximum output tokens: 65536

Source Vals AI

Code Migration: COBOL (Show measurement, test conditions, and source)

Source value: 0
Score: 0
Metric: accuracy
Unit: %
Benchmark version: 1
Category: coding
Task: cobol
Task label: COBOL
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-11-06
Methodology: Source model ID: inception/mercury-2.5. Source model name: Mercury 2.5. Provider reported by source: Inception. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-29. Reasoning effort: high. Maximum output tokens: 65536. Latency in seconds: 1614.952. Standard error: 0. Cost per task in USD: 0.94276. Source ID: vals-ai. Evidence capture ID: vals-code-migration-2026-11-06-29583d3e44f7. Source payload hash: 29583d3e44f7f84f9585638967cbc19a412a65265d07cbe16ce4bc09b685241c. Parser version: vals-astro-v4. Imported at: 2026-11-06. Source cohort: vals-code-migration:1:cobol
Comparison methodology: Reasoning effort: high. Maximum output tokens: 65536

Source Vals AI

Code Migration: Overall (Show measurement, test conditions, and source)

Source value: 4.517
Score: 4.517
Metric: accuracy
Unit: %
Benchmark version: 1
Category: coding
Task: overall
Task label: Overall
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-11-06
Methodology: Source model ID: inception/mercury-2.5. Source model name: Mercury 2.5. Provider reported by source: Inception. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-29. Reasoning effort: high. Maximum output tokens: 65536. Latency in seconds: 974.083. Standard error: 1.048. Cost per task in USD: 0.627223. Source ID: vals-ai. Evidence capture ID: vals-code-migration-2026-11-06-29583d3e44f7. Source payload hash: 29583d3e44f7f84f9585638967cbc19a412a65265d07cbe16ce4bc09b685241c. Parser version: vals-astro-v4. Imported at: 2026-11-06. Source cohort: vals-code-migration:1:overall
Comparison methodology: Reasoning effort: high. Maximum output tokens: 65536

Source Vals AI

CyberBench v1.1: Overall (Show measurement, test conditions, and source)

Source value: 45.476
Score: 45.476
Metric: accuracy
Unit: %
Benchmark version: 1.1
Category: cybersecurity
Task: overall
Task label: Overall
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-11-06
Methodology: Source model ID: inception/mercury-2.5. Source model name: Mercury 2.5. Provider reported by source: Inception. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-11-05. Reasoning effort: high. Maximum output tokens: 65536. Latency in seconds: 1139.168. Standard error: 6.056. Cost per task in USD: 0.12462. Source ID: vals-ai. Evidence capture ID: vals-cyber-2026-11-06-685f9c5e30d1. Source payload hash: 685f9c5e30d12de611455c14f9bff493f7ef804638b58412afbef7bd075190f3. Parser version: vals-astro-v4. Imported at: 2026-11-06. Source cohort: vals-cyberbench:1.1:overall
Comparison methodology: Reasoning effort: high. Maximum output tokens: 65536

Source Vals AI

CyberBench v1.1: Patch (Show measurement, test conditions, and source)

Source value: 64.286
Score: 64.286
Metric: accuracy
Unit: %
Benchmark version: 1.1
Category: cybersecurity
Task: patch
Task label: Patch
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-11-06
Methodology: Source model ID: inception/mercury-2.5. Source model name: Mercury 2.5. Provider reported by source: Inception. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-11-05. Reasoning effort: high. Maximum output tokens: 65536. Latency in seconds: 661.559. Standard error: 6.403. Cost per task in USD: 0.126262. Source ID: vals-ai. Evidence capture ID: vals-cyber-2026-11-06-685f9c5e30d1. Source payload hash: 685f9c5e30d12de611455c14f9bff493f7ef804638b58412afbef7bd075190f3. Parser version: vals-astro-v4. Imported at: 2026-11-06. Source cohort: vals-cyberbench:1.1:patch
Comparison methodology: Reasoning effort: high. Maximum output tokens: 65536

Source Vals AI

CyberBench v1.1: PoC (Show measurement, test conditions, and source)

Source value: 26.667
Score: 26.667
Metric: accuracy
Unit: %
Benchmark version: 1.1
Category: cybersecurity
Task: poc
Task label: PoC
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-11-06
Methodology: Source model ID: inception/mercury-2.5. Source model name: Mercury 2.5. Provider reported by source: Inception. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-11-05. Reasoning effort: high. Maximum output tokens: 65536. Latency in seconds: 1616.777. Standard error: 5.709. Cost per task in USD: 0.122978. Source ID: vals-ai. Evidence capture ID: vals-cyber-2026-11-06-685f9c5e30d1. Source payload hash: 685f9c5e30d12de611455c14f9bff493f7ef804638b58412afbef7bd075190f3. Parser version: vals-astro-v4. Imported at: 2026-11-06. Source cohort: vals-cyberbench:1.1:poc
Comparison methodology: Reasoning effort: high. Maximum output tokens: 65536

Source Vals AI

EMB: Comps (Show measurement, test conditions, and source)

Source value: 11.518
Score: 11.518
Metric: accuracy
Unit: %
Benchmark version: 1
Category: finance
Task: comps
Task label: Comps
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-11-06
Methodology: Source model ID: inception/mercury-2.5. Source model name: Mercury 2.5. Provider reported by source: Inception. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-20. Reasoning effort: high. Maximum output tokens: 65536. Latency in seconds: 317.21. Standard error: 4.015. Cost per task in USD: 0.481209. Source ID: vals-ai. Evidence capture ID: vals-emb-2026-11-06-1ccb1c0be00a. Source payload hash: 1ccb1c0be00a3ce7d6fbe1e420de1a64bad253458d78ca09d7d6209ba59fd571. Parser version: vals-astro-v4. Imported at: 2026-11-06. Source cohort: vals-emb:1:comps
Comparison methodology: Reasoning effort: high. Maximum output tokens: 65536

Source Vals AI

EMB: Dataroom (Show measurement, test conditions, and source)

Source value: 15.513
Score: 15.513
Metric: accuracy
Unit: %
Benchmark version: 1
Category: finance
Task: dataroom
Task label: Dataroom
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-11-06
Methodology: Source model ID: inception/mercury-2.5. Source model name: Mercury 2.5. Provider reported by source: Inception. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-20. Reasoning effort: high. Maximum output tokens: 65536. Latency in seconds: 362.75. Standard error: 5.699. Cost per task in USD: 0.379957. Source ID: vals-ai. Evidence capture ID: vals-emb-2026-11-06-1ccb1c0be00a. Source payload hash: 1ccb1c0be00a3ce7d6fbe1e420de1a64bad253458d78ca09d7d6209ba59fd571. Parser version: vals-astro-v4. Imported at: 2026-11-06. Source cohort: vals-emb:1:dataroom
Comparison methodology: Reasoning effort: high. Maximum output tokens: 65536

Source Vals AI

EMB: DCF (Show measurement, test conditions, and source)

Source value: 4.721
Score: 4.721
Metric: accuracy
Unit: %
Benchmark version: 1
Category: finance
Task: dcf
Task label: DCF
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-11-06
Methodology: Source model ID: inception/mercury-2.5. Source model name: Mercury 2.5. Provider reported by source: Inception. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-20. Reasoning effort: high. Maximum output tokens: 65536. Latency in seconds: 280.224. Standard error: 1.918. Cost per task in USD: 0.411431. Source ID: vals-ai. Evidence capture ID: vals-emb-2026-11-06-1ccb1c0be00a. Source payload hash: 1ccb1c0be00a3ce7d6fbe1e420de1a64bad253458d78ca09d7d6209ba59fd571. Parser version: vals-astro-v4. Imported at: 2026-11-06. Source cohort: vals-emb:1:dcf
Comparison methodology: Reasoning effort: high. Maximum output tokens: 65536

Source Vals AI

EMB: LBO (Show measurement, test conditions, and source)

Source value: 8.25
Score: 8.25
Metric: accuracy
Unit: %
Benchmark version: 1
Category: finance
Task: lbo
Task label: LBO
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-11-06
Methodology: Source model ID: inception/mercury-2.5. Source model name: Mercury 2.5. Provider reported by source: Inception. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-20. Reasoning effort: high. Maximum output tokens: 65536. Latency in seconds: 259.925. Standard error: 2.824. Cost per task in USD: 0.375911. Source ID: vals-ai. Evidence capture ID: vals-emb-2026-11-06-1ccb1c0be00a. Source payload hash: 1ccb1c0be00a3ce7d6fbe1e420de1a64bad253458d78ca09d7d6209ba59fd571. Parser version: vals-astro-v4. Imported at: 2026-11-06. Source cohort: vals-emb:1:lbo
Comparison methodology: Reasoning effort: high. Maximum output tokens: 65536

Source Vals AI

EMB: M&A (Show measurement, test conditions, and source)

Source value: 6.35
Score: 6.35
Metric: accuracy
Unit: %
Benchmark version: 1
Category: finance
Task: ma
Task label: M&A
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Vals AI
Status: active
Retrieved at: 2026-11-06
Methodology: Source model ID: inception/mercury-2.5. Source model name: Mercury 2.5. Provider reported by source: Inception. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-10-20. Reasoning effort: high. Maximum output tokens: 65536. Latency in seconds: 395.701. Standard error: 2.25. Cost per task in USD: 0.572506. Source ID: vals-ai. Evidence capture ID: vals-emb-2026-11-06-1ccb1c0be00a. Source payload hash: 1ccb1c0be00a3ce7d6fbe1e420de1a64bad253458d78ca09d7d6209ba59fd571. Parser version: vals-astro-v4. Imported at: 2026-11-06. Source cohort: vals-emb:1:ma
Comparison methodology: Reasoning effort: high. Maximum output tokens: 65536

Source Vals AI

Page 1 of 21

See for yourself: one prompt, one attempt

We show the latest attempt without fixes; it does not count toward the overall index.

Pelican on a bicycle

Prompt

Generate an SVG of a pelican riding a bicycle

Oct 5, 2026 · 5.2 s · $0.0002

Test details

Run date: Oct 5, 2026
API provider: openrouter
Requested API model: inception/mercury-2.5
Reasoning setting: high
Token limit including reasoning: 8,192 tokens
Output tokens: 1,543 tokens
Cost of the attempt: $0.0002
Response time: 5.2 s
API model reported in response: inception/mercury-2.5
Pinned endpoint: Inception | inception/mercury-2.5-20260908
Temperature: Provider default
Test protocol: community-visual-tests-v1
Technical checks: Passed, not a quality rating

Visually reviewed. This individual test does not establish overall model quality.

Task origin: Simon Willison

SVG short test

One SVG task with the same fixed settings for every model, without hidden thinking.

Mercury 2.5

Unavailable in this round

Popularity compared

How often the model is searched for, used and mentioned, compared with every model of its category.

Popularity index

The 100 most popular models of this category, 0 to 100, higher is more popular

20 of 101 models

GPT-573

DeepSeek V4.1 Flash 101768

Kimi K368

DiffusionGemma 26B A4B IT63

Mercury 2.50

DeepSeek V4.1 Flash 1017

DiffusionGemma 26B A4B IT

Claude Opus 5

Qwen 3.8 Flash

Mercury 2.5

As of November 2026 · Source: gradually.ai popularity index

Over time

Weekly values from October 18, 2026 to November 5, 2026

Mercury 2.5Agnes 3.0 FlashAion 3.5Aion 3.5 MiniEmber-1

All specifications

Architecture

Model class: Diffusion language model; Source: Model class

Input and output

Context window: 260K tokens according to Inception; Source: Context window
Speed: 1,107 tokens per second according to the provider; Source: Speed
Capabilities: Reasoning, parallel tool calls, and schema-aligned JSON; Source: Capabilities

Watermarking and provenance

AI watermarking refers to invisible signals embedded in the output. Visible marks and provenance metadata are listed separately. Unknown means there is insufficient evidence for that particular field.

AI watermarking: Unknown; no source
Watermarking technique: Not known; no source

Licence and access

Access: Inception API, Baseten, and OpenRouter; Source: Access

Prices

Not included in any subscription in Germany.

All plans in the AI subscription comparison

Frequently asked questions about Mercury 2.5

How good is Mercury 2.5 in benchmarks?

Weak: Rank 88 of 91 in the overall index.

Overall index (Gradually Meta-Score v4) from 17 benchmark results · data through Nov 6, 2026

IntelligenceOverall index in points, 100 = reference group average · higher is better

DeepSeek-R148.2

GPT-4.147.7

Mistral Medium 3.546.8

Mercury 2.5Rank 8844.7

DeepSeek-V3 032440.8

GPT-4.1 mini39.5

Mistral Large 338.9

What is the Overall index (Gradually Meta-Score v4)?

A point value that combines many independent benchmarks into one number. 100 points is the average of a fixed reference group, higher is better. Mercury 2.5 scores 44.7 points, which is rank 88 of 91.

Own analysis by gradually.ai, method benchmark-meta-score-v4 · for Mercury 2.5 from 17 test series

Which data: Only current, reviewed results from independent evaluators such as Artificial Analysis, Vals AI, LMArena, LiveBench and Epoch AI. Vendor claims and historical or disputed values do not count.
One value per test: For each source and test series the latest retrieval with one fixed setup counts. When a source tests several reasoning levels, the highest documented one applies. A series needs at least 5 models.
Making tests comparable: Percentages are converted so that gaps near 0 and near 100 % count fairly, then every series is put on the same scale. A hard test therefore does not weigh more than an easy one.
Shared strength: A statistical model estimates, from all series at once, one shared capability per model and, per area, how far a model deviates from it. Missing tests are never counted as zero.
Weighting: Core areas (Coding, Reasoning, Mathematics, Knowledge, Agents, General and Multimodal) count 75 % together, the domain areas Finance, Legal and Health 25 %.
The point scale: The value is measured against a fixed reference group of 29 models, much like an IQ score: 100 points is its average and 15 points one standard deviation of the group. The scale is open at both ends and is not a percentage. A model 15 points ahead does better in roughly three of four benchmark comparisons. New models do not shift older values.
Place by score: The place follows the order of the scores among all models with a place, such as "rank 14 of 95". A second calculation checks whether a lead is reliable by recomputing the score 200 times with randomly redrawn test families. If a model is not almost always ahead there, the two models count as level.
Minimum evidence: A value needs at least 8 test series from 2 sources and 3 areas, a place only with enough precision. Missing core areas are flagged as "estimated".
What it is not: Not a test run by gradually.ai and no verdict on every single task. For a specific task the area values and the individual benchmarks matter more.

How fast is Mercury 2.5?

576 tokens per second. That is faster than a typical model at 87.

Measured by Artificial Analysis on Nov 6, 2026. AA 3.0.0, including time to first token, averaged across available workloads and five days.

SpeedTokens per second · higher is better

Mercury 2.5Rank 1 of 100576

DeepSeek-V3 0324195

Mistral Medium 3.5144

GPT-4.1119

GPT-4.1 mini85

Mistral Large 378

GLM-4.632

Can I run Mercury 2.5 locally?

No. Inception does not publish the weights.

Stated by Inception

Open weights: no
License: n/a
Parameters: n/a
active per token: n/a

Sources and data date

Show all 44 sources

Benchmark sources at a glance

Which independent evaluators tested this model. This is coverage, not a quality ranking.

Evaluator	Test series	Composite indices	Recorded active measurements
Artificial Analysis	1	1	2
Vals AI	17	0	142

Without vendor claims and historical or disputed results; those stay labelled in the single values.

Type	Evidence and data date
Speed	Inception, Reasoning enabled; effort unspecified. AA 3.0.0, including time to first token, averaged across available workloads and five days. Retrieved November 6, 2026. Source: Artificial Analysis · Artificial Analysis, mercury 2 5 model page (retrieved October 17, 2026)Speed measurement methodology
Research status	Research date October 4, 2026. 13 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Not published	sample-count-and-confidence-interval Original wording AA publishes neither a per-profile total executed N nor a confidence interval for this aggregate index. Methodology component task counts are not this model's measured N.
Unresolved	serving-revision-and-reasoning-effort Original wording The exact Mercury 2.5 page does not disclose a model weights source, immutable serving revision, or model-side reasoning effort.
Not published	score scale, denominator, serving revision and confidence interval Note The comparison chart supplies scores but no task-level denominator or 95% interval. It does not tie the named product to an immutable serving revision.
Additional source	Inception Mercury 2.5 announcement (retrieved October 3, 2026; October 4, 2026; October 17, 2026; October 19, 2026) · Inception Mercury 2.5 announcement · Editorial description reviewed October 4, 2026
Additional source	Inception model blog (retrieved October 3, 2026) · Inception model blog · Editorial description reviewed October 4, 2026
Additional source	Artificial Analysis (retrieved November 2, 2026)
Additional source	Artificial Analysis (retrieved October 29, 2026; November 2, 2026; November 6, 2026)
Additional source	Vals AI (retrieved November 6, 2026)1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Additional source	Vals AI (retrieved October 16, 2026)1 2
Additional source	Vals AI (retrieved October 16, 2026; October 31, 2026; November 6, 2026)
Additional source	Vals AI (retrieved October 16, 2026; October 30, 2026; October 31, 2026)
Additional source	MathBench AI, Mercury 2.5 results (retrieved October 17, 2026; October 28, 2026)
Additional source	Jevals, Mercury 2.5 benchmark (retrieved October 17, 2026)
Additional source	Epoch AI (retrieved October 20, 2026)
Additional source	Model metadata (retrieved October 4, 2026)1 2
Additional source	Model metadata (retrieved October 17, 2026)1 2 3 4 5
Additional source	Model metadata (retrieved October 17, 2026; October 28, 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
Additional source	Inception Mercury 3 launch (2026-10-24)