Gemini 2.5 Pro vs Mistral Large 2407
Gemini 2.5 Pro leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 16.1 to 7.6, and Mistral Large 2407 costs 1.7x less per output token ($6.00 vs $10.00 per million). Updated October 21, 2026; 14 benchmarks have a result for both.
Gemini 2.5 Pro or Mistral Large 2407: which to pick
Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; Mistral Large 2407 has none, so the two cannot be ranked on coding.
Pick Mistral Large 2407 for output tokens 1.7x cheaper ($6.00 vs $10.00 per million).
- Shared results
- 14
- Gemini 2.5 Pro only
- 73
- Mistral Large 2407 only
- 3
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Gemini 2.5 Pro has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Gemini 2.5 Pro
Gemini 2.5 Pro leads, 16.1 against 7.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemini 2.5 Pro has a AA Agentic Index result, 1.6.
Directional only
Web design
Design Arena: websites
No call
Only Gemini 2.5 Pro has a Design Arena: websites result, 1167.
Directional only
Long documents
Listed context window
Gemini 2.5 Pro
Gemini 2.5 Pro takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 2407
Gemini 2.5 Pro $0.0063, Mistral Large 2407 $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 2.5 Pro has the number, $0.64.
Directional only
At a glance
- Coding index
- Gemini 2.5 Pro 19.1 from 3 benchmarks, Mistral Large 2407 not benchmarked.
- Widest category
- Math, 83.9 points apart on average over 3 shared benchmarks.
- Widest single gap
- AA Math Index: Gemini 2.5 Pro 87.7 against Mistral Large 2407 0.0, 87.7 points apart on a 0 to 100 scale.
- Output price
- Gemini 2.5 Pro $10.00 against Mistral Large 2407 $6.00 per million output tokens, 1.7x apart.
- Context window
- Gemini 2.5 Pro takes 1.05M tokens, Mistral Large 2407 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Gemini 2.5 Pro | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 4 | 0 | ||
| All shared boards | 14 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Gemini 2.5 Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| SWE-bench Verified | 53.6% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena WebDev | 1226 | Not measured | Only Gemini 2.5 Pro measured |
| AA Coding Index | 33.3 | Not measured | Only Gemini 2.5 Pro measured |
| SWE-bench Verified (Vals AI) | 54.4% | Not measured | Only Gemini 2.5 Pro measured |
| Vibe Code Bench (Vals AI) | 0.4% | Not measured | Only Gemini 2.5 Pro measured |
| LiveCodeBench (Artificial Analysis run) | 80.1% | 26.7% | Gemini 2.5 Pro |
| SciCode | 46.3% | Not measured | Only Gemini 2.5 Pro measured |
| Terminal-Bench Hard (Artificial Analysis run) | 26.5% | Not measured | Only Gemini 2.5 Pro measured |
| Terminal-Bench | 32.6% | Not measured | Only Gemini 2.5 Pro measured |
| GSO-Bench | 3.9% | Not measured | Only Gemini 2.5 Pro measured |
| WeirdML | 54.0% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: coding | 1452 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: software and IT | 1459 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena WebDev: HTML | 1235 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena WebDev: from an image | 1285 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 2.5 Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.1 | 7.6 | Gemini 2.5 Pro |
| Epoch Capabilities Index | 145.1 | Not measured | Only Gemini 2.5 Pro measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 2.5 Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.6 | Not measured | Only Gemini 2.5 Pro measured |
| τ²-Bench Telecom | 54.1% | 33.0% | Gemini 2.5 Pro |
| GDPval | 23.3% | Not measured | Only Gemini 2.5 Pro measured |
| DeepResearch Bench | 41.5% | Not measured | Only Gemini 2.5 Pro measured |
| Vending-Bench 2 | $574 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Gemini 2.5 Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.4% | 47.2% | Gemini 2.5 Pro |
| Humanity's Last Exam (Artificial Analysis run) | 22.5% | 2.9% | Gemini 2.5 Pro |
| GPQA Diamond (Epoch AI run) | 85.3% | 49.0% | Gemini 2.5 Pro |
| ARC-AGI | 41.0% | Not measured | Only Gemini 2.5 Pro measured |
| ARC-AGI-2 | 4.9% | Not measured | Only Gemini 2.5 Pro measured |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 measured |
| LMArena Text: hard prompts | 1455 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: expert | 1452 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: hard prompts in English | 1453 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: diagrams | 1274 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: homework | 1281 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Gemini 2.5 Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | 0.0 | Gemini 2.5 Pro |
| AIME 2025 (Artificial Analysis run) | 87.7% | 0.0% | Gemini 2.5 Pro |
| OTIS Mock AIME | 84.7% | 8.5% | Gemini 2.5 Pro |
| FrontierMath, tiers 1 to 3 | 24.6% | Not measured | Only Gemini 2.5 Pro measured |
| FrontierMath, tier 4 | 0.0% | Not measured | Only Gemini 2.5 Pro measured |
| MATH level 5 | Not measured | 44.8% | Only Mistral Large 2407 measured |
| LMArena Text: math | 1450 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: mathematical work | 1454 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 2.5 Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | 63.9% | Not measured | Only Gemini 2.5 Pro measured |
| CorpFin (Vals AI) | 60.8% | Not measured | Only Gemini 2.5 Pro measured |
| MortgageTax (Vals AI) | 68.9% | Not measured | Only Gemini 2.5 Pro measured |
| MedCode (Vals AI) | 50.6% | Not measured | Only Gemini 2.5 Pro measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | 68.3% | Gemini 2.5 Pro |
| MMLU | Not measured | 80.0% | Only Mistral Large 2407 measured |
| LMArena Text: science | 1480 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: medicine and healthcare | 1476 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: legal and government | 1480 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: business and finance | 1439 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemini 2.5 Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| Design Arena: websites | 1167 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: UI components | 1140 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: game dev | 1111 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: data viz | 1221 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: 3D | 1081 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: all code categories | 1152 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Gemini 2.5 Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 41.9% | Not measured | Only Gemini 2.5 Pro measured |
| MedScribe (Vals AI) | 73.5% | Not measured | Only Gemini 2.5 Pro measured |
| IFBench | 48.7% | 31.6% | Gemini 2.5 Pro |
| Long context reasoning (AA-LCR) | 69.0% | 2.0% | Gemini 2.5 Pro |
| DTBench | 82.4% | 61.2% | Gemini 2.5 Pro |
| LMCA | 34.8% | Not measured | Only Gemini 2.5 Pro measured |
| Creative writing (Lech Mazur) | 83.8% | 69.0% | Gemini 2.5 Pro |
| LMArena Text | 1458 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: instruction following | 1437 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: creative writing | 1454 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision | 1263 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: English | 1458 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: non-English | 1452 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Chinese | 1507 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: German | 1487 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Japanese | 1461 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Korean | 1434 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: French | 1473 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Spanish | 1474 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Russian | 1461 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Polish | 1479 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: multi-turn | 1453 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: longer queries | 1449 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: writing and language | 1454 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: entertainment and media | 1441 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: English | 1261 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: Chinese | 1300 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: reading text | 1270 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: humor | 1262 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: captioning | 1304 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: recognizing things | 1283 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: creative writing | 1265 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Document | 1421 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 2.5 Pro | Mistral Large 2407 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Mistral Large 2407 costs less
Repository review
50K input + 3K output tokens
Gemini 2.5 Pro costs less
Agent loop
200K input + 10K output tokens
Gemini 2.5 Pro costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Google Gemini 2.5 Pro | Mistral Large 2407 |
|---|---|---|
| Maker | Mistral AI | |
| Weights | No public weights listed | No public weights listed |
| Released | June 17, 2025 | November 19, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $1.25 | $2.00 |
| Output $/Mtok | $10.00 | $6.00 |
| Coding index | 19.1 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.64 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 2.5 Pro better than Mistral Large 2407 for coding?
Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; Mistral Large 2407 has none, so the two cannot be ranked on coding. Pick Mistral Large 2407 for output tokens 1.7x cheaper ($6.00 vs $10.00 per million).
Which is cheaper, Gemini 2.5 Pro or Mistral Large 2407?
Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; Mistral Large 2407 costs $2.00 and $6.00. Cost per solved task is known for Gemini 2.5 Pro only.
Which has the bigger context window, Gemini 2.5 Pro or Mistral Large 2407?
Gemini 2.5 Pro, with 1.05M tokens against 131K.
How many benchmarks is the Gemini 2.5 Pro vs Mistral Large 2407 comparison based on?
14 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Pro's coding index rests on 3 independent benchmarks and Mistral Large 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.