GPT-5.6 Sol vs Mistral Large 2407
GPT-5.6 Sol leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 47.0 to 7.6, and Mistral Large 2407 costs 3.3x less per output token ($6.00 vs $20.00 per million). Updated October 22, 2026; 10 benchmarks have a result for both.
vs boards won of 10 shared
GPT-5.6 Sol or Mistral Large 2407: which to pick
Only GPT-5.6 Sol has a coding score, 73.5 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 3.3x cheaper ($6.00 vs $20.00 per million).
Model AGPT-5.6 SolOpenAI73.5/1006 independent benchmarks$1.20 per solved taskModel BMistral Large 2407Mistral AINot benchmarkedNo coding benchmarkNo cost per solved task
- Shared results
- 10
- GPT-5.6 Sol only
- 129
- Mistral Large 2407 only
- 7
- Like-for-like categories
- 5 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 GPT-5.6 Sol has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 7.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Sol has a AA Agentic Index result, 50.2.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Sol has a GPQA Diamond result, 95.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Sol has a MMLU Pro result, 89.1%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.6 Sol has a Design Arena: websites result, 1321.
Directional only
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 2407
GPT-5.6 Sol $0.014, Mistral Large 2407 $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Sol has the number, $1.20.
Directional only
At a glance
- Coding index
- GPT-5.6 Sol 73.5 from 6 benchmarks, Mistral Large 2407 not benchmarked.
- Widest category
- Math, 91.5 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: GPT-5.6 Sol 100.0% against Mistral Large 2407 8.5%, 91.5 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol $20.00 against Mistral Large 2407 $6.00 per million output tokens, 3.3x apart.
- Context window
- GPT-5.6 Sol 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 | GPT-5.6 Sol | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 1 | 0 | ||
| General | 3 | 0 | ||
| All shared boards | 10 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index29 boards, none shared
| Benchmark | GPT-5.6 Sol | Mistral Large 2407 | Leader |
|---|---|---|---|
| LiveBench coding | 89.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev | 1616 | Not measured | Only GPT-5.6 Sol measured |
| AA Coding Index | 77.4 | Not measured | Only GPT-5.6 Sol measured |
| LiveCodeBench (Vals AI) | 82.6% | Not measured | Only GPT-5.6 Sol measured |
| SWE-bench Verified (Vals AI) | 96.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 4 (Vals AI) | 37.9% | Not measured | Only GPT-5.6 Sol measured |
| Vibe Code Bench (Vals AI) | 80.5% | Not measured | Only GPT-5.6 Sol measured |
| Code migration (Vals AI) | 52.9% | Not measured | Only GPT-5.6 Sol measured |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GPT-5.6 Sol measured |
| IOI (Vals AI) | 91.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 85.8% | Not measured | Only GPT-5.6 Sol measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| SkillsBench (Vals AI) | 54.1% | Not measured | Only GPT-5.6 Sol measured |
| CyberBench (Vals AI) | 76.3% | Not measured | Only GPT-5.6 Sol measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 26.7% | Only Mistral Large 2407 measured |
| SciCode | 57.8% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench Hard (Artificial Analysis run) | 65.9% | Not measured | Only GPT-5.6 Sol measured |
| FrontierCode | 47.5% | Not measured | Only GPT-5.6 Sol measured |
| FrontierSWE | 32.2% | Not measured | Only GPT-5.6 Sol measured |
| GSO-Bench | 76.5% | Not measured | Only GPT-5.6 Sol measured |
| WeirdML | 88.8% | Not measured | Only GPT-5.6 Sol measured |
| MirrorCode | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| ALE-Bench | 2176.9 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: coding | 1496 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: software and IT | 1489 | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev: React | 1620 | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev: HTML | 1618 | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev: from an image | 1609 | Not measured | Only GPT-5.6 Sol measured |
| SciCode | 10.8% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Sol | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.0 | 7.6 | GPT-5.6 Sol |
| Epoch Capabilities Index | 162.8 | Not measured | Only GPT-5.6 Sol measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Sol | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Agentic Index | 50.2 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building web apps | 1245 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building full-stack apps | 1261 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building mobile apps | 1248 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building Android apps | 1240 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building Godot games | 1260 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: agents building games | 1266 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: HTML slides | 1236 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: PowerPoint slides | 1215 | Not measured | Only GPT-5.6 Sol measured |
| LiveBench research | 54.5% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench browser | 88.0% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench long agentic | 48.5% | Not measured | Only GPT-5.6 Sol measured |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-5.6 Sol measured |
| Finance Agent (Vals AI) | 53.8% | Not measured | Only GPT-5.6 Sol measured |
| Tax Agent Bench (Vals AI) | 31.1% | Not measured | Only GPT-5.6 Sol measured |
| Legal Research Bench (Vals AI) | 48.1% | Not measured | Only GPT-5.6 Sol measured |
| Excel Modeling Benchmark (Vals AI) | 72.3% | Not measured | Only GPT-5.6 Sol measured |
| BioMysteryBench (Vals AI) | 71.1% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| Vals Science Index | 41.5% | Not measured | Only GPT-5.6 Sol measured |
| Time Horizon Index (Vals AI) | 23.8% | Not measured | Only GPT-5.6 Sol measured |
| CUA-bench (Vals AI) | 8.3% | Not measured | Only GPT-5.6 Sol measured |
| τ²-Bench Telecom | 85.1% | 33.0% | GPT-5.6 Sol |
| OSWorld 2.0 | 27.3% | Not measured | Only GPT-5.6 Sol measured |
| BALROG | 60.0% | Not measured | Only GPT-5.6 Sol measured |
| PostTrainBench | 36.2% | Not measured | Only GPT-5.6 Sol measured |
| Vending-Bench 2 | $9,619 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Search | 1261 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 13 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5.6 Sol | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond | 95.2% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench reasoning | 91.7% | Not measured | Only GPT-5.6 Sol measured |
| MysteryMechanism (Vals AI) | 33.3% | Not measured | Only GPT-5.6 Sol measured |
| MMMU Pro (Vals AI) | 88.8% | Not measured | Only GPT-5.6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | 94.1% | 47.2% | GPT-5.6 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 49.5% | 2.9% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 93.5% | 49.0% | GPT-5.6 Sol |
| ARC-AGI | 97.5% | Not measured | Only GPT-5.6 Sol measured |
| ARC-AGI-2 | 92.5% | Not measured | Only GPT-5.6 Sol measured |
| SimpleBench | 64.8% | 22.5% | GPT-5.6 Sol |
| Chess puzzles | 59.0% | Not measured | Only GPT-5.6 Sol measured |
| Mystery game puzzles | 58.0% | Not measured | Only GPT-5.6 Sol measured |
| EBR-bench | 44.8% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts | 1483 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: expert | 1517 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts in English | 1477 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: diagrams | 1305 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: homework | 1305 | Not measured | Only GPT-5.6 Sol measured |
| Kagi LLM Benchmark | 67.0% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 3 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Sol | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 0.0 | Only Mistral Large 2407 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 0.0% | Only Mistral Large 2407 measured |
| OTIS Mock AIME | 100.0% | 8.5% | GPT-5.6 Sol |
| FrontierMath, tiers 1 to 3 | 89.1% | Not measured | Only GPT-5.6 Sol measured |
| FrontierMath, tier 4 | 82.9% | Not measured | Only GPT-5.6 Sol measured |
| ProofBench | 83.0% | Not measured | Only GPT-5.6 Sol measured |
| MATH level 5 | Not measured | 44.8% | Only Mistral Large 2407 measured |
| LMArena Text: math | 1473 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: mathematical work | 1496 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | GPT-5.6 Sol | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro | 89.1% | Not measured | Only GPT-5.6 Sol measured |
| LegalBench (Vals AI) | 87.0% | Not measured | Only GPT-5.6 Sol measured |
| CorpFin (Vals AI) | 64.4% | Not measured | Only GPT-5.6 Sol measured |
| TaxEval (Vals AI) | 74.8% | Not measured | Only GPT-5.6 Sol measured |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Sol measured |
| MedCode (Vals AI) | 44.0% | Not measured | Only GPT-5.6 Sol measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 68.3% | Only Mistral Large 2407 measured |
| SimpleQA Verified | 69.7% | Not measured | Only GPT-5.6 Sol measured |
| MMLU | Not measured | 80.0% | Only Mistral Large 2407 measured |
| LMArena Text: science | 1455 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: medicine and healthcare | 1431 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: legal and government | 1476 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: business and finance | 1457 | Not measured | Only GPT-5.6 Sol measured |
| FACTS Benchmark Suite | 52.4% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.6 Sol | Mistral Large 2407 | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: UI components | 1335 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: game dev | 1367 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: data viz | 1322 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: 3D | 1384 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: all code categories | 1341 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: SVG | 1314 | Not measured | Only GPT-5.6 Sol measured |
| Design Arena: ASCII art | 1304 | Not measured | Only GPT-5.6 Sol measured |
| LiveBench design | 81.6% | Not measured | Only GPT-5.6 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.6 Sol | Mistral Large 2407 | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.8% | Not measured | Only GPT-5.6 Sol measured |
| Public Benefits Bench (Vals AI) | 66.5% | Not measured | Only GPT-5.6 Sol measured |
| SAGE (Vals AI) | 52.6% | Not measured | Only GPT-5.6 Sol measured |
| MedScribe (Vals AI) | 85.2% | Not measured | Only GPT-5.6 Sol measured |
| Vals Multimodal Index | 72.6% | Not measured | Only GPT-5.6 Sol measured |
| IFBench | 72.7% | 31.6% | GPT-5.6 Sol |
| Long context reasoning (AA-LCR) | 84.0% | 2.0% | GPT-5.6 Sol |
| DTBench | 95.7% | 61.2% | GPT-5.6 Sol |
| LMCA | 58.5% | Not measured | Only GPT-5.6 Sol measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| Surface Evolver Bench | 93.1% | Not measured | Only GPT-5.6 Sol measured |
| Furniture assembly | 56.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text | 1457 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: instruction following | 1483 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: creative writing | 1449 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision | 1281 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: English | 1457 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: non-English | 1451 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Chinese | 1525 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: German | 1476 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Japanese | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Korean | 1442 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: French | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Spanish | 1443 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Russian | 1471 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Polish | 1459 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: multi-turn | 1461 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: longer queries | 1481 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: writing and language | 1462 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: entertainment and media | 1450 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: English | 1280 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: Chinese | 1313 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: reading text | 1290 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: humor | 1246 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: creative writing | 1283 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Document | 1483 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol | 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
GPT-5.6 Sol$0.014Fits in one request
Mistral Large 2407$0.0050Fits in one request
Mistral Large 2407 costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Sol$0.260Fits in one request
Mistral Large 2407$0.118Fits in one request
Mistral Large 2407 costs less
Agent loop
200K input + 10K output tokens
GPT-5.6 Sol$1.00Fits in one request
Mistral Large 2407$0.460Exceeds the context window
Mistral Large 2407 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 | OpenAI GPT-5.6 Sol | Mistral Large 2407 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | November 19, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $4.00 | $2.00 |
| Output $/Mtok | $20.00 | $6.00 |
| Coding index | 73.5 | Not benchmarked |
| Independent measurements behind it | 6 | 0 |
| Cost per solved task | $1.20 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Sol better than Mistral Large 2407 for coding?
Only GPT-5.6 Sol has a coding score, 73.5 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 3.3x cheaper ($6.00 vs $20.00 per million).
Which is cheaper, GPT-5.6 Sol or Mistral Large 2407?
GPT-5.6 Sol costs $4.00 per million input tokens and $20.00 per million output tokens; Mistral Large 2407 costs $2.00 and $6.00. Cost per solved task is known for GPT-5.6 Sol only.
Which has the bigger context window, GPT-5.6 Sol or Mistral Large 2407?
GPT-5.6 Sol, with 1.05M tokens against 131K.
How many benchmarks is the GPT-5.6 Sol vs Mistral Large 2407 comparison based on?
10 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Sol's coding index rests on 6 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.