GPT-6.1 Sol vs Mistral Medium 3.1
GPT-6.1 Sol leads Mistral Medium 3.1 on the Artificial Analysis Intelligence Index, 51.8 to 9.2, and Mistral Medium 3.1 costs 5.0x less per output token ($2.00 vs $10.00 per million). Updated October 21, 2026; 43 benchmarks have a result for both.
GPT-6.1 Sol or Mistral Medium 3.1: which to pick
Only GPT-6.1 Sol has a coding score, 82.4 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding.
Pick Mistral Medium 3.1 for output tokens 5.0x cheaper ($2.00 vs $10.00 per million) and work measured by LMArena Text: science or LMArena Text: medicine and healthcare, where it leads.
- Shared results
- 43
- GPT-6.1 Sol only
- 55
- Mistral Medium 3.1 only
- 31
- 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 GPT-6.1 Sol has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-6.1 Sol
GPT-6.1 Sol leads, 51.8 against 9.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.1 has a AA Agentic Index result, 1.9.
Directional only
Math
AIME
No call
Only Mistral Medium 3.1 has a AIME result, 42.3%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Medium 3.1 has a MMLU Pro result, 75.3%.
Directional only
Web design
Design Arena: websites
GPT-6.1 Sol
GPT-6.1 Sol leads, 1321 against 1133.
Same board, both measured
General
Vals Index
No call
Only GPT-6.1 Sol has a Vals Index result, 63.9%.
Directional only
Long documents
Listed context window
GPT-6.1 Sol
GPT-6.1 Sol takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Medium 3.1
GPT-6.1 Sol $0.0070, Mistral Medium 3.1 $0.0014 per turn.
List prices
At a glance
- Coding index
- GPT-6.1 Sol 82.4 from 5 benchmarks, Mistral Medium 3.1 not benchmarked.
- Widest category
- Intelligence, 42.6 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: GPT-6.1 Sol 2743.8 against Mistral Medium 3.1 210.2, 84.5 points apart on a 0 to 100 scale.
- Output price
- GPT-6.1 Sol $10.00 against Mistral Medium 3.1 $2.00 per million output tokens, 5.0x apart.
- Context window
- GPT-6.1 Sol takes 1.05M tokens, Mistral Medium 3.1 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-6.1 Sol | Bars | Mistral Medium 3.1 | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 2 | 2 | ||
| Web design | 6 | 0 | ||
| General | 20 | 0 | ||
| All shared boards | 41 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GPT-6.1 Sol | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev | 1753 | Not measured | Only GPT-6.1 Sol measured |
| AA Coding Index | Not measured | 20.5 | Only Mistral Medium 3.1 measured |
| LiveCodeBench (Vals AI) | Not measured | 44.8% | Only Mistral Medium 3.1 measured |
| Terminal-Bench 4 (Vals AI) | 55.0% | Not measured | Only GPT-6.1 Sol measured |
| Vibe Code Bench (Vals AI) | 88.9% | Not measured | Only GPT-6.1 Sol measured |
| Code migration (Vals AI) | 65.1% | Not measured | Only GPT-6.1 Sol measured |
| ProgramBench (Vals AI) | 3.0% | Not measured | Only GPT-6.1 Sol measured |
| IOI (Vals AI) | 96.9% | Not measured | Only GPT-6.1 Sol measured |
| SWE-bench Multimodal (Vals AI) | 43.5% | Not measured | Only GPT-6.1 Sol measured |
| CyberBench (Vals AI) | 39.3% | Not measured | Only GPT-6.1 Sol measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 40.6% | Only Mistral Medium 3.1 measured |
| SciCode | 55.8% | 32.4% | GPT-6.1 Sol |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 10.6% | Only Mistral Medium 3.1 measured |
| FrontierCode | 50.2% | Not measured | Only GPT-6.1 Sol measured |
| ALE-Bench | 2743.8 | 210.2 | GPT-6.1 Sol |
| LMArena Text: coding | 1478 | 1433 | GPT-6.1 Sol |
| LMArena Text: software and IT | 1476 | 1436 | GPT-6.1 Sol |
| LMArena WebDev: React | 1754 | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev: HTML | 1800 | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev: from an image | 1702 | Not measured | Only GPT-6.1 Sol measured |
| SciCode | 9.2% | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6.1 Sol | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 51.8 | 9.2 | GPT-6.1 Sol |
| Epoch Capabilities Index | 168.4 | Not measured | Only GPT-6.1 Sol measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
| Benchmark | GPT-6.1 Sol | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.9 | Only Mistral Medium 3.1 measured |
| Design Arena: agents building web apps | 1306 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: agents building mobile apps | 1204 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: spreadsheets | 1242 | Not measured | Only GPT-6.1 Sol measured |
| LiveBench research | 83.5% | Not measured | Only GPT-6.1 Sol measured |
| LiveBench browser | 88.0% | Not measured | Only GPT-6.1 Sol measured |
| LiveBench long agentic | 73.0% | Not measured | Only GPT-6.1 Sol measured |
| SREBench (Vals AI) | 50.8% | Not measured | Only GPT-6.1 Sol measured |
| Finance Agent (Vals AI) | 52.0% | Not measured | Only GPT-6.1 Sol measured |
| Tax Agent Bench (Vals AI) | 21.8% | Not measured | Only GPT-6.1 Sol measured |
| Legal Research Bench (Vals AI) | 38.5% | Not measured | Only GPT-6.1 Sol measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 6.3% | Not measured | Only GPT-6.1 Sol measured |
| Excel Modeling Benchmark (Vals AI) | 70.8% | Not measured | Only GPT-6.1 Sol measured |
| BioMysteryBench (Vals AI) | 79.6% | Not measured | Only GPT-6.1 Sol measured |
| Terminal-Bench-Science (Vals AI) | 52.9% | Not measured | Only GPT-6.1 Sol measured |
| Vals Science Index | 59.6% | Not measured | Only GPT-6.1 Sol measured |
| Robo-bench (Vals AI) | 28.3% | Not measured | Only GPT-6.1 Sol measured |
| τ²-Bench Telecom | Not measured | 40.6% | Only Mistral Medium 3.1 measured |
| APEX-Agents | 60.0% | Not measured | Only GPT-6.1 Sol measured |
| SpireClimb | 24.7% | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-6.1 Sol | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| LiveBench reasoning | 92.6% | Not measured | Only GPT-6.1 Sol measured |
| MysteryMechanism (Vals AI) | 46.4% | Not measured | Only GPT-6.1 Sol measured |
| MMMU Pro (Vals AI) | Not measured | 63.0% | Only Mistral Medium 3.1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 58.8% | Only Mistral Medium 3.1 measured |
| Humanity's Last Exam (Artificial Analysis run) | 52.9% | 4.7% | GPT-6.1 Sol |
| GPQA Diamond (Epoch AI run) | 95.4% | Not measured | Only GPT-6.1 Sol measured |
| ARC-AGI | 98.5% | Not measured | Only GPT-6.1 Sol measured |
| ARC-AGI-2 | 94.2% | Not measured | Only GPT-6.1 Sol measured |
| Chess puzzles | 68.0% | Not measured | Only GPT-6.1 Sol measured |
| Mystery game puzzles | 80.0% | Not measured | Only GPT-6.1 Sol measured |
| EBR-bench | 54.3% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: hard prompts | 1463 | 1426 | GPT-6.1 Sol |
| LMArena Text: expert | 1489 | 1408 | GPT-6.1 Sol |
| LMArena Text: hard prompts in English | 1465 | 1435 | GPT-6.1 Sol |
| LMArena Vision: diagrams | 1306 | 1175 | GPT-6.1 Sol |
| LMArena Vision: homework | 1356 | 1165 | GPT-6.1 Sol |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6.1 Sol | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AIME | Not measured | 42.3% | Only Mistral Medium 3.1 measured |
| MATH 500 (Vals AI) | Not measured | 87.0% | Only Mistral Medium 3.1 measured |
| MGSM (Vals AI) | Not measured | 90.9% | Only Mistral Medium 3.1 measured |
| AA Math Index | Not measured | 38.3 | Only Mistral Medium 3.1 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 38.3% | Only Mistral Medium 3.1 measured |
| OTIS Mock AIME | 100.0% | Not measured | Only GPT-6.1 Sol measured |
| FrontierMath, tiers 1 to 3 | 93.7% | Not measured | Only GPT-6.1 Sol measured |
| FrontierMath, tier 4 | 100.0% | Not measured | Only GPT-6.1 Sol measured |
| ProofBench | 99.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: math | 1489 | 1408 | GPT-6.1 Sol |
| LMArena Text: mathematical work | 1513 | 1412 | GPT-6.1 Sol |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | GPT-6.1 Sol | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 75.3% | Only Mistral Medium 3.1 measured |
| LegalBench (Vals AI) | Not measured | 62.0% | Only Mistral Medium 3.1 measured |
| CorpFin (Vals AI) | Not measured | 50.7% | Only Mistral Medium 3.1 measured |
| TaxEval (Vals AI) | Not measured | 70.3% | Only Mistral Medium 3.1 measured |
| MortgageTax (Vals AI) | Not measured | 36.5% | Only Mistral Medium 3.1 measured |
| MedQA (Vals AI) | Not measured | 78.2% | Only Mistral Medium 3.1 measured |
| MedCode (Vals AI) | 48.8% | Not measured | Only GPT-6.1 Sol measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 68.3% | Only Mistral Medium 3.1 measured |
| SimpleQA Verified | 73.9% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: science | 1444 | 1444 | Mistral Medium 3.1 |
| LMArena Text: medicine and healthcare | 1433 | 1449 | Mistral Medium 3.1 |
| LMArena Text: legal and government | 1471 | 1439 | GPT-6.1 Sol |
| LMArena Text: business and finance | 1445 | 1418 | GPT-6.1 Sol |
| FACTS Benchmark Suite | 75.3% | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-6.1 Sol | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | 1133 | GPT-6.1 Sol |
| Design Arena: UI components | 1330 | 1100 | GPT-6.1 Sol |
| Design Arena: game dev | 1389 | 1074 | GPT-6.1 Sol |
| Design Arena: data viz | 1280 | 1144 | GPT-6.1 Sol |
| Design Arena: 3D | 1463 | 1081 | GPT-6.1 Sol |
| Design Arena: all code categories | 1346 | 1120 | GPT-6.1 Sol |
| Design Arena: SVG | Not measured | 993 | Only Mistral Medium 3.1 measured |
| Design Arena: ASCII art | Not measured | 997 | Only Mistral Medium 3.1 measured |
| LiveBench design | 82.1% | Not measured | Only GPT-6.1 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index200
| Benchmark | GPT-6.1 Sol | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| Vals Index | 63.9% | Not measured | Only GPT-6.1 Sol measured |
| LiveBench data analysis | 82.7% | Not measured | Only GPT-6.1 Sol measured |
| Public Benefits Bench (Vals AI) | 59.3% | Not measured | Only GPT-6.1 Sol measured |
| SAGE (Vals AI) | 46.5% | Not measured | Only GPT-6.1 Sol measured |
| MedScribe (Vals AI) | 86.5% | Not measured | Only GPT-6.1 Sol measured |
| IFBench | Not measured | 39.8% | Only Mistral Medium 3.1 measured |
| Long context reasoning (AA-LCR) | 84.0% | 42.7% | GPT-6.1 Sol |
| DTBench | 96.5% | 67.5% | GPT-6.1 Sol |
| LMCA | 62.8% | 19.1% | GPT-6.1 Sol |
| Furniture assembly | 80.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text | 1443 | 1424 | GPT-6.1 Sol |
| LMArena Text: instruction following | 1466 | 1398 | GPT-6.1 Sol |
| LMArena Text: creative writing | 1425 | 1391 | GPT-6.1 Sol |
| LMArena Vision | 1288 | 1172 | GPT-6.1 Sol |
| LMArena Text: English | 1450 | 1438 | GPT-6.1 Sol |
| LMArena Text: non-English | 1433 | 1408 | GPT-6.1 Sol |
| LMArena Text: Chinese | 1487 | 1447 | GPT-6.1 Sol |
| LMArena Text: German | Not measured | 1432 | Only Mistral Medium 3.1 measured |
| LMArena Text: Japanese | Not measured | 1379 | Only Mistral Medium 3.1 measured |
| LMArena Text: Korean | Not measured | 1380 | Only Mistral Medium 3.1 measured |
| LMArena Text: French | 1460 | 1460 | GPT-6.1 Sol |
| LMArena Text: Spanish | 1439 | 1433 | GPT-6.1 Sol |
| LMArena Text: Russian | 1457 | 1411 | GPT-6.1 Sol |
| LMArena Text: Polish | Not measured | 1430 | Only Mistral Medium 3.1 measured |
| LMArena Text: multi-turn | 1442 | 1418 | GPT-6.1 Sol |
| LMArena Text: longer queries | 1458 | 1406 | GPT-6.1 Sol |
| LMArena Text: writing and language | 1444 | 1395 | GPT-6.1 Sol |
| LMArena Text: entertainment and media | 1426 | 1390 | GPT-6.1 Sol |
| LMArena Vision: English | 1274 | 1179 | GPT-6.1 Sol |
| LMArena Vision: Chinese | Not measured | 1190 | Only Mistral Medium 3.1 measured |
| LMArena Vision: reading text | 1289 | 1175 | GPT-6.1 Sol |
| LMArena Vision: humor | Not measured | 1168 | Only Mistral Medium 3.1 measured |
| LMArena Vision: captioning | Not measured | 1145 | Only Mistral Medium 3.1 measured |
| LMArena Vision: recognizing things | Not measured | 1147 | Only Mistral Medium 3.1 measured |
| LMArena Vision: creative writing | 1301 | 1200 | GPT-6.1 Sol |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6.1 Sol | Mistral Medium 3.1 | 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 Medium 3.1 costs less
Repository review
50K input + 3K output tokens
Mistral Medium 3.1 costs less
Agent loop
200K input + 10K output tokens
Mistral Medium 3.1 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-6.1 Sol | Mistral Medium 3.1 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | September 29, 2026 | August 13, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $2.00 | $0.40 |
| Output $/Mtok | $10.00 | $2.00 |
| Coding index | 82.4 | Not benchmarked |
| Independent measurements behind it | 5 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6.1 Sol better than Mistral Medium 3.1 for coding?
Only GPT-6.1 Sol has a coding score, 82.4 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding. Pick Mistral Medium 3.1 for output tokens 5.0x cheaper ($2.00 vs $10.00 per million) and work measured by LMArena Text: science or LMArena Text: medicine and healthcare, where it leads.
Which is cheaper, GPT-6.1 Sol or Mistral Medium 3.1?
GPT-6.1 Sol costs $2.00 per million input tokens and $10.00 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-6.1 Sol or Mistral Medium 3.1?
GPT-6.1 Sol, with 1.05M tokens against 131K.
How many benchmarks is the GPT-6.1 Sol vs Mistral Medium 3.1 comparison based on?
43 benchmarks have a result for both models as of October 21, 2026. GPT-6.1 Sol's coding index rests on 5 independent benchmarks and Mistral Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.