GPT-6.1 Sol vs Mistral Small 3
GPT-6.1 Sol leads Mistral Small 3 on the Artificial Analysis Intelligence Index, 51.8 to 6.7, and Mistral Small 3 costs 125.0x less per output token ($0.08 vs $10.00 per million). Updated October 22, 2026; 26 benchmarks have a result for both.
GPT-6.1 Sol or Mistral Small 3: which to pick
Only GPT-6.1 Sol has a coding score, 82.4 on the Vibe Coding Index; Mistral Small 3 has none, so the two cannot be ranked on coding.
Pick Mistral Small 3 for output tokens 125.0x cheaper ($0.08 vs $10.00 per million).
- Shared results
- 26
- GPT-6.1 Sol only
- 72
- Mistral Small 3 only
- 10
- Like-for-like categories
- 6 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 6.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-6.1 Sol has a Design Arena: websites result, 1321.
Directional only
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 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3
GPT-6.1 Sol $0.0070, Mistral Small 3 $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-6.1 Sol 82.4 from 5 benchmarks, Mistral Small 3 not benchmarked.
- Widest category
- Intelligence, 45.1 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-6.1 Sol 84.0% against Mistral Small 3 0.0%, 84.0 points apart on a 0 to 100 scale.
- Output price
- GPT-6.1 Sol $10.00 against Mistral Small 3 $0.08 per million output tokens, 125.0x apart.
- Context window
- GPT-6.1 Sol takes 1.05M tokens, Mistral Small 3 33K.
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 Small 3 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 13 | 0 | ||
| All shared boards | 26 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-6.1 Sol | Mistral Small 3 | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev | 1753 | Not measured | Only GPT-6.1 Sol 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 | 25.2% | Only Mistral Small 3 measured |
| SciCode | 55.8% | Not measured | Only GPT-6.1 Sol measured |
| FrontierCode | 50.2% | Not measured | Only GPT-6.1 Sol measured |
| ALE-Bench | 2743.8 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: coding | 1478 | 1246 | GPT-6.1 Sol |
| LMArena Text: software and IT | 1476 | 1248 | 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 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6.1 Sol | Mistral Small 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 51.8 | 6.7 | 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 index19 boards, none shared
| Benchmark | GPT-6.1 Sol | Mistral Small 3 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1306 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: agents building mobile apps | 1213 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: spreadsheets | 1259 | 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 | 19.6% | Only Mistral Small 3 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 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-6.1 Sol | Mistral Small 3 | 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 |
| GPQA Diamond (Artificial Analysis run) | Not measured | 46.2% | Only Mistral Small 3 measured |
| Humanity's Last Exam (Artificial Analysis run) | 52.9% | 3.8% | 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 | 1233 | GPT-6.1 Sol |
| LMArena Text: expert | 1489 | 1202 | GPT-6.1 Sol |
| LMArena Text: hard prompts in English | 1465 | 1243 | GPT-6.1 Sol |
| LMArena Vision: diagrams | 1306 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: homework | 1356 | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6.1 Sol | Mistral Small 3 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 4.3 | Only Mistral Small 3 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 4.3% | Only Mistral Small 3 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 | 1240 | GPT-6.1 Sol |
| LMArena Text: mathematical work | 1513 | 1243 | GPT-6.1 Sol |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-6.1 Sol | Mistral Small 3 | Leader |
|---|---|---|---|
| MedCode (Vals AI) | 48.8% | Not measured | Only GPT-6.1 Sol measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 65.2% | Only Mistral Small 3 measured |
| SimpleQA Verified | 73.9% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: science | 1444 | 1235 | GPT-6.1 Sol |
| LMArena Text: medicine and healthcare | 1433 | 1215 | GPT-6.1 Sol |
| LMArena Text: legal and government | 1471 | 1219 | GPT-6.1 Sol |
| LMArena Text: business and finance | 1445 | 1205 | 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 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-6.1 Sol | Mistral Small 3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: UI components | 1317 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: game dev | 1389 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: data viz | 1281 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: 3D | 1460 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: all code categories | 1345 | Not measured | Only GPT-6.1 Sol measured |
| LiveBench design | 82.1% | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index130
| Benchmark | GPT-6.1 Sol | Mistral Small 3 | 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 | 26.4% | Only Mistral Small 3 measured |
| Long context reasoning (AA-LCR) | 84.0% | 0.0% | GPT-6.1 Sol |
| DTBench | 96.5% | Not measured | Only GPT-6.1 Sol measured |
| LMCA | 62.8% | Not measured | Only GPT-6.1 Sol measured |
| Furniture assembly | 80.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text | 1443 | 1234 | GPT-6.1 Sol |
| LMArena Text: instruction following | 1466 | 1214 | GPT-6.1 Sol |
| LMArena Text: creative writing | 1425 | 1194 | GPT-6.1 Sol |
| LMArena Vision | 1288 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: English | 1450 | 1256 | GPT-6.1 Sol |
| LMArena Text: non-English | 1433 | 1198 | GPT-6.1 Sol |
| LMArena Text: Chinese | 1487 | 1204 | GPT-6.1 Sol |
| LMArena Text: German | Not measured | 1211 | Only Mistral Small 3 measured |
| LMArena Text: Japanese | Not measured | 1111 | Only Mistral Small 3 measured |
| LMArena Text: Korean | Not measured | 1188 | Only Mistral Small 3 measured |
| LMArena Text: French | 1460 | 1204 | GPT-6.1 Sol |
| LMArena Text: Spanish | 1439 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: Russian | 1457 | 1216 | GPT-6.1 Sol |
| LMArena Text: multi-turn | 1442 | 1217 | GPT-6.1 Sol |
| LMArena Text: longer queries | 1458 | 1246 | GPT-6.1 Sol |
| LMArena Text: writing and language | 1444 | 1214 | GPT-6.1 Sol |
| LMArena Text: entertainment and media | 1426 | 1184 | GPT-6.1 Sol |
| LMArena Vision: English | 1274 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: reading text | 1289 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: creative writing | 1301 | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6.1 Sol | Mistral Small 3 | 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 Small 3 costs less
Repository review
50K input + 3K output tokens
Mistral Small 3 costs less
Agent loop
200K input + 10K output tokens
Mistral Small 3 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 Small 3 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | September 29, 2026 | January 30, 2025 |
| Context window | 1.05M | 33K |
| Input $/Mtok | $2.00 | $0.05 |
| Output $/Mtok | $10.00 | $0.08 |
| 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 Small 3 for coding?
Only GPT-6.1 Sol has a coding score, 82.4 on the Vibe Coding Index; Mistral Small 3 has none, so the two cannot be ranked on coding. Pick Mistral Small 3 for output tokens 125.0x cheaper ($0.08 vs $10.00 per million).
Which is cheaper, GPT-6.1 Sol or Mistral Small 3?
GPT-6.1 Sol costs $2.00 per million input tokens and $10.00 per million output tokens; Mistral Small 3 costs $0.05 and $0.08. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-6.1 Sol or Mistral Small 3?
GPT-6.1 Sol, with 1.05M tokens against 33K.
How many benchmarks is the GPT-6.1 Sol vs Mistral Small 3 comparison based on?
26 benchmarks have a result for both models as of October 22, 2026. GPT-6.1 Sol's coding index rests on 5 independent benchmarks and Mistral Small 3's on 0. Where only one model has a result, the page says so instead of naming a winner.