GPT-5.6 Sol vs Mistral Small 3.1 24B
Mistral Small 3.1 24B costs 36.0x less per output token than GPT-5.6 Sol ($0.555 vs $20.00 per million). Updated October 22, 2026; 36 benchmarks have a result for both.
vs boards won of 36 shared
GPT-5.6 Sol or Mistral Small 3.1 24B: which to pick
Only GPT-5.6 Sol has a coding score, 73.5 on the Vibe Coding Index; Mistral Small 3.1 24B has none, so the two cannot be ranked on coding.
Pick Mistral Small 3.1 24B for output tokens 36.0x cheaper ($0.555 vs $20.00 per million).
Model AGPT-5.6 SolOpenAI73.5/1006 independent benchmarks$1.20 per solved taskModel BMistral Small 3.1 24BMistral AINot benchmarkedNo coding benchmarkNo cost per solved task
- Shared results
- 36
- GPT-5.6 Sol only
- 103
- Mistral Small 3.1 24B only
- 2
- 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
No call
Only GPT-5.6 Sol has a AA Intelligence Index result, 47.0.
Directional only
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 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3.1 24B
GPT-5.6 Sol $0.014, Mistral Small 3.1 24B $0.0006 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 Small 3.1 24B not benchmarked.
- Widest category
- Math, 36.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: GPT-5.6 Sol 1525 against Mistral Small 3.1 24B 1253, 45.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol $20.00 against Mistral Small 3.1 24B $0.555 per million output tokens, 36.0x apart.
- Context window
- GPT-5.6 Sol takes 1.05M tokens, Mistral Small 3.1 24B 128K.
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 Small 3.1 24B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 23 | 0 | ||
| All shared boards | 36 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 24B | 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 |
| 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 | 1309 | GPT-5.6 Sol |
| LMArena Text: software and IT | 1489 | 1301 | GPT-5.6 Sol |
| 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 11 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.0 | Not measured | Only GPT-5.6 Sol measured |
| Epoch Capabilities Index | 162.8 | Not measured | Only GPT-5.6 Sol measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index28 boards, none shared
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 24B | 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% | Not measured | Only GPT-5.6 Sol measured |
| 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 index50
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 24B | 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% | Not measured | Only GPT-5.6 Sol measured |
| Humanity's Last Exam (Artificial Analysis run) | 49.5% | Not measured | Only GPT-5.6 Sol measured |
| GPQA Diamond (Epoch AI run) | 93.5% | Not measured | Only GPT-5.6 Sol measured |
| 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% | Not measured | Only GPT-5.6 Sol measured |
| 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 | 1278 | GPT-5.6 Sol |
| LMArena Text: expert | 1517 | 1257 | GPT-5.6 Sol |
| LMArena Text: hard prompts in English | 1477 | 1290 | GPT-5.6 Sol |
| LMArena Vision: diagrams | 1305 | 1119 | GPT-5.6 Sol |
| LMArena Vision: homework | 1305 | 1158 | GPT-5.6 Sol |
| 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 index20
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | Not measured | Only GPT-5.6 Sol measured |
| 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 |
| LMArena Text: math | 1473 | 1261 | GPT-5.6 Sol |
| LMArena Text: mathematical work | 1496 | 1271 | GPT-5.6 Sol |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 24B | 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 |
| SimpleQA Verified | 69.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: science | 1455 | 1265 | GPT-5.6 Sol |
| LMArena Text: medicine and healthcare | 1431 | 1267 | GPT-5.6 Sol |
| LMArena Text: legal and government | 1476 | 1271 | GPT-5.6 Sol |
| LMArena Text: business and finance | 1457 | 1257 | GPT-5.6 Sol |
| FACTS Benchmark Suite | 52.4% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 24B | 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 index230
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 24B | 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% | Not measured | Only GPT-5.6 Sol measured |
| Long context reasoning (AA-LCR) | 84.0% | Not measured | Only GPT-5.6 Sol measured |
| DTBench | 95.7% | Not measured | Only GPT-5.6 Sol measured |
| LMCA | 58.5% | Not measured | Only GPT-5.6 Sol 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 | 1277 | GPT-5.6 Sol |
| LMArena Text: instruction following | 1483 | 1263 | GPT-5.6 Sol |
| LMArena Text: creative writing | 1449 | 1252 | GPT-5.6 Sol |
| LMArena Vision | 1281 | 1136 | GPT-5.6 Sol |
| LMArena Text: English | 1457 | 1295 | GPT-5.6 Sol |
| LMArena Text: non-English | 1451 | 1255 | GPT-5.6 Sol |
| LMArena Text: Chinese | 1525 | 1253 | GPT-5.6 Sol |
| LMArena Text: German | 1476 | 1266 | GPT-5.6 Sol |
| LMArena Text: Japanese | 1472 | 1208 | GPT-5.6 Sol |
| LMArena Text: Korean | 1442 | 1207 | GPT-5.6 Sol |
| LMArena Text: French | 1472 | 1273 | GPT-5.6 Sol |
| LMArena Text: Spanish | 1443 | 1284 | GPT-5.6 Sol |
| LMArena Text: Russian | 1471 | 1263 | GPT-5.6 Sol |
| LMArena Text: Polish | 1459 | 1242 | GPT-5.6 Sol |
| LMArena Text: multi-turn | 1461 | 1270 | GPT-5.6 Sol |
| LMArena Text: longer queries | 1481 | 1299 | GPT-5.6 Sol |
| LMArena Text: writing and language | 1462 | 1268 | GPT-5.6 Sol |
| LMArena Text: entertainment and media | 1450 | 1250 | GPT-5.6 Sol |
| LMArena Vision: English | 1280 | 1157 | GPT-5.6 Sol |
| LMArena Vision: Chinese | 1313 | 1155 | GPT-5.6 Sol |
| LMArena Vision: reading text | 1290 | 1132 | GPT-5.6 Sol |
| LMArena Vision: humor | 1246 | 1142 | GPT-5.6 Sol |
| LMArena Vision: captioning | Not measured | 1153 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: recognizing things | Not measured | 1155 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: creative writing | 1283 | 1123 | GPT-5.6 Sol |
| LMArena Document | 1483 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 9 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 24B | 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 Small 3.1 24B$0.0006Fits in one request
Mistral Small 3.1 24B costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Sol$0.260Fits in one request
Mistral Small 3.1 24B$0.019Fits in one request
Mistral Small 3.1 24B costs less
Agent loop
200K input + 10K output tokens
GPT-5.6 Sol$1.00Fits in one request
Mistral Small 3.1 24B$0.076Exceeds the context window
Mistral Small 3.1 24B 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 Small 3.1 24B |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | March 17, 2025 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $4.00 | $0.351 |
| Output $/Mtok | $20.00 | $0.555 |
| 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 Small 3.1 24B for coding?
Only GPT-5.6 Sol has a coding score, 73.5 on the Vibe Coding Index; Mistral Small 3.1 24B has none, so the two cannot be ranked on coding. Pick Mistral Small 3.1 24B for output tokens 36.0x cheaper ($0.555 vs $20.00 per million).
Which is cheaper, GPT-5.6 Sol or Mistral Small 3.1 24B?
GPT-5.6 Sol costs $4.00 per million input tokens and $20.00 per million output tokens; Mistral Small 3.1 24B costs $0.351 and $0.555. Cost per solved task is known for GPT-5.6 Sol only.
Which has the bigger context window, GPT-5.6 Sol or Mistral Small 3.1 24B?
GPT-5.6 Sol, with 1.05M tokens against 128K.
How many benchmarks is the GPT-5.6 Sol vs Mistral Small 3.1 24B comparison based on?
36 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 Small 3.1 24B's on 0. Where only one model has a result, the page says so instead of naming a winner.