GPT-5.2 vs Mistral Small 3.1 24B
Mistral Small 3.1 24B costs 25.2x less per output token than GPT-5.2 ($0.555 vs $14.00 per million). Updated October 21, 2026; 38 benchmarks have a result for both.
GPT-5.2 or Mistral Small 3.1 24B: which to pick
Only GPT-5.2 has a coding score, 55.9 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 25.2x cheaper ($0.555 vs $14.00 per million).
- Shared results
- 38
- GPT-5.2 only
- 76
- Mistral Small 3.1 24B only
- 0
- 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-5.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.2 has a AA Intelligence Index result, 30.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.2 has a GPQA Diamond result, 91.7%.
Directional only
Math
AIME
No call
Only GPT-5.2 has a AIME result, 96.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.2 has a MMLU Pro result, 86.2%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.2 has a Design Arena: websites result, 1195.
Directional only
Long documents
Listed context window
GPT-5.2
GPT-5.2 takes 400K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3.1 24B
GPT-5.2 $0.0088, 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-5.2 has the number, $1.91.
Directional only
At a glance
- Coding index
- GPT-5.2 55.9 from 3 benchmarks, Mistral Small 3.1 24B not benchmarked.
- Widest category
- Math, 29.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Japanese: GPT-5.2 1420 against Mistral Small 3.1 24B 1208, 35.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against Mistral Small 3.1 24B $0.555 per million output tokens, 25.2x apart.
- Context window
- GPT-5.2 takes 400K 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-5.2 | Bars | Mistral Small 3.1 24B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 25 | 0 | ||
| All shared boards | 38 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-5.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2 measured |
| SWE-bench Multilingual | 66.7% | Not measured | Only GPT-5.2 measured |
| LMArena WebDev | 1417 | Not measured | Only GPT-5.2 measured |
| LiveCodeBench (Vals AI) | 85.4% | Not measured | Only GPT-5.2 measured |
| SWE-bench Verified (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| Vibe Code Bench (Vals AI) | 53.5% | Not measured | Only GPT-5.2 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GPT-5.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | Not measured | Only GPT-5.2 measured |
| Terminal-Bench | 64.9% | Not measured | Only GPT-5.2 measured |
| GSO-Bench | 27.5% | Not measured | Only GPT-5.2 measured |
| WeirdML | 72.2% | Not measured | Only GPT-5.2 measured |
| ALE-Bench | 1293.5 | Not measured | Only GPT-5.2 measured |
| LMArena Text: coding | 1442 | 1309 | GPT-5.2 |
| LMArena Text: software and IT | 1434 | 1301 | GPT-5.2 |
| LMArena WebDev: HTML | 1427 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | Not measured | Only GPT-5.2 measured |
| Epoch Capabilities Index | 153.7 | Not measured | Only GPT-5.2 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1086 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building full-stack apps | 1043 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building mobile apps | 1093 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | 1058 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2 measured |
| τ²-Bench Telecom | 84.8% | Not measured | Only GPT-5.2 measured |
| METR time horizons | 75.3% | Not measured | Only GPT-5.2 measured |
| Remote Labor Index | 2.5% | Not measured | Only GPT-5.2 measured |
| GDPval | 49.7% | Not measured | Only GPT-5.2 measured |
| DeepResearch Bench | 41.1% | Not measured | Only GPT-5.2 measured |
| Vending-Bench 2 | $3,591 | Not measured | Only GPT-5.2 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 55.9% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.2 measured |
| MMMU Pro (Vals AI) | 86.7% | Not measured | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 90.3% | Not measured | Only GPT-5.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 37.7% | Not measured | Only GPT-5.2 measured |
| GPQA Diamond (Epoch AI run) | 91.4% | Not measured | Only GPT-5.2 measured |
| ARC-AGI | 86.2% | Not measured | Only GPT-5.2 measured |
| ARC-AGI-2 | 52.9% | Not measured | Only GPT-5.2 measured |
| SimpleBench | 45.8% | Not measured | Only GPT-5.2 measured |
| Chess puzzles | 48.0% | Not measured | Only GPT-5.2 measured |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5.2 measured |
| EBR-bench | 23.0% | Not measured | Only GPT-5.2 measured |
| VPCT | 84.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts | 1428 | 1278 | GPT-5.2 |
| LMArena Text: expert | 1445 | 1257 | GPT-5.2 |
| LMArena Text: hard prompts in English | 1431 | 1290 | GPT-5.2 |
| LMArena Vision: diagrams | 1265 | 1119 | GPT-5.2 |
| LMArena Vision: homework | 1276 | 1158 | GPT-5.2 |
| Kagi LLM Benchmark | 73.3% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AIME | 96.9% | Not measured | Only GPT-5.2 measured |
| MGSM (Vals AI) | 94.0% | Not measured | Only GPT-5.2 measured |
| AA Math Index | 99.0 | Not measured | Only GPT-5.2 measured |
| AIME 2025 (Artificial Analysis run) | 99.0% | Not measured | Only GPT-5.2 measured |
| OTIS Mock AIME | 96.1% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tier 4 | 31.7% | Not measured | Only GPT-5.2 measured |
| ProofBench | 15.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: math | 1440 | 1261 | GPT-5.2 |
| LMArena Text: mathematical work | 1442 | 1271 | GPT-5.2 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | Not measured | Only GPT-5.2 measured |
| LegalBench (Vals AI) | 82.8% | Not measured | Only GPT-5.2 measured |
| CaseLaw (Vals AI) | 66.0% | Not measured | Only GPT-5.2 measured |
| CorpFin (Vals AI) | 65.9% | Not measured | Only GPT-5.2 measured |
| TaxEval (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| MortgageTax (Vals AI) | 67.1% | Not measured | Only GPT-5.2 measured |
| MedQA (Vals AI) | 94.1% | Not measured | Only GPT-5.2 measured |
| MedCode (Vals AI) | 49.8% | Not measured | Only GPT-5.2 measured |
| MMLU Pro (Artificial Analysis run) | 87.4% | Not measured | Only GPT-5.2 measured |
| SimpleQA Verified | 37.1% | Not measured | Only GPT-5.2 measured |
| LMArena Text: science | 1425 | 1265 | GPT-5.2 |
| LMArena Text: medicine and healthcare | 1425 | 1267 | GPT-5.2 |
| LMArena Text: legal and government | 1427 | 1271 | GPT-5.2 |
| LMArena Text: business and finance | 1414 | 1257 | GPT-5.2 |
| FACTS Benchmark Suite | 48.5% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| Design Arena: websites | 1195 | Not measured | Only GPT-5.2 measured |
| Design Arena: UI components | 1190 | Not measured | Only GPT-5.2 measured |
| Design Arena: game dev | 1195 | Not measured | Only GPT-5.2 measured |
| Design Arena: data viz | 1202 | Not measured | Only GPT-5.2 measured |
| Design Arena: 3D | 1079 | Not measured | Only GPT-5.2 measured |
| Design Arena: all code categories | 1170 | Not measured | Only GPT-5.2 measured |
| Design Arena: SVG | 1139 | Not measured | Only GPT-5.2 measured |
| Design Arena: ASCII art | 1152 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index250
| Benchmark | GPT-5.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 49.3% | Not measured | Only GPT-5.2 measured |
| MedScribe (Vals AI) | 84.4% | Not measured | Only GPT-5.2 measured |
| IFBench | 75.4% | Not measured | Only GPT-5.2 measured |
| Long context reasoning (AA-LCR) | 82.7% | Not measured | Only GPT-5.2 measured |
| DTBench | 90.9% | Not measured | Only GPT-5.2 measured |
| LMCA | 43.9% | Not measured | Only GPT-5.2 measured |
| CL-bench | 18.2% | Not measured | Only GPT-5.2 measured |
| Furniture assembly | 38.3% | Not measured | Only GPT-5.2 measured |
| LMArena Text | 1416 | 1277 | GPT-5.2 |
| LMArena Text: instruction following | 1409 | 1263 | GPT-5.2 |
| LMArena Text: creative writing | 1376 | 1252 | GPT-5.2 |
| LMArena Vision | 1246 | 1136 | GPT-5.2 |
| LMArena Text: English | 1422 | 1295 | GPT-5.2 |
| LMArena Text: non-English | 1405 | 1255 | GPT-5.2 |
| LMArena Text: Chinese | 1447 | 1253 | GPT-5.2 |
| LMArena Text: German | 1444 | 1266 | GPT-5.2 |
| LMArena Text: Japanese | 1420 | 1208 | GPT-5.2 |
| LMArena Text: Korean | 1374 | 1207 | GPT-5.2 |
| LMArena Text: French | 1454 | 1273 | GPT-5.2 |
| LMArena Text: Spanish | 1413 | 1284 | GPT-5.2 |
| LMArena Text: Russian | 1415 | 1263 | GPT-5.2 |
| LMArena Text: Polish | 1426 | 1242 | GPT-5.2 |
| LMArena Text: multi-turn | 1419 | 1270 | GPT-5.2 |
| LMArena Text: longer queries | 1413 | 1299 | GPT-5.2 |
| LMArena Text: writing and language | 1392 | 1268 | GPT-5.2 |
| LMArena Text: entertainment and media | 1378 | 1250 | GPT-5.2 |
| LMArena Vision: English | 1243 | 1157 | GPT-5.2 |
| LMArena Vision: Chinese | 1305 | 1155 | GPT-5.2 |
| LMArena Vision: reading text | 1257 | 1132 | GPT-5.2 |
| LMArena Vision: humor | 1227 | 1142 | GPT-5.2 |
| LMArena Vision: captioning | 1267 | 1153 | GPT-5.2 |
| LMArena Vision: recognizing things | 1232 | 1155 | GPT-5.2 |
| LMArena Vision: creative writing | 1225 | 1123 | GPT-5.2 |
| LMArena Document | 1405 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2 | 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
Mistral Small 3.1 24B costs less
Repository review
50K input + 3K output tokens
Mistral Small 3.1 24B costs less
Agent loop
200K input + 10K output tokens
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-5.2 | Mistral Small 3.1 24B |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | December 10, 2025 | March 17, 2025 |
| Context window | 400K | 128K |
| Input $/Mtok | $1.75 | $0.351 |
| Output $/Mtok | $14.00 | $0.555 |
| Coding index | 55.9 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $1.91 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than Mistral Small 3.1 24B for coding?
Only GPT-5.2 has a coding score, 55.9 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 25.2x cheaper ($0.555 vs $14.00 per million).
Which is cheaper, GPT-5.2 or Mistral Small 3.1 24B?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Mistral Small 3.1 24B costs $0.351 and $0.555. Cost per solved task is known for GPT-5.2 only.
Which has the bigger context window, GPT-5.2 or Mistral Small 3.1 24B?
GPT-5.2, with 400K tokens against 128K.
How many benchmarks is the GPT-5.2 vs Mistral Small 3.1 24B comparison based on?
38 benchmarks have a result for both models as of October 21, 2026. GPT-5.2's coding index rests on 3 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.