GPT-5.2 vs Mistral Large 4
Mistral Large 4 leads GPT-5.2 on coding, 56.4 to 55.9 on the Vibe Coding Index, and Mistral Large 4 costs 3.3x less per output token ($4.18 vs $14.00 per million). Updated October 22, 2026; 58 benchmarks have a result for both.
GPT-5.2 or Mistral Large 4: which to pick
GPT-5.2 and Mistral Large 4 are level for coding, 55.9 against 56.4 on the Vibe Coding Index, so price and context decide.
Pick GPT-5.2 for work measured by ALE-Bench or Epoch Capabilities Index, where it leads. Pick Mistral Large 4 for output tokens 3.3x cheaper ($4.18 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
- Shared results
- 58
- GPT-5.2 only
- 56
- Mistral Large 4 only
- 23
- 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
Level: 55.9 against 56.4, inside 2 points.
3 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
Mistral Large 4
Mistral Large 4 leads, 38.4 against 30.4.
Same board, both measured
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
Mistral Large 4
Mistral Large 4 leads, 1257 against 1195.
Same board, both measured
Long documents
Listed context window
Mistral Large 4
Mistral Large 4 takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 4
GPT-5.2 $0.0088, Mistral Large 4 $0.0034 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 Large 4 56.4 from 5 benchmarks.
- Widest category
- Coding, 11.2 points apart on average over 6 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.2 53.5% against Mistral Large 4 78.4%, 24.9 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against Mistral Large 4 $4.18 per million output tokens, 3.3x apart.
- Context window
- GPT-5.2 takes 400K tokens, Mistral Large 4 1.05M.
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 Large 4 | Ties |
|---|---|---|---|---|
| Coding | 1 | 5 | ||
| Intelligence | 1 | 1 | ||
| Reasoning | 5 | 3 | ||
| Math | 3 | 3 | ||
| Knowledge | 3 | 3 | ||
| Web design | 0 | 8 | ||
| General | 9 | 13 | ||
| All shared boards | 22 | 36 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index15
| Benchmark | GPT-5.2 | Mistral Large 4 | 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 |
| LiveBench coding | Not measured | 60.0% | Only Mistral Large 4 measured |
| LMArena WebDev | 1417 | 1543 | Mistral Large 4 |
| 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 |
| Terminal-Bench 4 (Vals AI) | Not measured | 27.8% | Only Mistral Large 4 measured |
| Vibe Code Bench (Vals AI) | 53.5% | 78.4% | Mistral Large 4 |
| Code migration (Vals AI) | Not measured | 30.6% | Only Mistral Large 4 measured |
| IOI (Vals AI) | Not measured | 45.3% | Only Mistral Large 4 measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 33.1% | Only Mistral Large 4 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 14.3% | Only Mistral Large 4 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GPT-5.2 measured |
| SciCode | Not measured | 54.2% | Only Mistral Large 4 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 |
| FrontierCode | Not measured | 39.4% | Only Mistral Large 4 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 | 1213.2 | GPT-5.2 |
| LMArena Text: coding | 1442 | 1478 | Mistral Large 4 |
| LMArena Text: software and IT | 1434 | 1467 | Mistral Large 4 |
| LMArena WebDev: React | Not measured | 1559 | Only Mistral Large 4 measured |
| LMArena WebDev: HTML | 1427 | 1487 | Mistral Large 4 |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.2 | Mistral Large 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 38.4 | Mistral Large 4 |
| Epoch Capabilities Index | 153.7 | 150.0 | GPT-5.2 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared
| Benchmark | GPT-5.2 | Mistral Large 4 | 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 | 1059 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2 measured |
| LiveBench research | Not measured | 28.1% | Only Mistral Large 4 measured |
| LiveBench browser | Not measured | 8.0% | Only Mistral Large 4 measured |
| LiveBench long agentic | Not measured | 11.8% | Only Mistral Large 4 measured |
| Finance Agent (Vals AI) | Not measured | 54.7% | Only Mistral Large 4 measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.4% | Only Mistral Large 4 measured |
| Legal Research Bench (Vals AI) | Not measured | 31.7% | Only Mistral Large 4 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 17.5% | Only Mistral Large 4 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 56.2% | Only Mistral Large 4 measured |
| BioMysteryBench (Vals AI) | Not measured | 67.0% | Only Mistral Large 4 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 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index53
| Benchmark | GPT-5.2 | Mistral Large 4 | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.2 measured |
| LiveBench reasoning | Not measured | 83.9% | Only Mistral Large 4 measured |
| MysteryMechanism (Vals AI) | Not measured | 12.2% | Only Mistral Large 4 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% | 35.0% | GPT-5.2 |
| GPQA Diamond (Epoch AI run) | 91.4% | 88.1% | GPT-5.2 |
| 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% | 33.0% | GPT-5.2 |
| Mystery game puzzles | 23.0% | 17.0% | GPT-5.2 |
| 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 | 1444 | Mistral Large 4 |
| LMArena Text: expert | 1445 | 1444 | GPT-5.2 |
| LMArena Text: hard prompts in English | 1431 | 1442 | Mistral Large 4 |
| LMArena Vision: diagrams | 1265 | 1288 | Mistral Large 4 |
| LMArena Vision: homework | 1276 | Not measured | Only GPT-5.2 measured |
| Kagi LLM Benchmark | 73.3% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 2 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | GPT-5.2 | Mistral Large 4 | 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% | 98.3% | Mistral Large 4 |
| FrontierMath, tiers 1 to 3 | 67.4% | 58.6% | GPT-5.2 |
| FrontierMath, tier 4 | 31.7% | 26.8% | GPT-5.2 |
| ProofBench | 15.0% | 10.0% | GPT-5.2 |
| LMArena Text: math | 1440 | 1469 | Mistral Large 4 |
| LMArena Text: mathematical work | 1442 | 1462 | Mistral Large 4 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | GPT-5.2 | Mistral Large 4 | 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% | 40.7% | GPT-5.2 |
| MMLU Pro (Artificial Analysis run) | 87.4% | Not measured | Only GPT-5.2 measured |
| SimpleQA Verified | 37.1% | 20.0% | GPT-5.2 |
| LMArena Text: science | 1425 | 1448 | Mistral Large 4 |
| LMArena Text: medicine and healthcare | 1425 | 1433 | Mistral Large 4 |
| LMArena Text: legal and government | 1427 | 1425 | GPT-5.2 |
| LMArena Text: business and finance | 1414 | 1439 | Mistral Large 4 |
| FACTS Benchmark Suite | 48.5% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | GPT-5.2 | Mistral Large 4 | Leader |
|---|---|---|---|
| Design Arena: websites | 1195 | 1257 | Mistral Large 4 |
| Design Arena: UI components | 1190 | 1272 | Mistral Large 4 |
| Design Arena: game dev | 1195 | 1225 | Mistral Large 4 |
| Design Arena: data viz | 1202 | 1275 | Mistral Large 4 |
| Design Arena: 3D | 1079 | 1252 | Mistral Large 4 |
| Design Arena: all code categories | 1170 | 1259 | Mistral Large 4 |
| Design Arena: SVG | 1139 | 1232 | Mistral Large 4 |
| Design Arena: ASCII art | 1152 | 1202 | Mistral Large 4 |
| LiveBench design | Not measured | 65.6% | Only Mistral Large 4 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index913
| Benchmark | GPT-5.2 | Mistral Large 4 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.5% | Only Mistral Large 4 measured |
| SAGE (Vals AI) | 49.3% | Not measured | Only GPT-5.2 measured |
| MedScribe (Vals AI) | 84.4% | 80.4% | GPT-5.2 |
| IFBench | 75.4% | Not measured | Only GPT-5.2 measured |
| Long context reasoning (AA-LCR) | 82.7% | 81.3% | GPT-5.2 |
| DTBench | 90.9% | 88.2% | GPT-5.2 |
| LMCA | 43.9% | 35.8% | GPT-5.2 |
| CL-bench | 18.2% | Not measured | Only GPT-5.2 measured |
| Surface Evolver Bench | Not measured | 28.1% | Only Mistral Large 4 measured |
| Furniture assembly | 38.3% | 20.8% | GPT-5.2 |
| LMArena Text | 1416 | 1431 | Mistral Large 4 |
| LMArena Text: instruction following | 1409 | 1422 | Mistral Large 4 |
| LMArena Text: creative writing | 1376 | 1363 | GPT-5.2 |
| LMArena Vision | 1246 | 1272 | Mistral Large 4 |
| LMArena Text: English | 1422 | 1433 | Mistral Large 4 |
| LMArena Text: non-English | 1405 | 1422 | Mistral Large 4 |
| LMArena Text: Chinese | 1447 | 1492 | Mistral Large 4 |
| LMArena Text: German | 1444 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Japanese | 1420 | 1415 | GPT-5.2 |
| LMArena Text: Korean | 1374 | Not measured | Only GPT-5.2 measured |
| LMArena Text: French | 1454 | 1492 | Mistral Large 4 |
| LMArena Text: Spanish | 1413 | 1455 | Mistral Large 4 |
| LMArena Text: Russian | 1415 | 1417 | Mistral Large 4 |
| LMArena Text: Polish | 1426 | Not measured | Only GPT-5.2 measured |
| LMArena Text: multi-turn | 1419 | 1435 | Mistral Large 4 |
| LMArena Text: longer queries | 1413 | 1428 | Mistral Large 4 |
| LMArena Text: writing and language | 1392 | 1386 | GPT-5.2 |
| LMArena Text: entertainment and media | 1378 | 1378 | GPT-5.2 |
| LMArena Vision: English | 1243 | 1283 | Mistral Large 4 |
| LMArena Vision: Chinese | 1305 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: reading text | 1257 | 1284 | Mistral Large 4 |
| LMArena Vision: humor | 1227 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: captioning | 1267 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: recognizing things | 1232 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: creative writing | 1225 | Not measured | Only GPT-5.2 measured |
| LMArena Document | 1405 | Not measured | Only GPT-5.2 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-5.2 | Mistral Large 4 | 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 Large 4 costs less
Repository review
50K input + 3K output tokens
Mistral Large 4 costs less
Agent loop
200K input + 10K output tokens
Mistral Large 4 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 Large 4 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | December 10, 2025 | October 6, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $1.36 |
| Output $/Mtok | $14.00 | $4.18 |
| Coding index | 55.9 | 56.4 |
| Independent measurements behind it | 3 | 5 |
| Cost per solved task | $1.91 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than Mistral Large 4 for coding?
GPT-5.2 and Mistral Large 4 are level for coding, 55.9 against 56.4 on the Vibe Coding Index, so price and context decide. Pick GPT-5.2 for work measured by ALE-Bench or Epoch Capabilities Index, where it leads. Pick Mistral Large 4 for output tokens 3.3x cheaper ($4.18 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
Which is cheaper, GPT-5.2 or Mistral Large 4?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Mistral Large 4 costs $1.36 and $4.18. Cost per solved task is known for GPT-5.2 only.
Which has the bigger context window, GPT-5.2 or Mistral Large 4?
Mistral Large 4, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.2 vs Mistral Large 4 comparison based on?
58 benchmarks have a result for both models as of October 22, 2026. GPT-5.2's coding index rests on 3 independent benchmarks and Mistral Large 4's on 5. Where only one model has a result, the page says so instead of naming a winner.