GPT-5.2 vs Mistral Medium 3.5
Updated October 21, 2026. 68 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-5.2 for coding: it scores 58.9 on the Vibe Coding Index against 9.2 for Mistral Medium 3.5.
Pick Mistral Medium 3.5 when output tokens cost 1.9x less, or when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 68
- GPT-5.2 only
- 43
- Mistral Medium 3.5 only
- 12
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
GPT-5.2
GPT-5.2 scores 58.9 against 9.2.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.2
GPT-5.2 leads, 30.4 against 14.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.5 has a AA Agentic Index result, 7.0.
Directional only
Reasoning
GPQA Diamond
GPT-5.2
GPT-5.2 leads, 91.7% against 34.9%.
Same board, both measured
Math
AIME
No call
Only GPT-5.2 has a AIME result, 96.9%.
Directional only
Knowledge
MMLU Pro
GPT-5.2
GPT-5.2 leads, 86.2% against 75.3%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.2
GPT-5.2 leads, 1196 against 1142.
Same board, both measured
Long documents
Listed context window
GPT-5.2
GPT-5.2 takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Medium 3.5
GPT-5.2 $0.0088, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.2
GPT-5.2 $1.91, Mistral Medium 3.5 $6.02.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.2 58.9 from 2 benchmarks, Mistral Medium 3.5 9.2 from 1 benchmark.
- Widest category
- Coding, 17.5 points apart on average over 8 shared benchmarks.
- Widest single gap
- GPQA Diamond: GPT-5.2 91.7% against Mistral Medium 3.5 34.9%, 56.8 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against Mistral Medium 3.5 $7.50 per million output tokens, 1.9x apart.
- Context window
- GPT-5.2 takes 400K tokens, Mistral Medium 3.5 262K.
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 Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 6 | 2 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 6 | 2 | ||
| Math | 3 | 0 | ||
| Knowledge | 7 | 3 | ||
| Web design | 8 | 0 | ||
| General | 19 | 9 | ||
| All shared boards | 51 | 17 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index62
| Benchmark | GPT-5.2 | Mistral Medium 3.5 | 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 | 1264 | GPT-5.2 |
| AA Coding Index | Not measured | 46.9 | Only Mistral Medium 3.5 measured |
| LiveCodeBench (Vals AI) | 85.4% | Not measured | Only GPT-5.2 measured |
| SWE-bench Verified (Vals AI) | 75.8% | 66.4% | GPT-5.2 |
| Vibe Code Bench (Vals AI) | 53.5% | 2.9% | GPT-5.2 |
| Code migration (Vals AI) | Not measured | 5.1% | Only Mistral Medium 3.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 39.0% | Only Mistral Medium 3.5 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GPT-5.2 measured |
| SciCode | Not measured | 40.2% | Only Mistral Medium 3.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | 33.3% | GPT-5.2 |
| 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 | 764.0 | GPT-5.2 |
| LMArena Text: coding | 1442 | 1460 | Mistral Medium 3.5 |
| LMArena Text: software and IT | 1434 | 1458 | Mistral Medium 3.5 |
| LMArena WebDev: React | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena WebDev: HTML | 1427 | 1308 | GPT-5.2 |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 14.2 | GPT-5.2 |
| Epoch Capabilities Index | 153.7 | 141.4 | GPT-5.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.2 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 7.0 | Only Mistral Medium 3.5 measured |
| 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 | 1094 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | 1064 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2 measured |
| Finance Agent (Vals AI) | Not measured | 32.1% | Only Mistral Medium 3.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 4.5% | Only Mistral Medium 3.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 9.1% | Only Mistral Medium 3.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 11.2% | Only Mistral Medium 3.5 measured |
| τ²-Bench Telecom | 84.8% | 94.2% | Mistral Medium 3.5 |
| 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 |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index62
| Benchmark | GPT-5.2 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | 34.9% | GPT-5.2 |
| MMMU Pro (Vals AI) | 86.7% | Not measured | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 90.3% | 74.8% | GPT-5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 37.7% | 13.8% | GPT-5.2 |
| 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 | 1436 | Mistral Medium 3.5 |
| LMArena Text: expert | 1445 | 1432 | GPT-5.2 |
| LMArena Text: hard prompts in English | 1431 | 1445 | Mistral Medium 3.5 |
| LMArena Vision: diagrams | 1265 | 1254 | GPT-5.2 |
| LMArena Vision: homework | 1276 | 1230 | GPT-5.2 |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.2 | Mistral Medium 3.5 | 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% | 9.0% | GPT-5.2 |
| LMArena Text: math | 1440 | 1431 | GPT-5.2 |
| LMArena Text: mathematical work | 1442 | 1440 | 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 index73
| Benchmark | GPT-5.2 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | 75.3% | GPT-5.2 |
| LegalBench (Vals AI) | 82.8% | Not measured | Only GPT-5.2 measured |
| CaseLaw (Vals AI) | 66.0% | 44.2% | GPT-5.2 |
| CorpFin (Vals AI) | 65.9% | 58.8% | GPT-5.2 |
| TaxEval (Vals AI) | 75.8% | 68.0% | GPT-5.2 |
| MortgageTax (Vals AI) | 67.1% | 28.9% | GPT-5.2 |
| MedQA (Vals AI) | 94.1% | Not measured | Only GPT-5.2 measured |
| MedCode (Vals AI) | 49.8% | 33.8% | GPT-5.2 |
| 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 | 1428 | Mistral Medium 3.5 |
| LMArena Text: medicine and healthcare | 1425 | 1409 | GPT-5.2 |
| LMArena Text: legal and government | 1427 | 1437 | Mistral Medium 3.5 |
| LMArena Text: business and finance | 1414 | 1426 | Mistral Medium 3.5 |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-5.2 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1196 | 1142 | GPT-5.2 |
| Design Arena: UI components | 1190 | 1079 | GPT-5.2 |
| Design Arena: game dev | 1196 | 1079 | GPT-5.2 |
| Design Arena: data viz | 1202 | 1120 | GPT-5.2 |
| Design Arena: 3D | 1079 | 1061 | GPT-5.2 |
| Design Arena: all code categories | 1171 | 1122 | GPT-5.2 |
| Design Arena: SVG | 1137 | 949 | GPT-5.2 |
| Design Arena: ASCII art | 1153 | 1062 | GPT-5.2 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index199
| Benchmark | GPT-5.2 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 49.3% | 37.6% | GPT-5.2 |
| MedScribe (Vals AI) | 84.4% | 67.7% | GPT-5.2 |
| Vals Multimodal Index | Not measured | 34.8% | Only Mistral Medium 3.5 measured |
| IFBench | 75.4% | 68.8% | GPT-5.2 |
| Long context reasoning (AA-LCR) | 82.7% | 69.3% | GPT-5.2 |
| DTBench | 90.9% | 75.5% | GPT-5.2 |
| LMCA | 43.9% | 26.1% | GPT-5.2 |
| CL-bench | 18.2% | Not measured | Only GPT-5.2 measured |
| Surface Evolver Bench | Not measured | 26.9% | Only Mistral Medium 3.5 measured |
| Furniture assembly | 38.3% | Not measured | Only GPT-5.2 measured |
| LMArena Text | 1416 | 1421 | Mistral Medium 3.5 |
| LMArena Text: instruction following | 1409 | 1415 | Mistral Medium 3.5 |
| LMArena Text: creative writing | 1376 | 1374 | GPT-5.2 |
| LMArena Vision | 1246 | 1222 | GPT-5.2 |
| LMArena Text: English | 1422 | 1433 | Mistral Medium 3.5 |
| LMArena Text: non-English | 1405 | 1404 | GPT-5.2 |
| LMArena Text: Chinese | 1447 | 1444 | GPT-5.2 |
| LMArena Text: German | 1444 | 1453 | Mistral Medium 3.5 |
| LMArena Text: Japanese | 1420 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Korean | 1374 | 1384 | Mistral Medium 3.5 |
| LMArena Text: French | 1454 | 1448 | GPT-5.2 |
| LMArena Text: Spanish | 1413 | 1411 | GPT-5.2 |
| LMArena Text: Russian | 1415 | 1395 | GPT-5.2 |
| LMArena Text: Polish | 1426 | 1430 | Mistral Medium 3.5 |
| LMArena Text: multi-turn | 1419 | 1423 | Mistral Medium 3.5 |
| LMArena Text: longer queries | 1413 | 1415 | Mistral Medium 3.5 |
| LMArena Text: writing and language | 1392 | 1384 | GPT-5.2 |
| LMArena Text: entertainment and media | 1378 | 1382 | Mistral Medium 3.5 |
| LMArena Vision: English | 1243 | 1240 | GPT-5.2 |
| LMArena Vision: Chinese | 1305 | 1244 | GPT-5.2 |
| LMArena Vision: reading text | 1257 | 1233 | GPT-5.2 |
| LMArena Vision: humor | 1227 | 1216 | GPT-5.2 |
| 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 | 1198 | GPT-5.2 |
| 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 Medium 3.5 | 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 3.5 costs less
Repository review
50K input + 3K output tokens
Mistral Medium 3.5 costs less
Agent loop
200K input + 10K output tokens
Mistral Medium 3.5 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 Medium 3.5 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | December 10, 2025 | April 30, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.75 | $1.50 |
| Output $/Mtok | $14.00 | $7.50 |
| Coding index | 58.9 | 9.2 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $1.91 | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than Mistral Medium 3.5 for coding?
Pick GPT-5.2 for coding: it scores 58.9 on the Vibe Coding Index against 9.2 for Mistral Medium 3.5. Pick Mistral Medium 3.5 when output tokens cost 1.9x less, or when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GPT-5.2 or Mistral Medium 3.5?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Per solved SWE-bench Verified task, GPT-5.2 costs $1.91 and Mistral Medium 3.5 $6.02.
Which has the bigger context window, GPT-5.2 or Mistral Medium 3.5?
GPT-5.2, with 400K tokens against 262K.
How many benchmarks is the GPT-5.2 vs Mistral Medium 3.5 comparison based on?
68 benchmarks have a result for both models as of October 21, 2026. GPT-5.2's coding index rests on 2 independent benchmarks and Mistral Medium 3.5's on 1. Where only one model has a result, the page says so instead of naming a winner.