GPT-4o (2024-11-20) vs Mistral Medium 3.1
Mistral Medium 3.1 leads GPT-4o (2024-11-20) on the Artificial Analysis Intelligence Index, 9.2 to 8.4, and Mistral Medium 3.1 costs 5.0x less per output token ($2.00 vs $10.00 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
GPT-4o (2024-11-20) or Mistral Medium 3.1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o (2024-11-20) for work measured by LiveCodeBench (Vals AI) or AA Intelligence Index, where it leads.
- Shared results
- 11
- GPT-4o (2024-11-20) only
- 13
- Mistral Medium 3.1 only
- 63
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Mistral Medium 3.1
Mistral Medium 3.1 leads, 9.2 against 8.4.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.1 has a AA Agentic Index result, 1.9.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-4o (2024-11-20) has a GPQA Diamond result, 53.8%.
Directional only
Math
AIME
Mistral Medium 3.1
Mistral Medium 3.1 leads, 42.3% against 11.9%.
Same board, both measured
Knowledge
MMLU Pro
Mistral Medium 3.1
Mistral Medium 3.1 leads, 75.3% against 72.6%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Mistral Medium 3.1 has a Design Arena: websites result, 1132.
Directional only
Long documents
Listed context window
Mistral Medium 3.1
Mistral Medium 3.1 takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Medium 3.1
GPT-4o (2024-11-20) $0.0075, Mistral Medium 3.1 $0.0014 per turn.
List prices
At a glance
- Coding index
- GPT-4o (2024-11-20) not benchmarked, Mistral Medium 3.1 not benchmarked.
- Widest category
- Math, 14.6 points apart on average over 3 shared benchmarks.
- Widest single gap
- AIME: GPT-4o (2024-11-20) 11.9% against Mistral Medium 3.1 42.3%, 30.4 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-11-20) $10.00 against Mistral Medium 3.1 $2.00 per million output tokens, 5.0x apart.
- Context window
- GPT-4o (2024-11-20) takes 128K tokens, Mistral Medium 3.1 131K.
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-4o (2024-11-20) | Bars | Mistral Medium 3.1 | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Math | 0 | 3 | ||
| Knowledge | 3 | 2 | ||
| All shared boards | 3 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4o (2024-11-20) | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| Aider polyglot | 18.2% | Not measured | Only GPT-4o (2024-11-20) measured |
| AA Coding Index | Not measured | 20.5 | Only Mistral Medium 3.1 measured |
| LiveCodeBench (Vals AI) | 43.4% | 44.8% | Mistral Medium 3.1 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 40.6% | Only Mistral Medium 3.1 measured |
| SciCode | Not measured | 32.4% | Only Mistral Medium 3.1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 10.6% | Only Mistral Medium 3.1 measured |
| GSO-Bench | 0.0% | Not measured | Only GPT-4o (2024-11-20) measured |
| WeirdML | 25.1% | Not measured | Only GPT-4o (2024-11-20) measured |
| Cybench | 12.5% | Not measured | Only GPT-4o (2024-11-20) measured |
| ALE-Bench | Not measured | 210.2 | Only Mistral Medium 3.1 measured |
| LMArena Text: coding | Not measured | 1433 | Only Mistral Medium 3.1 measured |
| LMArena Text: software and IT | Not measured | 1436 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o (2024-11-20) | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 8.4 | 9.2 | Mistral Medium 3.1 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.9 | Only Mistral Medium 3.1 measured |
| τ²-Bench Telecom | Not measured | 40.6% | Only Mistral Medium 3.1 measured |
| TheAgentCompany | 8.6% | Not measured | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o (2024-11-20) | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| GPQA Diamond | 53.8% | Not measured | Only GPT-4o (2024-11-20) measured |
| MMMU Pro (Vals AI) | 62.2% | 63.0% | Mistral Medium 3.1 |
| GPQA Diamond (Artificial Analysis run) | Not measured | 58.8% | Only Mistral Medium 3.1 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.7% | Only Mistral Medium 3.1 measured |
| GPQA Diamond (Epoch AI run) | 47.9% | Not measured | Only GPT-4o (2024-11-20) measured |
| VPCT | 40.0% | Not measured | Only GPT-4o (2024-11-20) measured |
| LMArena Text: hard prompts | Not measured | 1426 | Only Mistral Medium 3.1 measured |
| LMArena Text: expert | Not measured | 1408 | Only Mistral Medium 3.1 measured |
| LMArena Text: hard prompts in English | Not measured | 1435 | Only Mistral Medium 3.1 measured |
| LMArena Vision: diagrams | Not measured | 1175 | Only Mistral Medium 3.1 measured |
| LMArena Vision: homework | Not measured | 1165 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o (2024-11-20) | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AIME | 11.9% | 42.3% | Mistral Medium 3.1 |
| MATH 500 (Vals AI) | 74.0% | 87.0% | Mistral Medium 3.1 |
| MGSM (Vals AI) | 90.4% | 90.9% | Mistral Medium 3.1 |
| AA Math Index | Not measured | 38.3 | Only Mistral Medium 3.1 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 38.3% | Only Mistral Medium 3.1 measured |
| OTIS Mock AIME | 6.3% | Not measured | Only GPT-4o (2024-11-20) measured |
| MATH level 5 | 49.8% | Not measured | Only GPT-4o (2024-11-20) measured |
| LMArena Text: math | Not measured | 1408 | Only Mistral Medium 3.1 measured |
| LMArena Text: mathematical work | Not measured | 1412 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | GPT-4o (2024-11-20) | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| MMLU Pro | 72.6% | 75.3% | Mistral Medium 3.1 |
| LegalBench (Vals AI) | 82.2% | 62.0% | GPT-4o (2024-11-20) |
| CaseLaw (Vals AI) | 59.7% | Not measured | Only GPT-4o (2024-11-20) measured |
| CorpFin (Vals AI) | 45.9% | 50.7% | Mistral Medium 3.1 |
| TaxEval (Vals AI) | 74.5% | 70.3% | GPT-4o (2024-11-20) |
| MortgageTax (Vals AI) | 57.4% | 36.5% | GPT-4o (2024-11-20) |
| MedQA (Vals AI) | Not measured | 78.2% | Only Mistral Medium 3.1 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 68.3% | Only Mistral Medium 3.1 measured |
| GeoBench | 71.0% | Not measured | Only GPT-4o (2024-11-20) measured |
| LMArena Text: science | Not measured | 1444 | Only Mistral Medium 3.1 measured |
| LMArena Text: medicine and healthcare | Not measured | 1449 | Only Mistral Medium 3.1 measured |
| LMArena Text: legal and government | Not measured | 1439 | Only Mistral Medium 3.1 measured |
| LMArena Text: business and finance | Not measured | 1418 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1132 | Only Mistral Medium 3.1 measured |
| Design Arena: UI components | Not measured | 1097 | Only Mistral Medium 3.1 measured |
| Design Arena: game dev | Not measured | 1073 | Only Mistral Medium 3.1 measured |
| Design Arena: data viz | Not measured | 1144 | Only Mistral Medium 3.1 measured |
| Design Arena: 3D | Not measured | 1081 | Only Mistral Medium 3.1 measured |
| Design Arena: all code categories | Not measured | 1120 | Only Mistral Medium 3.1 measured |
| Design Arena: SVG | Not measured | 992 | Only Mistral Medium 3.1 measured |
| Design Arena: ASCII art | Not measured | 997 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index30 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| IFBench | Not measured | 39.8% | Only Mistral Medium 3.1 measured |
| Long context reasoning (AA-LCR) | Not measured | 42.7% | Only Mistral Medium 3.1 measured |
| DTBench | Not measured | 67.5% | Only Mistral Medium 3.1 measured |
| LMCA | Not measured | 19.1% | Only Mistral Medium 3.1 measured |
| Creative writing (Lech Mazur) | 81.8% | Not measured | Only GPT-4o (2024-11-20) measured |
| LMArena Text | Not measured | 1424 | Only Mistral Medium 3.1 measured |
| LMArena Text: instruction following | Not measured | 1398 | Only Mistral Medium 3.1 measured |
| LMArena Text: creative writing | Not measured | 1391 | Only Mistral Medium 3.1 measured |
| LMArena Vision | Not measured | 1172 | Only Mistral Medium 3.1 measured |
| LMArena Text: English | Not measured | 1438 | Only Mistral Medium 3.1 measured |
| LMArena Text: non-English | Not measured | 1408 | Only Mistral Medium 3.1 measured |
| LMArena Text: Chinese | Not measured | 1447 | Only Mistral Medium 3.1 measured |
| LMArena Text: German | Not measured | 1432 | Only Mistral Medium 3.1 measured |
| LMArena Text: Japanese | Not measured | 1379 | Only Mistral Medium 3.1 measured |
| LMArena Text: Korean | Not measured | 1380 | Only Mistral Medium 3.1 measured |
| LMArena Text: French | Not measured | 1460 | Only Mistral Medium 3.1 measured |
| LMArena Text: Spanish | Not measured | 1433 | Only Mistral Medium 3.1 measured |
| LMArena Text: Russian | Not measured | 1411 | Only Mistral Medium 3.1 measured |
| LMArena Text: Polish | Not measured | 1430 | Only Mistral Medium 3.1 measured |
| LMArena Text: multi-turn | Not measured | 1418 | Only Mistral Medium 3.1 measured |
| LMArena Text: longer queries | Not measured | 1406 | Only Mistral Medium 3.1 measured |
| LMArena Text: writing and language | Not measured | 1395 | Only Mistral Medium 3.1 measured |
| LMArena Text: entertainment and media | Not measured | 1390 | Only Mistral Medium 3.1 measured |
| LMArena Vision: English | Not measured | 1179 | Only Mistral Medium 3.1 measured |
| LMArena Vision: Chinese | Not measured | 1190 | Only Mistral Medium 3.1 measured |
| LMArena Vision: reading text | Not measured | 1175 | Only Mistral Medium 3.1 measured |
| LMArena Vision: humor | Not measured | 1168 | Only Mistral Medium 3.1 measured |
| LMArena Vision: captioning | Not measured | 1145 | Only Mistral Medium 3.1 measured |
| LMArena Vision: recognizing things | Not measured | 1147 | Only Mistral Medium 3.1 measured |
| LMArena Vision: creative writing | Not measured | 1200 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | Mistral Medium 3.1 | 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 3.1 costs less
Repository review
50K input + 3K output tokens
Mistral Medium 3.1 costs less
Agent loop
200K input + 10K output tokens
Mistral Medium 3.1 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-4o (2024-11-20) | Mistral Medium 3.1 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | November 20, 2024 | August 13, 2025 |
| Context window | 128K | 131K |
| Input $/Mtok | $2.50 | $0.40 |
| Output $/Mtok | $10.00 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o (2024-11-20) better than Mistral Medium 3.1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o (2024-11-20) for work measured by LiveCodeBench (Vals AI) or AA Intelligence Index, where it leads.
Which is cheaper, GPT-4o (2024-11-20) or Mistral Medium 3.1?
GPT-4o (2024-11-20) costs $2.50 per million input tokens and $10.00 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o (2024-11-20) or Mistral Medium 3.1?
Mistral Medium 3.1, with 131K tokens against 128K.
How many benchmarks is the GPT-4o (2024-11-20) vs Mistral Medium 3.1 comparison based on?
11 benchmarks have a result for both models as of October 22, 2026. GPT-4o (2024-11-20)'s coding index rests on 0 independent benchmarks and Mistral Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.