GPT-4o (2024-05-13) vs Mistral Large
GPT-4o (2024-05-13) leads Mistral Large on the Artificial Analysis Intelligence Index, 7.3 to 5.8, and Mistral Large costs 2.5x less per output token ($6.00 vs $15.00 per million). Updated October 22, 2026; 35 benchmarks have a result for both.
GPT-4o (2024-05-13) or Mistral Large: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o (2024-05-13) for work measured by Humanity's Last Exam (Artificial Analysis run) or LMArena Text: French, where it leads.
- Shared results
- 35
- GPT-4o (2024-05-13) only
- 9
- Mistral Large only
- 9
- Like-for-like categories
- 6 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
GPT-4o (2024-05-13)
GPT-4o (2024-05-13) leads, 7.3 against 5.8.
Same board, both measured
Reasoning
GPQA Diamond
GPT-4o (2024-05-13)
GPT-4o (2024-05-13) leads, 50.2% against 47.7%.
Same board, both measured
Math
AIME
No call
Only Mistral Large has a AIME result, 9.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Large has a MMLU Pro result, 69.7%.
Directional only
Long documents
Listed context window
No call
Both take 128K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large
GPT-4o (2024-05-13) $0.013, Mistral Large $0.0050 per turn.
List prices
At a glance
- Coding index
- GPT-4o (2024-05-13) not benchmarked, Mistral Large not benchmarked.
- Widest category
- Knowledge, 9.7 points apart on average over 6 shared benchmarks.
- Widest single gap
- LMArena Text: Polish: GPT-4o (2024-05-13) 1365 against Mistral Large 1157, 34.6 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-05-13) $15.00 against Mistral Large $6.00 per million output tokens, 2.5x apart.
- Context window
- Both take 128K tokens.
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 (2024-05-13) | Bars | Mistral Large | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 5 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 6 | 0 | ||
| General | 16 | 1 | ||
| All shared boards | 33 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GPT-4o (2024-05-13) | Mistral Large | Leader |
|---|---|---|---|
| AA Coding Index | 24.2 | Not measured | Only GPT-4o (2024-05-13) measured |
| LiveCodeBench (Vals AI) | Not measured | 37.1% | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | 33.4% | 17.8% | GPT-4o (2024-05-13) |
| LMArena Text: coding | 1297 | 1275 | GPT-4o (2024-05-13) |
| LMArena Text: software and IT | 1297 | 1274 | GPT-4o (2024-05-13) |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4o (2024-05-13) | Mistral Large | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | 5.8 | GPT-4o (2024-05-13) |
| Epoch Capabilities Index | Not measured | 122.1 | Only Mistral Large measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Mistral Large | Leader |
|---|---|---|---|
| METR time horizons | 40.8% | Not measured | Only GPT-4o (2024-05-13) measured |
| BALROG | 32.3% | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | GPT-4o (2024-05-13) | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | 50.2% | 47.7% | GPT-4o (2024-05-13) |
| GPQA Diamond (Artificial Analysis run) | 52.6% | 35.1% | GPT-4o (2024-05-13) |
| Humanity's Last Exam (Artificial Analysis run) | 1.8% | 3.5% | Mistral Large |
| GPQA Diamond (Epoch AI run) | 48.9% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts | 1281 | 1257 | GPT-4o (2024-05-13) |
| LMArena Text: expert | 1250 | 1208 | GPT-4o (2024-05-13) |
| LMArena Text: hard prompts in English | 1293 | 1268 | GPT-4o (2024-05-13) |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4o (2024-05-13) | Mistral Large | Leader |
|---|---|---|---|
| AIME | Not measured | 9.2% | Only Mistral Large measured |
| MATH 500 (Vals AI) | Not measured | 74.4% | Only Mistral Large measured |
| MGSM (Vals AI) | Not measured | 87.2% | Only Mistral Large measured |
| OTIS Mock AIME | 6.3% | Not measured | Only GPT-4o (2024-05-13) measured |
| MATH level 5 | 51.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: math | 1284 | 1261 | GPT-4o (2024-05-13) |
| LMArena Text: mathematical work | 1284 | 1257 | GPT-4o (2024-05-13) |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-4o (2024-05-13) | Mistral Large | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 69.7% | Only Mistral Large measured |
| TaxEval (Vals AI) | Not measured | 63.8% | Only Mistral Large measured |
| MedQA (Vals AI) | Not measured | 76.2% | Only Mistral Large measured |
| MMLU Pro (Artificial Analysis run) | 74.0% | 51.5% | GPT-4o (2024-05-13) |
| MMLU | 84.2% | 68.8% | GPT-4o (2024-05-13) |
| LMArena Text: science | 1295 | 1260 | GPT-4o (2024-05-13) |
| LMArena Text: medicine and healthcare | 1278 | 1255 | GPT-4o (2024-05-13) |
| LMArena Text: legal and government | 1314 | 1275 | GPT-4o (2024-05-13) |
| LMArena Text: business and finance | 1262 | 1238 | GPT-4o (2024-05-13) |
| Neither model has a result on the other 9 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Mistral Large | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index161
| Benchmark | GPT-4o (2024-05-13) | Mistral Large | Leader |
|---|---|---|---|
| DTBench | Not measured | 60.8% | Only Mistral Large measured |
| LMArena Text | 1300 | 1265 | GPT-4o (2024-05-13) |
| LMArena Text: instruction following | 1278 | 1249 | GPT-4o (2024-05-13) |
| LMArena Text: creative writing | 1291 | 1242 | GPT-4o (2024-05-13) |
| LMArena Vision | 1137 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: English | 1312 | 1284 | GPT-4o (2024-05-13) |
| LMArena Text: non-English | 1283 | 1235 | GPT-4o (2024-05-13) |
| LMArena Text: Chinese | 1277 | 1239 | GPT-4o (2024-05-13) |
| LMArena Text: German | 1283 | 1242 | GPT-4o (2024-05-13) |
| LMArena Text: Japanese | 1257 | 1170 | GPT-4o (2024-05-13) |
| LMArena Text: Korean | 1234 | 1202 | GPT-4o (2024-05-13) |
| LMArena Text: French | 1304 | 1325 | Mistral Large |
| LMArena Text: Spanish | 1292 | 1268 | GPT-4o (2024-05-13) |
| LMArena Text: Russian | 1285 | 1252 | GPT-4o (2024-05-13) |
| LMArena Text: Polish | 1365 | 1157 | GPT-4o (2024-05-13) |
| LMArena Text: multi-turn | 1302 | 1260 | GPT-4o (2024-05-13) |
| LMArena Text: longer queries | 1288 | 1261 | GPT-4o (2024-05-13) |
| LMArena Text: writing and language | 1298 | 1261 | GPT-4o (2024-05-13) |
| LMArena Text: entertainment and media | 1289 | 1230 | GPT-4o (2024-05-13) |
| LMArena Vision: English | 1147 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: Chinese | 1132 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Mistral Large | 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 costs less
Repository review
50K input + 3K output tokens
Mistral Large costs less
Agent loop
200K input + 10K output tokens
Mistral Large 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 (2024-05-13) | Mistral Large |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | May 13, 2024 | February 26, 2024 |
| Context window | 128K | 128K |
| Input $/Mtok | $5.00 | $2.00 |
| Output $/Mtok | $15.00 | $6.00 |
| 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 (2024-05-13) better than Mistral Large for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o (2024-05-13) for work measured by Humanity's Last Exam (Artificial Analysis run) or LMArena Text: French, where it leads.
Which is cheaper, GPT-4o (2024-05-13) or Mistral Large?
GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 per million output tokens; Mistral Large costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o (2024-05-13) or Mistral Large?
Neither: both take 128K tokens.
How many benchmarks is the GPT-4o (2024-05-13) vs Mistral Large comparison based on?
35 benchmarks have a result for both models as of October 22, 2026. GPT-4o (2024-05-13)'s coding index rests on 0 independent benchmarks and Mistral Large's on 0. Where only one model has a result, the page says so instead of naming a winner.