Mistral Large vs o3 Pro
Updated October 21, 2026. 4 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Mistral Large has the edge where output tokens cost 13.3x less, and where what matters is AA Intelligence Index or Epoch Capabilities Index, where it leads.
- Shared results
- 4
- Mistral Large only
- 40
- o3 Pro only
- 7
- Like-for-like categories
- 3 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, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
o3 Pro
o3 Pro leads, 21.9 against 5.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Mistral Large has a GPQA Diamond result, 47.7%.
Directional only
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
o3 Pro
o3 Pro takes 200K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large
Mistral Large $0.0050, o3 Pro $0.060 per turn.
List prices
At a glance
- Coding index
- Mistral Large not benchmarked, o3 Pro not benchmarked.
- Widest category
- Reasoning, 49.4 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Mistral Large 35.1% against o3 Pro 84.5%, 49.4 points apart on a 0 to 100 scale.
- Output price
- Mistral Large $6.00 against o3 Pro $80.00 per million output tokens, 13.3x apart.
- Context window
- Mistral Large takes 128K tokens, o3 Pro 200K.
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 | Mistral Large | Bars | o3 Pro | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 4 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards, none shared
| Benchmark | Mistral Large | o3 Pro | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 84.9% | Only o3 Pro measured |
| LiveCodeBench (Vals AI) | 37.1% | Not measured | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | 17.8% | Not measured | Only Mistral Large measured |
| WeirdML | Not measured | 58.2% | Only o3 Pro measured |
| LMArena Text: coding | 1275 | Not measured | Only Mistral Large measured |
| LMArena Text: software and IT | 1274 | Not measured | Only Mistral Large measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large | o3 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.8 | 21.9 | o3 Pro |
| Epoch Capabilities Index | 122.1 | 147.3 | o3 Pro |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | o3 Pro | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Large | o3 Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 47.7% | Not measured | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | 35.1% | 84.5% | o3 Pro |
| Humanity's Last Exam (Artificial Analysis run) | 3.5% | Not measured | Only Mistral Large measured |
| ARC-AGI | Not measured | 59.3% | Only o3 Pro measured |
| ARC-AGI-2 | Not measured | 4.9% | Only o3 Pro measured |
| LMArena Text: hard prompts | 1257 | Not measured | Only Mistral Large measured |
| LMArena Text: expert | 1208 | Not measured | Only Mistral Large measured |
| LMArena Text: hard prompts in English | 1268 | Not measured | Only Mistral Large measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Mistral Large | o3 Pro | Leader |
|---|---|---|---|
| AIME | 9.2% | Not measured | Only Mistral Large measured |
| MATH 500 (Vals AI) | 74.4% | Not measured | Only Mistral Large measured |
| MGSM (Vals AI) | 87.2% | Not measured | Only Mistral Large measured |
| LMArena Text: math | 1261 | Not measured | Only Mistral Large measured |
| LMArena Text: mathematical work | 1257 | Not measured | Only Mistral Large measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Mistral Large | o3 Pro | Leader |
|---|---|---|---|
| MMLU Pro | 69.7% | Not measured | Only Mistral Large measured |
| TaxEval (Vals AI) | 63.8% | Not measured | Only Mistral Large measured |
| MedQA (Vals AI) | 76.2% | Not measured | Only Mistral Large measured |
| MMLU Pro (Artificial Analysis run) | 51.5% | Not measured | Only Mistral Large measured |
| MMLU | 68.8% | Not measured | Only Mistral Large measured |
| LMArena Text: science | 1260 | Not measured | Only Mistral Large measured |
| LMArena Text: medicine and healthcare | 1255 | Not measured | Only Mistral Large measured |
| LMArena Text: legal and government | 1275 | Not measured | Only Mistral Large measured |
| LMArena Text: business and finance | 1238 | Not measured | Only Mistral Large measured |
| Neither model has a result on the other 8 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | o3 Pro | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Large | o3 Pro | Leader |
|---|---|---|---|
| DTBench | 60.8% | 86.9% | o3 Pro |
| LMCA | Not measured | 38.5% | Only o3 Pro measured |
| Fiction.LiveBench | Not measured | 97.2% | Only o3 Pro measured |
| Creative writing (Lech Mazur) | Not measured | 84.4% | Only o3 Pro measured |
| LMArena Text | 1265 | Not measured | Only Mistral Large measured |
| LMArena Text: instruction following | 1249 | Not measured | Only Mistral Large measured |
| LMArena Text: creative writing | 1242 | Not measured | Only Mistral Large measured |
| LMArena Text: English | 1284 | Not measured | Only Mistral Large measured |
| LMArena Text: non-English | 1235 | Not measured | Only Mistral Large measured |
| LMArena Text: Chinese | 1239 | Not measured | Only Mistral Large measured |
| LMArena Text: German | 1242 | Not measured | Only Mistral Large measured |
| LMArena Text: Japanese | 1170 | Not measured | Only Mistral Large measured |
| LMArena Text: Korean | 1202 | Not measured | Only Mistral Large measured |
| LMArena Text: French | 1325 | Not measured | Only Mistral Large measured |
| LMArena Text: Spanish | 1268 | Not measured | Only Mistral Large measured |
| LMArena Text: Russian | 1252 | Not measured | Only Mistral Large measured |
| LMArena Text: Polish | 1157 | Not measured | Only Mistral Large measured |
| LMArena Text: multi-turn | 1260 | Not measured | Only Mistral Large measured |
| LMArena Text: longer queries | 1261 | Not measured | Only Mistral Large measured |
| LMArena Text: writing and language | 1261 | Not measured | Only Mistral Large measured |
| LMArena Text: entertainment and media | 1230 | Not measured | Only Mistral Large 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 | Mistral Large | o3 Pro | 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 | Mistral Large | OpenAI o3 Pro |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | February 26, 2024 | June 10, 2025 |
| Context window | 128K | 200K |
| Input $/Mtok | $2.00 | $20.00 |
| Output $/Mtok | $6.00 | $80.00 |
| 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 Mistral Large better than o3 Pro for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Mistral Large has the edge where output tokens cost 13.3x less, and where what matters is AA Intelligence Index or Epoch Capabilities Index, where it leads.
Which is cheaper, Mistral Large or o3 Pro?
Mistral Large costs $2.00 per million input tokens and $6.00 per million output tokens; o3 Pro costs $20.00 and $80.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large or o3 Pro?
o3 Pro, with 200K tokens against 128K.
How many benchmarks is the Mistral Large vs o3 Pro comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. Mistral Large's coding index rests on 0 independent benchmarks and o3 Pro's on 0. Where only one model has a result, the page says so instead of naming a winner.