Mistral Large 3 2512 vs o3
o3 leads Mistral Large 3 2512 on the Artificial Analysis Intelligence Index, 20.2 to 9.3, and Mistral Large 3 2512 costs 5.3x less per output token ($1.50 vs $8.00 per million). Updated October 21, 2026; 21 benchmarks have a result for both.
Mistral Large 3 2512 or o3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mistral Large 3 2512 for output tokens 5.3x cheaper ($1.50 vs $8.00 per million) and a larger context window (262K against 200K tokens).
- Shared results
- 21
- Mistral Large 3 2512 only
- 7
- o3 only
- 81
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
o3
o3 leads, 20.2 against 9.3.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Large 3 2512 has a AA Agentic Index result, 1.0.
Directional only
Reasoning
GPQA Diamond
o3
o3 leads, 84.1% against 68.4%.
Same board, both measured
Math
AIME
o3
o3 leads, 85.3% against 42.9%.
Same board, both measured
Knowledge
MMLU Pro
o3
o3 leads, 85.6% against 79.8%.
Same board, both measured
Web design
Design Arena: websites
Mistral Large 3 2512
Mistral Large 3 2512 leads, 1162 against 1036.
Same board, both measured
Long documents
Listed context window
Mistral Large 3 2512
Mistral Large 3 2512 takes 262K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 3 2512
Mistral Large 3 2512 $0.0013, o3 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
o3
Mistral Large 3 2512 $0.77, o3 $0.571.
Published attempt cost and pass rate
At a glance
- Coding index
- Mistral Large 3 2512 not benchmarked, o3 not benchmarked.
- Widest category
- Coding, 25.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- AIME: Mistral Large 3 2512 42.9% against o3 85.3%, 42.4 points apart on a 0 to 100 scale.
- Output price
- Mistral Large 3 2512 $1.50 against o3 $8.00 per million output tokens, 5.3x apart.
- Context window
- Mistral Large 3 2512 takes 262K tokens, o3 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 3 2512 | Bars | o3 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 5 | ||
| Web design | 4 | 1 | ||
| General | 0 | 3 | ||
| All shared boards | 5 | 16 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Mistral Large 3 2512 | o3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 81.3% | Only o3 measured |
| SWE-bench Verified | Not measured | 58.4% | Only o3 measured |
| SWE-bench Multimodal | Not measured | 36.0% | Only o3 measured |
| AA Coding Index | 20.1 | Not measured | Only Mistral Large 3 2512 measured |
| LiveCodeBench (Vals AI) | 55.3% | 83.9% | o3 |
| SWE-bench Verified (Vals AI) | 41.4% | Not measured | Only Mistral Large 3 2512 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 80.8% | Only o3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 37.1% | Only o3 measured |
| GSO-Bench | Not measured | 8.8% | Only o3 measured |
| WeirdML | Not measured | 52.4% | Only o3 measured |
| ALE-Bench | 264.7 | 933.5 | o3 |
| LMArena Text: coding | Not measured | 1407 | Only o3 measured |
| LMArena Text: software and IT | Not measured | 1415 | Only o3 measured |
| SciCode | Not measured | 9.2% | Only o3 measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Large 3 2512 | o3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.3 | 20.2 | o3 |
| Epoch Capabilities Index | Not measured | 146.7 | Only o3 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Mistral Large 3 2512 | o3 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Mistral Large 3 2512 measured |
| τ²-Bench Telecom | Not measured | 80.7% | Only o3 measured |
| METR time horizons | Not measured | 65.4% | Only o3 measured |
| GDPval | Not measured | 30.8% | Only o3 measured |
| DeepResearch Bench | Not measured | 45.2% | Only o3 measured |
| OSWorld | Not measured | 23.0% | Only o3 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 63.0% | Only o3 measured |
| BrowseComp-Plus | Not measured | 65.9% | Only o3 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large 3 2512 | o3 | Leader |
|---|---|---|---|
| GPQA Diamond | 68.4% | 84.1% | o3 |
| MMMU Pro (Vals AI) | 66.2% | 80.4% | o3 |
| GPQA Diamond (Artificial Analysis run) | Not measured | 82.7% | Only o3 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 20.1% | Only o3 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 81.8% | Only o3 measured |
| ARC-AGI | Not measured | 60.8% | Only o3 measured |
| ARC-AGI-2 | Not measured | 6.5% | Only o3 measured |
| SimpleBench | Not measured | 53.1% | Only o3 measured |
| Chess puzzles | Not measured | 37.0% | Only o3 measured |
| Mystery game puzzles | Not measured | 29.0% | Only o3 measured |
| VPCT | Not measured | 52.0% | Only o3 measured |
| LMArena Text: hard prompts | Not measured | 1402 | Only o3 measured |
| LMArena Text: expert | Not measured | 1402 | Only o3 measured |
| LMArena Text: hard prompts in English | Not measured | 1411 | Only o3 measured |
| LMArena Vision: diagrams | Not measured | 1220 | Only o3 measured |
| LMArena Vision: homework | Not measured | 1234 | Only o3 measured |
| Kagi LLM Benchmark | Not measured | 67.6% | Only o3 measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large 3 2512 | o3 | Leader |
|---|---|---|---|
| AIME | 42.9% | 85.3% | o3 |
| MATH 500 (Vals AI) | Not measured | 94.6% | Only o3 measured |
| MGSM (Vals AI) | 85.4% | 91.8% | o3 |
| AA Math Index | Not measured | 88.3 | Only o3 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 88.3% | Only o3 measured |
| OTIS Mock AIME | Not measured | 84.4% | Only o3 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 33.3% | Only o3 measured |
| MATH level 5 | Not measured | 97.8% | Only o3 measured |
| LMArena Text: math | Not measured | 1426 | Only o3 measured |
| LMArena Text: mathematical work | Not measured | 1423 | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | Mistral Large 3 2512 | o3 | Leader |
|---|---|---|---|
| MMLU Pro | 79.8% | 85.6% | o3 |
| LegalBench (Vals AI) | 79.1% | 83.8% | o3 |
| CaseLaw (Vals AI) | 61.4% | Not measured | Only Mistral Large 3 2512 measured |
| CorpFin (Vals AI) | 61.0% | 59.7% | Mistral Large 3 2512 |
| TaxEval (Vals AI) | 73.1% | 74.6% | o3 |
| MortgageTax (Vals AI) | 52.1% | 65.7% | o3 |
| MedQA (Vals AI) | 82.2% | 96.1% | o3 |
| MedCode (Vals AI) | Not measured | 47.3% | Only o3 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 85.3% | Only o3 measured |
| SimpleQA Verified | Not measured | 49.4% | Only o3 measured |
| GeoBench | Not measured | 74.0% | Only o3 measured |
| LMArena Text: science | Not measured | 1429 | Only o3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1448 | Only o3 measured |
| LMArena Text: legal and government | Not measured | 1428 | Only o3 measured |
| LMArena Text: business and finance | Not measured | 1393 | Only o3 measured |
| FACTS Benchmark Suite | Not measured | 44.4% | Only o3 measured |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | Mistral Large 3 2512 | o3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1162 | 1036 | Mistral Large 3 2512 |
| Design Arena: UI components | 1102 | 1017 | Mistral Large 3 2512 |
| Design Arena: game dev | 1080 | 1034 | Mistral Large 3 2512 |
| Design Arena: data viz | 1136 | 1200 | o3 |
| Design Arena: 3D | 1100 | Not measured | Only Mistral Large 3 2512 measured |
| Design Arena: all code categories | 1142 | 1020 | Mistral Large 3 2512 |
| Design Arena: SVG | 993 | Not measured | Only Mistral Large 3 2512 measured |
| Design Arena: ASCII art | 1069 | Not measured | Only Mistral Large 3 2512 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Mistral Large 3 2512 | o3 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 24.6% | 41.8% | o3 |
| MedScribe (Vals AI) | Not measured | 76.7% | Only o3 measured |
| IFBench | Not measured | 71.4% | Only o3 measured |
| Long context reasoning (AA-LCR) | Not measured | 74.7% | Only o3 measured |
| DTBench | 65.1% | 84.8% | o3 |
| LMCA | 16.7% | 39.7% | o3 |
| Fiction.LiveBench | Not measured | 88.9% | Only o3 measured |
| Creative writing (Lech Mazur) | Not measured | 83.9% | Only o3 measured |
| CL-bench | Not measured | 17.8% | Only o3 measured |
| LMArena Text | Not measured | 1410 | Only o3 measured |
| LMArena Text: instruction following | Not measured | 1368 | Only o3 measured |
| LMArena Text: creative writing | Not measured | 1359 | Only o3 measured |
| LMArena Vision | Not measured | 1214 | Only o3 measured |
| LMArena Text: English | Not measured | 1417 | Only o3 measured |
| LMArena Text: non-English | Not measured | 1401 | Only o3 measured |
| LMArena Text: Chinese | Not measured | 1437 | Only o3 measured |
| LMArena Text: German | Not measured | 1420 | Only o3 measured |
| LMArena Text: Japanese | Not measured | 1403 | Only o3 measured |
| LMArena Text: Korean | Not measured | 1370 | Only o3 measured |
| LMArena Text: French | Not measured | 1430 | Only o3 measured |
| LMArena Text: Spanish | Not measured | 1395 | Only o3 measured |
| LMArena Text: Russian | Not measured | 1406 | Only o3 measured |
| LMArena Text: Polish | Not measured | 1438 | Only o3 measured |
| LMArena Text: multi-turn | Not measured | 1405 | Only o3 measured |
| LMArena Text: longer queries | Not measured | 1372 | Only o3 measured |
| LMArena Text: writing and language | Not measured | 1372 | Only o3 measured |
| LMArena Text: entertainment and media | Not measured | 1373 | Only o3 measured |
| LMArena Vision: English | Not measured | 1221 | Only o3 measured |
| LMArena Vision: Chinese | Not measured | 1237 | Only o3 measured |
| LMArena Vision: reading text | Not measured | 1218 | Only o3 measured |
| LMArena Vision: humor | Not measured | 1222 | Only o3 measured |
| LMArena Vision: captioning | Not measured | 1210 | Only o3 measured |
| LMArena Vision: recognizing things | Not measured | 1234 | Only o3 measured |
| LMArena Vision: creative writing | Not measured | 1201 | Only o3 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 3 2512 | o3 | 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 3 2512 costs less
Repository review
50K input + 3K output tokens
Mistral Large 3 2512 costs less
Agent loop
200K input + 10K output tokens
Mistral Large 3 2512 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 3 2512 | OpenAI o3 |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | December 1, 2025 | April 16, 2025 |
| Context window | 262K | 200K |
| Input $/Mtok | $0.50 | $2.00 |
| Output $/Mtok | $1.50 | $8.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | $0.77 | $0.571 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Large 3 2512 better than o3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Large 3 2512 for output tokens 5.3x cheaper ($1.50 vs $8.00 per million) and a larger context window (262K against 200K tokens).
Which is cheaper, Mistral Large 3 2512 or o3?
Mistral Large 3 2512 costs $0.50 per million input tokens and $1.50 per million output tokens; o3 costs $2.00 and $8.00. Per solved SWE-bench Verified task, Mistral Large 3 2512 costs $0.77 and o3 $0.571.
Which has the bigger context window, Mistral Large 3 2512 or o3?
Mistral Large 3 2512, with 262K tokens against 200K.
How many benchmarks is the Mistral Large 3 2512 vs o3 comparison based on?
21 benchmarks have a result for both models as of October 21, 2026. Mistral Large 3 2512's coding index rests on 1 independent benchmark and o3's on 0. Where only one model has a result, the page says so instead of naming a winner.