Command A vs Mistral Large 3 2512
Updated October 21, 2026. 16 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.
Command A has the edge where what matters is LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.
- Shared results
- 16
- Command A only
- 44
- Mistral Large 3 2512 only
- 12
- Like-for-like categories
- 7 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
Command A
Command A leads, 13.1 against 9.3.
Same board, both measured
Agentic work
AA Agentic Index
No call
Tied at 1.0.
Same board, both measured
Reasoning
GPQA Diamond
Mistral Large 3 2512
Mistral Large 3 2512 leads, 68.4% against 48.5%.
Same board, both measured
Math
AIME
Mistral Large 3 2512
Mistral Large 3 2512 leads, 42.9% against 13.3%.
Same board, both measured
Knowledge
MMLU Pro
Mistral Large 3 2512
Mistral Large 3 2512 leads, 79.8% against 69.2%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Mistral Large 3 2512 has a Design Arena: websites result, 1162.
Directional only
Long documents
Listed context window
Mistral Large 3 2512
Mistral Large 3 2512 takes 262K tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 3 2512
Command A $0.0075, Mistral Large 3 2512 $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Mistral Large 3 2512
Command A $2.69, Mistral Large 3 2512 $0.77.
Published attempt cost and pass rate
At a glance
- Coding index
- Command A not benchmarked, Mistral Large 3 2512 not benchmarked.
- Widest category
- Coding, 20.5 points apart on average over 3 shared benchmarks.
- Widest single gap
- SWE-bench Verified (Vals AI): Command A 7.8% against Mistral Large 3 2512 41.4%, 33.6 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against Mistral Large 3 2512 $1.50 per million output tokens, 6.7x apart.
- Context window
- Command A takes 256K tokens, Mistral Large 3 2512 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 | Command A | Bars | Mistral Large 3 2512 | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 0 | 1 tie | |
| Reasoning | 0 | 1 | ||
| Math | 1 | 1 | ||
| Knowledge | 2 | 4 | ||
| General | 0 | 2 | ||
| All shared boards | 5 | 10 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | Command A | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AA Coding Index | 27.8 | 20.1 | Command A |
| LiveCodeBench (Vals AI) | 35.1% | 55.3% | Mistral Large 3 2512 |
| SWE-bench Verified (Vals AI) | 7.8% | 41.4% | Mistral Large 3 2512 |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | Not measured | Only Command A measured |
| LiveCodeBench (Artificial Analysis run) | 28.7% | Not measured | Only Command A measured |
| SciCode | 38.5% | Not measured | Only Command A measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | Not measured | Only Command A measured |
| ALE-Bench | Not measured | 264.7 | Only Mistral Large 3 2512 measured |
| LMArena Text: coding | 1330 | Not measured | Only Command A measured |
| LMArena Text: software and IT | 1336 | Not measured | Only Command A measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Command A | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 9.3 | Command A |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index00
| Benchmark | Command A | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | 1.0 | Tie |
| Finance Agent (Vals AI) | 9.0% | Not measured | Only Command A measured |
| τ²-Bench Telecom | 80.7% | Not measured | Only Command A measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | 68.4% | Mistral Large 3 2512 |
| MMMU Pro (Vals AI) | Not measured | 66.2% | Only Mistral Large 3 2512 measured |
| GPQA Diamond (Artificial Analysis run) | 76.1% | Not measured | Only Command A measured |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | Not measured | Only Command A measured |
| LMArena Text: hard prompts | 1326 | Not measured | Only Command A measured |
| LMArena Text: expert | 1296 | Not measured | Only Command A measured |
| LMArena Text: hard prompts in English | 1335 | Not measured | Only Command A measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Command A | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AIME | 13.3% | 42.9% | Mistral Large 3 2512 |
| MATH 500 (Vals AI) | 76.2% | Not measured | Only Command A measured |
| MGSM (Vals AI) | 85.7% | 85.4% | Command A |
| AA Math Index | 13.0 | Not measured | Only Command A measured |
| AIME 2025 (Artificial Analysis run) | 13.0% | Not measured | Only Command A measured |
| LMArena Text: math | 1300 | Not measured | Only Command A measured |
| LMArena Text: mathematical work | 1312 | Not measured | Only Command A measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | Command A | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | 79.8% | Mistral Large 3 2512 |
| LegalBench (Vals AI) | 79.7% | 79.1% | Command A |
| CaseLaw (Vals AI) | 64.5% | 61.4% | Command A |
| CorpFin (Vals AI) | 46.0% | 61.0% | Mistral Large 3 2512 |
| TaxEval (Vals AI) | 61.4% | 73.1% | Mistral Large 3 2512 |
| MortgageTax (Vals AI) | Not measured | 52.1% | Only Mistral Large 3 2512 measured |
| MedQA (Vals AI) | 80.5% | 82.2% | Mistral Large 3 2512 |
| MedCode (Vals AI) | 19.7% | Not measured | Only Command A measured |
| MMLU Pro (Artificial Analysis run) | 71.2% | Not measured | Only Command A measured |
| LMArena Text: science | 1339 | Not measured | Only Command A measured |
| LMArena Text: medicine and healthcare | 1334 | Not measured | Only Command A measured |
| LMArena Text: legal and government | 1348 | Not measured | Only Command A measured |
| LMArena Text: business and finance | 1315 | Not measured | Only Command A measured |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Command A | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1162 | Only Mistral Large 3 2512 measured |
| Design Arena: UI components | Not measured | 1102 | Only Mistral Large 3 2512 measured |
| Design Arena: game dev | Not measured | 1080 | Only Mistral Large 3 2512 measured |
| Design Arena: data viz | Not measured | 1136 | Only Mistral Large 3 2512 measured |
| Design Arena: 3D | Not measured | 1099 | Only Mistral Large 3 2512 measured |
| Design Arena: all code categories | Not measured | 1142 | Only Mistral Large 3 2512 measured |
| Design Arena: SVG | Not measured | 993 | Only Mistral Large 3 2512 measured |
| Design Arena: ASCII art | Not measured | 1069 | 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 index02
| Benchmark | Command A | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 24.6% | Only Mistral Large 3 2512 measured |
| MedScribe (Vals AI) | 55.7% | Not measured | Only Command A measured |
| IFBench | 73.9% | Not measured | Only Command A measured |
| Long context reasoning (AA-LCR) | 52.7% | Not measured | Only Command A measured |
| DTBench | 61.3% | 65.1% | Mistral Large 3 2512 |
| LMCA | 10.3% | 16.7% | Mistral Large 3 2512 |
| LMArena Text | 1331 | Not measured | Only Command A measured |
| LMArena Text: instruction following | 1309 | Not measured | Only Command A measured |
| LMArena Text: creative writing | 1319 | Not measured | Only Command A measured |
| LMArena Text: English | 1344 | Not measured | Only Command A measured |
| LMArena Text: non-English | 1313 | Not measured | Only Command A measured |
| LMArena Text: Chinese | 1327 | Not measured | Only Command A measured |
| LMArena Text: German | 1342 | Not measured | Only Command A measured |
| LMArena Text: Japanese | 1285 | Not measured | Only Command A measured |
| LMArena Text: Korean | 1285 | Not measured | Only Command A measured |
| LMArena Text: French | 1351 | Not measured | Only Command A measured |
| LMArena Text: Spanish | 1347 | Not measured | Only Command A measured |
| LMArena Text: Russian | 1314 | Not measured | Only Command A measured |
| LMArena Text: Polish | 1324 | Not measured | Only Command A measured |
| LMArena Text: multi-turn | 1338 | Not measured | Only Command A measured |
| LMArena Text: longer queries | 1334 | Not measured | Only Command A measured |
| LMArena Text: writing and language | 1328 | Not measured | Only Command A measured |
| LMArena Text: entertainment and media | 1306 | Not measured | Only Command A measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | Mistral Large 3 2512 | 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 | Cohere Command A | Mistral Large 3 2512 |
|---|---|---|
| Maker | Cohere | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | December 1, 2025 |
| Context window | 256K | 262K |
| Input $/Mtok | $2.50 | $0.50 |
| Output $/Mtok | $10.00 | $1.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $2.69 | $0.77 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than Mistral Large 3 2512 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Command A has the edge where what matters is LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.
Which is cheaper, Command A or Mistral Large 3 2512?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Mistral Large 3 2512 costs $0.50 and $1.50. Per solved SWE-bench Verified task, Command A costs $2.69 and Mistral Large 3 2512 $0.77.
Which has the bigger context window, Command A or Mistral Large 3 2512?
Mistral Large 3 2512, with 262K tokens against 256K.
How many benchmarks is the Command A vs Mistral Large 3 2512 comparison based on?
16 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and Mistral Large 3 2512's on 1. Where only one model has a result, the page says so instead of naming a winner.