Command A vs Mistral Large 2407
Command A leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 13.1 to 7.6, and Mistral Large 2407 costs 1.7x less per output token ($6.00 vs $10.00 per million). Updated October 21, 2026; 11 benchmarks have a result for both.
Command A or Mistral Large 2407: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Command A for a larger context window (256K against 131K tokens).
- Shared results
- 11
- Command A only
- 51
- Mistral Large 2407 only
- 6
- 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
Command A
Command A leads, 13.1 against 7.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Command A has a AA Agentic Index result, 1.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Command A has a GPQA Diamond result, 48.5%.
Directional only
Math
AIME
No call
Only Command A has a AIME result, 13.3%.
Directional only
Knowledge
MMLU Pro
No call
Only Command A has a MMLU Pro result, 69.2%.
Directional only
Long documents
Listed context window
Command A
Command A takes 256K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 2407
Command A $0.0075, Mistral Large 2407 $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Command A has the number, $2.69.
Directional only
At a glance
- Coding index
- Command A not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- Agentic work, 47.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Command A 52.7% against Mistral Large 2407 2.0%, 50.7 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against Mistral Large 2407 $6.00 per million output tokens, 1.7x apart.
- Context window
- Command A takes 256K tokens, Mistral Large 2407 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 | Command A | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 3 | 0 | ||
| All shared boards | 11 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Command A | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Coding Index | 27.8 | Not measured | Only Command A measured |
| LiveCodeBench (Vals AI) | 35.1% | Not measured | Only Command A measured |
| SWE-bench Verified (Vals AI) | 7.8% | Not measured | Only Command A measured |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | Not measured | Only Command A measured |
| LiveCodeBench (Artificial Analysis run) | 28.7% | 26.7% | Command A |
| SciCode | 38.5% | Not measured | Only Command A measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | Not measured | Only Command A 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 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Command A | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 7.6 | 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 index10
| Benchmark | Command A | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Command A measured |
| Finance Agent (Vals AI) | 9.0% | Not measured | Only Command A measured |
| τ²-Bench Telecom | 80.7% | 33.0% | Command A |
| Berkeley Function Calling Leaderboard (BFCL) | 46.5% | Not measured | Only Command A measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Command A | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | Not measured | Only Command A measured |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 47.2% | Command A |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 2.9% | Command A |
| GPQA Diamond (Epoch AI run) | Not measured | 49.0% | Only Mistral Large 2407 measured |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 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 |
| Kagi LLM Benchmark | 28.8% | Not measured | Only Command A measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Command A | Mistral Large 2407 | Leader |
|---|---|---|---|
| AIME | 13.3% | Not measured | Only Command A measured |
| MATH 500 (Vals AI) | 76.2% | Not measured | Only Command A measured |
| MGSM (Vals AI) | 85.7% | Not measured | Only Command A measured |
| AA Math Index | 13.0 | 0.0 | Command A |
| AIME 2025 (Artificial Analysis run) | 13.0% | 0.0% | Command A |
| OTIS Mock AIME | Not measured | 8.5% | Only Mistral Large 2407 measured |
| MATH level 5 | Not measured | 44.8% | Only Mistral Large 2407 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 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Command A | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | Not measured | Only Command A measured |
| LegalBench (Vals AI) | 79.7% | Not measured | Only Command A measured |
| CaseLaw (Vals AI) | 64.5% | Not measured | Only Command A measured |
| CorpFin (Vals AI) | 46.0% | Not measured | Only Command A measured |
| TaxEval (Vals AI) | 61.4% | Not measured | Only Command A measured |
| MedQA (Vals AI) | 80.5% | Not measured | Only Command A measured |
| MedCode (Vals AI) | 19.7% | Not measured | Only Command A measured |
| MMLU Pro (Artificial Analysis run) | 71.2% | 68.3% | Command A |
| MMLU | Not measured | 80.0% | Only Mistral Large 2407 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 5 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | Mistral Large 2407 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Command A | Mistral Large 2407 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 55.7% | Not measured | Only Command A measured |
| IFBench | 73.9% | 31.6% | Command A |
| Long context reasoning (AA-LCR) | 52.7% | 2.0% | Command A |
| DTBench | 61.3% | 61.2% | Command A |
| LMCA | 10.3% | Not measured | Only Command A measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| 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 2407 | 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 2407 costs less
Repository review
50K input + 3K output tokens
Mistral Large 2407 costs less
Agent loop
200K input + 10K output tokens
Mistral Large 2407 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 2407 |
|---|---|---|
| Maker | Cohere | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | November 19, 2024 |
| Context window | 256K | 131K |
| Input $/Mtok | $2.50 | $2.00 |
| Output $/Mtok | $10.00 | $6.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | $2.69 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than Mistral Large 2407 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Command A for a larger context window (256K against 131K tokens).
Which is cheaper, Command A or Mistral Large 2407?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Mistral Large 2407 costs $2.00 and $6.00. Cost per solved task is known for Command A only.
Which has the bigger context window, Command A or Mistral Large 2407?
Command A, with 256K tokens against 131K.
How many benchmarks is the Command A vs Mistral Large 2407 comparison based on?
11 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 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.