Command A vs Mistral Large
Command A leads Mistral Large on the Artificial Analysis Intelligence Index, 13.1 to 5.8, and Mistral Large costs 1.7x less per output token ($6.00 vs $10.00 per million). Updated October 21, 2026; 42 benchmarks have a result for both.
Command A or Mistral Large: 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 128K tokens) and work measured by LiveCodeBench (Vals AI) or MGSM (Vals AI), where it leads.
- Shared results
- 42
- Command A only
- 20
- Mistral Large only
- 2
- 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
Command A
Command A leads, 13.1 against 5.8.
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
Command A
Command A leads, 48.5% against 47.7%.
Same board, both measured
Math
AIME
Command A
Command A leads, 13.3% against 9.2%.
Same board, both measured
Knowledge
MMLU Pro
Mistral Large
Mistral Large leads, 69.7% against 69.2%.
Same board, both measured
Long documents
Listed context window
Command A
Command A takes 256K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large
Command A $0.0075, Mistral Large $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 not benchmarked.
- Widest category
- Reasoning, 14.6 points apart on average over 6 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Command A 76.1% against Mistral Large 35.1%, 41.0 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against Mistral Large $6.00 per million output tokens, 1.7x apart.
- Context window
- Command A takes 256K tokens, Mistral Large 128K.
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 | Ties |
|---|---|---|---|---|
| Coding | 3 | 1 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 4 | 1 | ||
| Knowledge | 6 | 2 | ||
| General | 18 | 0 | ||
| All shared boards | 38 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index31
| Benchmark | Command A | Mistral Large | Leader |
|---|---|---|---|
| AA Coding Index | 27.8 | Not measured | Only Command A measured |
| LiveCodeBench (Vals AI) | 35.1% | 37.1% | Mistral Large |
| 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% | 17.8% | 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 | 1275 | Command A |
| LMArena Text: software and IT | 1336 | 1274 | Command A |
| 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 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 5.8 | Command A |
| Epoch Capabilities Index | Not measured | 122.1 | Only Mistral Large measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Command A | Mistral Large | 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% | Not measured | Only Command A measured |
| 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 index60
| Benchmark | Command A | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | 47.7% | Command A |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 35.1% | Command A |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 3.5% | Command A |
| LMArena Text: hard prompts | 1326 | 1257 | Command A |
| LMArena Text: expert | 1296 | 1208 | Command A |
| LMArena Text: hard prompts in English | 1335 | 1268 | Command A |
| Kagi LLM Benchmark | 28.8% | Not measured | Only Command A measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | Command A | Mistral Large | Leader |
|---|---|---|---|
| AIME | 13.3% | 9.2% | Command A |
| MATH 500 (Vals AI) | 76.2% | 74.4% | Command A |
| MGSM (Vals AI) | 85.7% | 87.2% | Mistral Large |
| 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 | 1261 | Command A |
| LMArena Text: mathematical work | 1312 | 1257 | Command A |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index62
| Benchmark | Command A | Mistral Large | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | 69.7% | Mistral Large |
| 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% | 63.8% | Mistral Large |
| MedQA (Vals AI) | 80.5% | 76.2% | Command A |
| MedCode (Vals AI) | 19.7% | Not measured | Only Command A measured |
| MMLU Pro (Artificial Analysis run) | 71.2% | 51.5% | Command A |
| MMLU | Not measured | 68.8% | Only Mistral Large measured |
| LMArena Text: science | 1339 | 1260 | Command A |
| LMArena Text: medicine and healthcare | 1334 | 1255 | Command A |
| LMArena Text: legal and government | 1348 | 1275 | Command A |
| LMArena Text: business and finance | 1315 | 1238 | Command A |
| 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 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
| Benchmark | Command A | Mistral Large | Leader |
|---|---|---|---|
| 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% | 60.8% | Command A |
| LMCA | 10.3% | Not measured | Only Command A measured |
| LMArena Text | 1331 | 1265 | Command A |
| LMArena Text: instruction following | 1309 | 1249 | Command A |
| LMArena Text: creative writing | 1319 | 1242 | Command A |
| LMArena Text: English | 1344 | 1284 | Command A |
| LMArena Text: non-English | 1313 | 1235 | Command A |
| LMArena Text: Chinese | 1327 | 1239 | Command A |
| LMArena Text: German | 1342 | 1242 | Command A |
| LMArena Text: Japanese | 1285 | 1170 | Command A |
| LMArena Text: Korean | 1285 | 1202 | Command A |
| LMArena Text: French | 1351 | 1325 | Command A |
| LMArena Text: Spanish | 1347 | 1268 | Command A |
| LMArena Text: Russian | 1314 | 1252 | Command A |
| LMArena Text: Polish | 1324 | 1157 | Command A |
| LMArena Text: multi-turn | 1338 | 1260 | Command A |
| LMArena Text: longer queries | 1334 | 1261 | Command A |
| LMArena Text: writing and language | 1328 | 1261 | Command A |
| LMArena Text: entertainment and media | 1306 | 1230 | Command A |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | 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 | Cohere Command A | Mistral Large |
|---|---|---|
| Maker | Cohere | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | February 26, 2024 |
| Context window | 256K | 128K |
| 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 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 128K tokens) and work measured by LiveCodeBench (Vals AI) or MGSM (Vals AI), where it leads.
Which is cheaper, Command A or Mistral Large?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Mistral Large 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?
Command A, with 256K tokens against 128K.
How many benchmarks is the Command A vs Mistral Large comparison based on?
42 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's on 0. Where only one model has a result, the page says so instead of naming a winner.