Command A vs Mistral Medium 3.5
Updated October 21, 2026. 49 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding.
Command A has the edge where a solved task costs $2.69 against $6.02, and where what matters is GPQA Diamond or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 49
- Command A only
- 11
- Mistral Medium 3.5 only
- 31
- 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
Only Mistral Medium 3.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Mistral Medium 3.5
Mistral Medium 3.5 leads, 14.2 against 13.1.
Same board, both measured
Agentic work
AA Agentic Index
Mistral Medium 3.5
Mistral Medium 3.5 leads, 7.0 against 1.0.
Same board, both measured
Reasoning
GPQA Diamond
Command A
Command A leads, 48.5% against 34.9%.
Same board, both measured
Math
AIME
No call
Only Command A has a AIME result, 13.3%.
Directional only
Knowledge
MMLU Pro
Mistral Medium 3.5
Mistral Medium 3.5 leads, 75.3% against 69.2%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Mistral Medium 3.5 has a Design Arena: websites result, 1142.
Directional only
Long documents
Listed context window
Mistral Medium 3.5
Mistral Medium 3.5 takes 262K tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Medium 3.5
Command A $0.0075, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Command A
Command A $2.69, Mistral Medium 3.5 $6.02.
Published attempt cost and pass rate
At a glance
- Coding index
- Command A not benchmarked, Mistral Medium 3.5 25.5 from 3 benchmarks.
- Widest category
- Math, 21.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- SWE-bench Verified (Vals AI): Command A 7.8% against Mistral Medium 3.5 66.4%, 58.6 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against Mistral Medium 3.5 $7.50 per million output tokens, 1.3x apart.
- Context window
- Command A takes 256K tokens, Mistral Medium 3.5 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 Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 3 | ||
| Reasoning | 2 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 8 | ||
| General | 1 | 20 | ||
| All shared boards | 4 | 45 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
| Benchmark | Command A | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1264 | Only Mistral Medium 3.5 measured |
| AA Coding Index | 27.8 | 46.9 | Mistral Medium 3.5 |
| LiveCodeBench (Vals AI) | 35.1% | Not measured | Only Command A measured |
| SWE-bench Verified (Vals AI) | 7.8% | 66.4% | Mistral Medium 3.5 |
| Vibe Code Bench (Vals AI) | Not measured | 2.9% | Only Mistral Medium 3.5 measured |
| Code migration (Vals AI) | Not measured | 5.1% | Only Mistral Medium 3.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | 39.0% | Mistral Medium 3.5 |
| LiveCodeBench (Artificial Analysis run) | 28.7% | Not measured | Only Command A measured |
| SciCode | 38.5% | 40.2% | Mistral Medium 3.5 |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 33.3% | Mistral Medium 3.5 |
| ALE-Bench | Not measured | 764.0 | Only Mistral Medium 3.5 measured |
| LMArena Text: coding | 1330 | 1460 | Mistral Medium 3.5 |
| LMArena Text: software and IT | 1336 | 1458 | Mistral Medium 3.5 |
| LMArena WebDev: React | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena WebDev: HTML | Not measured | 1308 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 14.2 | Mistral Medium 3.5 |
| Epoch Capabilities Index | Not measured | 141.4 | Only Mistral Medium 3.5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Command A | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | 7.0 | Mistral Medium 3.5 |
| Finance Agent (Vals AI) | 9.0% | 32.1% | Mistral Medium 3.5 |
| Tax Agent Bench (Vals AI) | Not measured | 4.5% | Only Mistral Medium 3.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 9.1% | Only Mistral Medium 3.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 11.2% | Only Mistral Medium 3.5 measured |
| τ²-Bench Telecom | 80.7% | 94.2% | Mistral Medium 3.5 |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | Command A | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | 34.9% | Command A |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 74.8% | Command A |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 13.8% | Mistral Medium 3.5 |
| LMArena Text: hard prompts | 1326 | 1436 | Mistral Medium 3.5 |
| LMArena Text: expert | 1296 | 1432 | Mistral Medium 3.5 |
| LMArena Text: hard prompts in English | 1335 | 1445 | Mistral Medium 3.5 |
| LMArena Vision: diagrams | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena Vision: homework | Not measured | 1230 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Command A | Mistral Medium 3.5 | 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 | Not measured | Only Command A measured |
| AIME 2025 (Artificial Analysis run) | 13.0% | Not measured | Only Command A measured |
| ProofBench | Not measured | 9.0% | Only Mistral Medium 3.5 measured |
| LMArena Text: math | 1300 | 1431 | Mistral Medium 3.5 |
| LMArena Text: mathematical work | 1312 | 1440 | Mistral Medium 3.5 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index18
| Benchmark | Command A | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | 75.3% | Mistral Medium 3.5 |
| LegalBench (Vals AI) | 79.7% | Not measured | Only Command A measured |
| CaseLaw (Vals AI) | 64.5% | 44.2% | Command A |
| CorpFin (Vals AI) | 46.0% | 58.8% | Mistral Medium 3.5 |
| TaxEval (Vals AI) | 61.4% | 68.0% | Mistral Medium 3.5 |
| MortgageTax (Vals AI) | Not measured | 28.9% | Only Mistral Medium 3.5 measured |
| MedQA (Vals AI) | 80.5% | Not measured | Only Command A measured |
| MedCode (Vals AI) | 19.7% | 33.8% | Mistral Medium 3.5 |
| MMLU Pro (Artificial Analysis run) | 71.2% | Not measured | Only Command A measured |
| LMArena Text: science | 1339 | 1428 | Mistral Medium 3.5 |
| LMArena Text: medicine and healthcare | 1334 | 1409 | Mistral Medium 3.5 |
| LMArena Text: legal and government | 1348 | 1437 | Mistral Medium 3.5 |
| LMArena Text: business and finance | 1315 | 1426 | Mistral Medium 3.5 |
| 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 Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1142 | Only Mistral Medium 3.5 measured |
| Design Arena: UI components | Not measured | 1079 | Only Mistral Medium 3.5 measured |
| Design Arena: game dev | Not measured | 1079 | Only Mistral Medium 3.5 measured |
| Design Arena: data viz | Not measured | 1120 | Only Mistral Medium 3.5 measured |
| Design Arena: 3D | Not measured | 1061 | Only Mistral Medium 3.5 measured |
| Design Arena: all code categories | Not measured | 1121 | Only Mistral Medium 3.5 measured |
| Design Arena: SVG | Not measured | 950 | Only Mistral Medium 3.5 measured |
| Design Arena: ASCII art | Not measured | 1062 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index120
| Benchmark | Command A | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 37.6% | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | 55.7% | 67.7% | Mistral Medium 3.5 |
| Vals Multimodal Index | Not measured | 34.8% | Only Mistral Medium 3.5 measured |
| IFBench | 73.9% | 68.8% | Command A |
| Long context reasoning (AA-LCR) | 52.7% | 69.3% | Mistral Medium 3.5 |
| DTBench | 61.3% | 75.5% | Mistral Medium 3.5 |
| LMCA | 10.3% | 26.1% | Mistral Medium 3.5 |
| Surface Evolver Bench | Not measured | 26.9% | Only Mistral Medium 3.5 measured |
| LMArena Text | 1331 | 1421 | Mistral Medium 3.5 |
| LMArena Text: instruction following | 1309 | 1415 | Mistral Medium 3.5 |
| LMArena Text: creative writing | 1319 | 1374 | Mistral Medium 3.5 |
| LMArena Vision | Not measured | 1222 | Only Mistral Medium 3.5 measured |
| LMArena Text: English | 1344 | 1433 | Mistral Medium 3.5 |
| LMArena Text: non-English | 1313 | 1404 | Mistral Medium 3.5 |
| LMArena Text: Chinese | 1327 | 1444 | Mistral Medium 3.5 |
| LMArena Text: German | 1342 | 1453 | Mistral Medium 3.5 |
| LMArena Text: Japanese | 1285 | Not measured | Only Command A measured |
| LMArena Text: Korean | 1285 | 1384 | Mistral Medium 3.5 |
| LMArena Text: French | 1351 | 1448 | Mistral Medium 3.5 |
| LMArena Text: Spanish | 1347 | 1411 | Mistral Medium 3.5 |
| LMArena Text: Russian | 1314 | 1395 | Mistral Medium 3.5 |
| LMArena Text: Polish | 1324 | 1430 | Mistral Medium 3.5 |
| LMArena Text: multi-turn | 1338 | 1423 | Mistral Medium 3.5 |
| LMArena Text: longer queries | 1334 | 1415 | Mistral Medium 3.5 |
| LMArena Text: writing and language | 1328 | 1384 | Mistral Medium 3.5 |
| LMArena Text: entertainment and media | 1306 | 1382 | Mistral Medium 3.5 |
| LMArena Vision: English | Not measured | 1240 | Only Mistral Medium 3.5 measured |
| LMArena Vision: Chinese | Not measured | 1244 | Only Mistral Medium 3.5 measured |
| LMArena Vision: reading text | Not measured | 1233 | Only Mistral Medium 3.5 measured |
| LMArena Vision: humor | Not measured | 1216 | Only Mistral Medium 3.5 measured |
| LMArena Vision: creative writing | Not measured | 1198 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | Mistral Medium 3.5 | 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 Medium 3.5 costs less
Repository review
50K input + 3K output tokens
Mistral Medium 3.5 costs less
Agent loop
200K input + 10K output tokens
Mistral Medium 3.5 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 Medium 3.5 |
|---|---|---|
| Maker | Cohere | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | April 30, 2026 |
| Context window | 256K | 262K |
| Input $/Mtok | $2.50 | $1.50 |
| Output $/Mtok | $10.00 | $7.50 |
| Coding index | Not benchmarked | 25.5 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | $2.69 | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than Mistral Medium 3.5 for coding?
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding. Command A has the edge where a solved task costs $2.69 against $6.02, and where what matters is GPQA Diamond or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, Command A or Mistral Medium 3.5?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Per solved SWE-bench Verified task, Command A costs $2.69 and Mistral Medium 3.5 $6.02.
Which has the bigger context window, Command A or Mistral Medium 3.5?
Mistral Medium 3.5, with 262K tokens against 256K.
How many benchmarks is the Command A vs Mistral Medium 3.5 comparison based on?
49 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and Mistral Medium 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.