Command A vs MiMo-V2.5-Pro
MiMo-V2.5-Pro leads Command A on the Artificial Analysis Intelligence Index, 26.0 to 13.1, and MiMo-V2.5-Pro costs 10.9x less per output token ($0.9152 vs $10.00 per million). Updated October 21, 2026; 51 benchmarks have a result for both.
Command A or MiMo-V2.5-Pro: which to pick
Only MiMo-V2.5-Pro has a coding score, 31.6 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding.
Pick Command A for work measured by LegalBench (Vals AI), where it leads.
- Shared results
- 51
- Command A only
- 11
- MiMo-V2.5-Pro only
- 19
- 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
Only MiMo-V2.5-Pro has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
MiMo-V2.5-Pro
MiMo-V2.5-Pro leads, 26.0 against 13.1.
Same board, both measured
Agentic work
AA Agentic Index
MiMo-V2.5-Pro
MiMo-V2.5-Pro leads, 21.3 against 1.0.
Same board, both measured
Reasoning
GPQA Diamond
MiMo-V2.5-Pro
MiMo-V2.5-Pro leads, 82.6% against 48.5%.
Same board, both measured
Math
AIME
No call
Only Command A has a AIME result, 13.3%.
Directional only
Knowledge
MMLU Pro
MiMo-V2.5-Pro
MiMo-V2.5-Pro leads, 84.6% against 69.2%.
Same board, both measured
Web design
Design Arena: websites
No call
Only MiMo-V2.5-Pro has a Design Arena: websites result, 1267.
Directional only
Long documents
Listed context window
MiMo-V2.5-Pro
MiMo-V2.5-Pro takes 1.05M tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.5-Pro
Command A $0.0075, MiMo-V2.5-Pro $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
MiMo-V2.5-Pro
Command A $2.69, MiMo-V2.5-Pro $0.03.
Published attempt cost and pass rate
At a glance
- Coding index
- Command A not benchmarked, MiMo-V2.5-Pro 31.6 from 4 benchmarks.
- Widest category
- Coding, 33.7 points apart on average over 8 shared benchmarks.
- Widest single gap
- SWE-bench Verified (Vals AI): Command A 7.8% against MiMo-V2.5-Pro 74.0%, 66.2 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against MiMo-V2.5-Pro $0.9152 per million output tokens, 10.9x apart.
- Context window
- Command A takes 256K tokens, MiMo-V2.5-Pro 1.05M.
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 | MiMo-V2.5-Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 8 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 3 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 8 | ||
| General | 0 | 22 | ||
| All shared boards | 1 | 50 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index08
| Benchmark | Command A | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1480 | Only MiMo-V2.5-Pro measured |
| AA Coding Index | 27.8 | 60.2 | MiMo-V2.5-Pro |
| LiveCodeBench (Vals AI) | 35.1% | 81.3% | MiMo-V2.5-Pro |
| SWE-bench Verified (Vals AI) | 7.8% | 74.0% | MiMo-V2.5-Pro |
| Terminal-Bench 4 (Vals AI) | Not measured | 0.5% | Only MiMo-V2.5-Pro measured |
| Vibe Code Bench (Vals AI) | Not measured | 34.1% | Only MiMo-V2.5-Pro measured |
| Code migration (Vals AI) | Not measured | 21.6% | Only MiMo-V2.5-Pro measured |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | 57.3% | MiMo-V2.5-Pro |
| LiveCodeBench (Artificial Analysis run) | 28.7% | Not measured | Only Command A measured |
| SciCode | 38.5% | 50.6% | MiMo-V2.5-Pro |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 43.2% | MiMo-V2.5-Pro |
| ALE-Bench | Not measured | 899.8 | Only MiMo-V2.5-Pro measured |
| LMArena Text: coding | 1330 | 1501 | MiMo-V2.5-Pro |
| LMArena Text: software and IT | 1336 | 1495 | MiMo-V2.5-Pro |
| LMArena WebDev: React | Not measured | 1469 | Only MiMo-V2.5-Pro measured |
| LMArena WebDev: HTML | Not measured | 1473 | Only MiMo-V2.5-Pro 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 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 26.0 | MiMo-V2.5-Pro |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Command A | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | 21.3 | MiMo-V2.5-Pro |
| Finance Agent (Vals AI) | 9.0% | 41.5% | MiMo-V2.5-Pro |
| Tax Agent Bench (Vals AI) | Not measured | 11.5% | Only MiMo-V2.5-Pro measured |
| Legal Research Bench (Vals AI) | Not measured | 15.9% | Only MiMo-V2.5-Pro measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 55.2% | Only MiMo-V2.5-Pro measured |
| τ²-Bench Telecom | 80.7% | 94.2% | MiMo-V2.5-Pro |
| Berkeley Function Calling Leaderboard (BFCL) | 46.5% | Not measured | Only Command A measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Command A | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | 82.6% | MiMo-V2.5-Pro |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 86.6% | MiMo-V2.5-Pro |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 35.7% | MiMo-V2.5-Pro |
| LMArena Text: hard prompts | 1326 | 1486 | MiMo-V2.5-Pro |
| LMArena Text: expert | 1296 | 1501 | MiMo-V2.5-Pro |
| LMArena Text: hard prompts in English | 1335 | 1494 | MiMo-V2.5-Pro |
| 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 index02
| Benchmark | Command A | MiMo-V2.5-Pro | 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 | 22.0% | Only MiMo-V2.5-Pro measured |
| LMArena Text: math | 1300 | 1481 | MiMo-V2.5-Pro |
| LMArena Text: mathematical work | 1312 | 1491 | MiMo-V2.5-Pro |
| 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 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | 84.6% | MiMo-V2.5-Pro |
| LegalBench (Vals AI) | 79.7% | 77.1% | Command A |
| CaseLaw (Vals AI) | 64.5% | Not measured | Only Command A measured |
| CorpFin (Vals AI) | 46.0% | 61.4% | MiMo-V2.5-Pro |
| TaxEval (Vals AI) | 61.4% | 73.8% | MiMo-V2.5-Pro |
| MedQA (Vals AI) | 80.5% | Not measured | Only Command A measured |
| MedCode (Vals AI) | 19.7% | 32.5% | MiMo-V2.5-Pro |
| MMLU Pro (Artificial Analysis run) | 71.2% | Not measured | Only Command A measured |
| LMArena Text: science | 1339 | 1482 | MiMo-V2.5-Pro |
| LMArena Text: medicine and healthcare | 1334 | 1473 | MiMo-V2.5-Pro |
| LMArena Text: legal and government | 1348 | 1474 | MiMo-V2.5-Pro |
| LMArena Text: business and finance | 1315 | 1466 | MiMo-V2.5-Pro |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Command A | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1267 | Only MiMo-V2.5-Pro measured |
| Design Arena: UI components | Not measured | 1249 | Only MiMo-V2.5-Pro measured |
| Design Arena: game dev | Not measured | 1262 | Only MiMo-V2.5-Pro measured |
| Design Arena: data viz | Not measured | 1257 | Only MiMo-V2.5-Pro measured |
| Design Arena: 3D | Not measured | 1244 | Only MiMo-V2.5-Pro measured |
| Design Arena: all code categories | Not measured | 1266 | Only MiMo-V2.5-Pro measured |
| Design Arena: SVG | Not measured | 1181 | Only MiMo-V2.5-Pro measured |
| Design Arena: ASCII art | Not measured | 1156 | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index022
| Benchmark | Command A | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 55.7% | 83.7% | MiMo-V2.5-Pro |
| IFBench | 73.9% | 79.9% | MiMo-V2.5-Pro |
| Long context reasoning (AA-LCR) | 52.7% | 79.7% | MiMo-V2.5-Pro |
| DTBench | 61.3% | 84.5% | MiMo-V2.5-Pro |
| LMCA | 10.3% | 29.5% | MiMo-V2.5-Pro |
| LMArena Text | 1331 | 1464 | MiMo-V2.5-Pro |
| LMArena Text: instruction following | 1309 | 1475 | MiMo-V2.5-Pro |
| LMArena Text: creative writing | 1319 | 1438 | MiMo-V2.5-Pro |
| LMArena Text: English | 1344 | 1477 | MiMo-V2.5-Pro |
| LMArena Text: non-English | 1313 | 1448 | MiMo-V2.5-Pro |
| LMArena Text: Chinese | 1327 | 1507 | MiMo-V2.5-Pro |
| LMArena Text: German | 1342 | 1459 | MiMo-V2.5-Pro |
| LMArena Text: Japanese | 1285 | 1410 | MiMo-V2.5-Pro |
| LMArena Text: Korean | 1285 | 1435 | MiMo-V2.5-Pro |
| LMArena Text: French | 1351 | 1487 | MiMo-V2.5-Pro |
| LMArena Text: Spanish | 1347 | 1469 | MiMo-V2.5-Pro |
| LMArena Text: Russian | 1314 | 1449 | MiMo-V2.5-Pro |
| LMArena Text: Polish | 1324 | 1464 | MiMo-V2.5-Pro |
| LMArena Text: multi-turn | 1338 | 1476 | MiMo-V2.5-Pro |
| LMArena Text: longer queries | 1334 | 1481 | MiMo-V2.5-Pro |
| LMArena Text: writing and language | 1328 | 1451 | MiMo-V2.5-Pro |
| LMArena Text: entertainment and media | 1306 | 1438 | MiMo-V2.5-Pro |
| 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 | MiMo-V2.5-Pro | 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
MiMo-V2.5-Pro costs less
Repository review
50K input + 3K output tokens
MiMo-V2.5-Pro costs less
Agent loop
200K input + 10K output tokens
MiMo-V2.5-Pro 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 | Xiaomi MiMo-V2.5-Pro |
|---|---|---|
| Maker | Cohere | Xiaomi |
| Weights | Open weights | Open weights |
| Released | March 13, 2025 | April 22, 2026 |
| Context window | 256K | 1.05M |
| Input $/Mtok | $2.50 | $0.4575 |
| Output $/Mtok | $10.00 | $0.9152 |
| Coding index | Not benchmarked | 31.6 |
| Independent measurements behind it | 1 | 4 |
| Cost per solved task | $2.69 | $0.03 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than MiMo-V2.5-Pro for coding?
Only MiMo-V2.5-Pro has a coding score, 31.6 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding. Pick Command A for work measured by LegalBench (Vals AI), where it leads.
Which is cheaper, Command A or MiMo-V2.5-Pro?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; MiMo-V2.5-Pro costs $0.4575 and $0.9152. Per solved SWE-bench Verified task, Command A costs $2.69 and MiMo-V2.5-Pro $0.03.
Which has the bigger context window, Command A or MiMo-V2.5-Pro?
MiMo-V2.5-Pro, with 1.05M tokens against 256K.
How many benchmarks is the Command A vs MiMo-V2.5-Pro comparison based on?
51 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and MiMo-V2.5-Pro's on 4. Where only one model has a result, the page says so instead of naming a winner.