Command A vs Gemini 2.5 Pro
Gemini 2.5 Pro leads Command A on the Artificial Analysis Intelligence Index, 16.1 to 13.1, at about the same output price ($10.00 vs $10.00 per million). Updated October 22, 2026; 50 benchmarks have a result for both.
Command A or Gemini 2.5 Pro: which to pick
Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding.
Pick Command A for work measured by τ²-Bench Telecom or CaseLaw (Vals AI), where it leads.
- Shared results
- 50
- Command A only
- 12
- Gemini 2.5 Pro only
- 40
- 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 Gemini 2.5 Pro has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Gemini 2.5 Pro
Gemini 2.5 Pro leads, 16.1 against 13.1.
Same board, both measured
Agentic work
AA Agentic Index
Gemini 2.5 Pro
Gemini 2.5 Pro leads, 1.6 against 1.0.
Same board, both measured
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
Web design
Design Arena: websites
No call
Only Gemini 2.5 Pro has a Design Arena: websites result, 1167.
Directional only
Long documents
Listed context window
Gemini 2.5 Pro
Gemini 2.5 Pro takes 1.05M tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 2.5 Pro
Command A $0.0075, Gemini 2.5 Pro $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Gemini 2.5 Pro
Command A $2.69, Gemini 2.5 Pro $0.64.
Published attempt cost and pass rate
At a glance
- Coding index
- Command A not benchmarked, Gemini 2.5 Pro 19.1 from 3 benchmarks.
- Widest category
- Math, 49.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- AA Math Index: Command A 13.0 against Gemini 2.5 Pro 87.7, 74.7 points apart on a 0 to 100 scale.
- Output price
- Both cost $10.00 per million output tokens.
- Context window
- Command A takes 256K tokens, Gemini 2.5 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 | Gemini 2.5 Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 4 | ||
| Knowledge | 1 | 7 | ||
| General | 1 | 21 | ||
| All shared boards | 3 | 47 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
| Benchmark | Command A | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 53.6% | Only Gemini 2.5 Pro measured |
| LMArena WebDev | Not measured | 1226 | Only Gemini 2.5 Pro measured |
| AA Coding Index | 27.8 | 33.3 | Gemini 2.5 Pro |
| LiveCodeBench (Vals AI) | 35.1% | Not measured | Only Command A measured |
| SWE-bench Verified (Vals AI) | 7.8% | 54.4% | Gemini 2.5 Pro |
| Vibe Code Bench (Vals AI) | Not measured | 0.4% | Only Gemini 2.5 Pro measured |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | Not measured | Only Command A measured |
| LiveCodeBench (Artificial Analysis run) | 28.7% | 80.1% | Gemini 2.5 Pro |
| SciCode | 38.5% | 46.3% | Gemini 2.5 Pro |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 26.5% | Gemini 2.5 Pro |
| Terminal-Bench | Not measured | 32.6% | Only Gemini 2.5 Pro measured |
| GSO-Bench | Not measured | 3.9% | Only Gemini 2.5 Pro measured |
| WeirdML | Not measured | 54.0% | Only Gemini 2.5 Pro measured |
| LMArena Text: coding | 1330 | 1452 | Gemini 2.5 Pro |
| LMArena Text: software and IT | 1336 | 1459 | Gemini 2.5 Pro |
| LMArena WebDev: HTML | Not measured | 1235 | Only Gemini 2.5 Pro measured |
| LMArena WebDev: from an image | Not measured | 1285 | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 16.1 | Gemini 2.5 Pro |
| Epoch Capabilities Index | Not measured | 145.1 | Only Gemini 2.5 Pro measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Command A | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | 1.6 | Gemini 2.5 Pro |
| Finance Agent (Vals AI) | 9.0% | Not measured | Only Command A measured |
| τ²-Bench Telecom | 80.7% | 54.1% | Command A |
| GDPval | Not measured | 23.3% | Only Gemini 2.5 Pro measured |
| DeepResearch Bench | Not measured | 41.5% | Only Gemini 2.5 Pro measured |
| Vending-Bench 2 | Not measured | $574 | Only Gemini 2.5 Pro measured |
| Berkeley Function Calling Leaderboard (BFCL) | 46.5% | Not measured | Only Command A measured |
| BrowseComp-Plus | Not measured | 29.5% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Command A | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | Not measured | Only Command A measured |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 84.4% | Gemini 2.5 Pro |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 22.5% | Gemini 2.5 Pro |
| GPQA Diamond (Epoch AI run) | Not measured | 85.3% | Only Gemini 2.5 Pro measured |
| ARC-AGI | Not measured | 41.0% | Only Gemini 2.5 Pro measured |
| ARC-AGI-2 | Not measured | 4.9% | Only Gemini 2.5 Pro measured |
| LMArena Text: hard prompts | 1326 | 1455 | Gemini 2.5 Pro |
| LMArena Text: expert | 1296 | 1452 | Gemini 2.5 Pro |
| LMArena Text: hard prompts in English | 1335 | 1453 | Gemini 2.5 Pro |
| LMArena Vision: diagrams | Not measured | 1274 | Only Gemini 2.5 Pro measured |
| LMArena Vision: homework | Not measured | 1281 | Only Gemini 2.5 Pro measured |
| Kagi LLM Benchmark | 28.8% | 70.3% | Gemini 2.5 Pro |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Command A | Gemini 2.5 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 | 87.7 | Gemini 2.5 Pro |
| AIME 2025 (Artificial Analysis run) | 13.0% | 87.7% | Gemini 2.5 Pro |
| OTIS Mock AIME | Not measured | 84.7% | Only Gemini 2.5 Pro measured |
| FrontierMath, tiers 1 to 3 | Not measured | 24.6% | Only Gemini 2.5 Pro measured |
| FrontierMath, tier 4 | Not measured | 0.0% | Only Gemini 2.5 Pro measured |
| LMArena Text: math | 1300 | 1450 | Gemini 2.5 Pro |
| LMArena Text: mathematical work | 1312 | 1454 | Gemini 2.5 Pro |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | Command A | Gemini 2.5 Pro | 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% | 63.9% | Command A |
| CorpFin (Vals AI) | 46.0% | 60.8% | Gemini 2.5 Pro |
| TaxEval (Vals AI) | 61.4% | Not measured | Only Command A measured |
| MortgageTax (Vals AI) | Not measured | 68.9% | Only Gemini 2.5 Pro measured |
| MedQA (Vals AI) | 80.5% | Not measured | Only Command A measured |
| MedCode (Vals AI) | 19.7% | 50.6% | Gemini 2.5 Pro |
| MMLU Pro (Artificial Analysis run) | 71.2% | 86.2% | Gemini 2.5 Pro |
| LMArena Text: science | 1339 | 1480 | Gemini 2.5 Pro |
| LMArena Text: medicine and healthcare | 1334 | 1476 | Gemini 2.5 Pro |
| LMArena Text: legal and government | 1348 | 1480 | Gemini 2.5 Pro |
| LMArena Text: business and finance | 1315 | 1439 | Gemini 2.5 Pro |
| FACTS Benchmark Suite | Not measured | 61.5% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Command A | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1167 | Only Gemini 2.5 Pro measured |
| Design Arena: UI components | Not measured | 1137 | Only Gemini 2.5 Pro measured |
| Design Arena: game dev | Not measured | 1111 | Only Gemini 2.5 Pro measured |
| Design Arena: data viz | Not measured | 1222 | Only Gemini 2.5 Pro measured |
| Design Arena: 3D | Not measured | 1080 | Only Gemini 2.5 Pro measured |
| Design Arena: all code categories | Not measured | 1152 | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index121
| Benchmark | Command A | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 41.9% | Only Gemini 2.5 Pro measured |
| MedScribe (Vals AI) | 55.7% | 73.5% | Gemini 2.5 Pro |
| IFBench | 73.9% | 48.7% | Command A |
| Long context reasoning (AA-LCR) | 52.7% | 69.0% | Gemini 2.5 Pro |
| DTBench | 61.3% | 82.4% | Gemini 2.5 Pro |
| LMCA | 10.3% | 34.8% | Gemini 2.5 Pro |
| Creative writing (Lech Mazur) | Not measured | 83.8% | Only Gemini 2.5 Pro measured |
| LMArena Text | 1331 | 1458 | Gemini 2.5 Pro |
| LMArena Text: instruction following | 1309 | 1437 | Gemini 2.5 Pro |
| LMArena Text: creative writing | 1319 | 1454 | Gemini 2.5 Pro |
| LMArena Vision | Not measured | 1263 | Only Gemini 2.5 Pro measured |
| LMArena Text: English | 1344 | 1458 | Gemini 2.5 Pro |
| LMArena Text: non-English | 1313 | 1452 | Gemini 2.5 Pro |
| LMArena Text: Chinese | 1327 | 1507 | Gemini 2.5 Pro |
| LMArena Text: German | 1342 | 1487 | Gemini 2.5 Pro |
| LMArena Text: Japanese | 1285 | 1461 | Gemini 2.5 Pro |
| LMArena Text: Korean | 1285 | 1434 | Gemini 2.5 Pro |
| LMArena Text: French | 1351 | 1473 | Gemini 2.5 Pro |
| LMArena Text: Spanish | 1347 | 1474 | Gemini 2.5 Pro |
| LMArena Text: Russian | 1314 | 1461 | Gemini 2.5 Pro |
| LMArena Text: Polish | 1324 | 1479 | Gemini 2.5 Pro |
| LMArena Text: multi-turn | 1338 | 1453 | Gemini 2.5 Pro |
| LMArena Text: longer queries | 1334 | 1449 | Gemini 2.5 Pro |
| LMArena Text: writing and language | 1328 | 1454 | Gemini 2.5 Pro |
| LMArena Text: entertainment and media | 1306 | 1441 | Gemini 2.5 Pro |
| LMArena Vision: English | Not measured | 1261 | Only Gemini 2.5 Pro measured |
| LMArena Vision: Chinese | Not measured | 1300 | Only Gemini 2.5 Pro measured |
| LMArena Vision: reading text | Not measured | 1270 | Only Gemini 2.5 Pro measured |
| LMArena Vision: humor | Not measured | 1262 | Only Gemini 2.5 Pro measured |
| LMArena Vision: captioning | Not measured | 1304 | Only Gemini 2.5 Pro measured |
| LMArena Vision: recognizing things | Not measured | 1283 | Only Gemini 2.5 Pro measured |
| LMArena Vision: creative writing | Not measured | 1265 | Only Gemini 2.5 Pro measured |
| LMArena Document | Not measured | 1421 | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | Gemini 2.5 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
Gemini 2.5 Pro costs less
Repository review
50K input + 3K output tokens
Gemini 2.5 Pro costs less
Agent loop
200K input + 10K output tokens
Gemini 2.5 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 | Google Gemini 2.5 Pro |
|---|---|---|
| Maker | Cohere | |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | June 17, 2025 |
| Context window | 256K | 1.05M |
| Input $/Mtok | $2.50 | $1.25 |
| Output $/Mtok | $10.00 | $10.00 |
| Coding index | Not benchmarked | 19.1 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | $2.69 | $0.64 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than Gemini 2.5 Pro for coding?
Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding. Pick Command A for work measured by τ²-Bench Telecom or CaseLaw (Vals AI), where it leads.
Which is cheaper, Command A or Gemini 2.5 Pro?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Gemini 2.5 Pro costs $1.25 and $10.00. Per solved SWE-bench Verified task, Command A costs $2.69 and Gemini 2.5 Pro $0.64.
Which has the bigger context window, Command A or Gemini 2.5 Pro?
Gemini 2.5 Pro, with 1.05M tokens against 256K.
How many benchmarks is the Command A vs Gemini 2.5 Pro comparison based on?
50 benchmarks have a result for both models as of October 22, 2026. Command A's coding index rests on 1 independent benchmark and Gemini 2.5 Pro's on 3. Where only one model has a result, the page says so instead of naming a winner.