Command A vs GPT-4o-mini
Updated October 21, 2026. 20 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Command A has the edge where the context window is larger, 256K against 128K tokens, and where what matters is MGSM (Vals AI) or AA Math Index, where it leads.
- Shared results
- 20
- Command A only
- 40
- GPT-4o-mini only
- 7
- Like-for-like categories
- 6 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Command A
Command A leads, 13.1 against 6.7.
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 44.2%.
Same board, both measured
Math
AIME
Command A
Command A leads, 13.3% against 11.5%.
Same board, both measured
Knowledge
MMLU Pro
Command A
Command A leads, 69.2% against 62.7%.
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
GPT-4o-mini
Command A $0.0075, GPT-4o-mini $0.0004 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, GPT-4o-mini not benchmarked.
- Widest category
- General, 16.7 points apart on average over 3 shared benchmarks.
- Widest single gap
- IFBench: Command A 73.9% against GPT-4o-mini 31.0%, 43.0 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against GPT-4o-mini $0.60 per million output tokens, 16.7x apart.
- Context window
- Command A takes 256K tokens, GPT-4o-mini 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 | GPT-4o-mini | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 2 | 3 | ||
| Knowledge | 5 | 0 | ||
| General | 2 | 1 | ||
| All shared boards | 16 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | Command A | GPT-4o-mini | Leader |
|---|---|---|---|
| AA Coding Index | 27.8 | 11.4 | Command A |
| LiveCodeBench (Vals AI) | 35.1% | 26.4% | Command A |
| 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% | 23.4% | 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 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Command A | GPT-4o-mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 6.7 | Command A |
| Epoch Capabilities Index | Not measured | 126.5 | Only GPT-4o-mini measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Command A | GPT-4o-mini | 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 |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Command A | GPT-4o-mini | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | 44.2% | Command A |
| MMMU Pro (Vals AI) | Not measured | 56.6% | Only GPT-4o-mini measured |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 42.6% | Command A |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 4.2% | Command A |
| ARC-AGI-2 | Not measured | 0.0% | Only GPT-4o-mini 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 |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | Command A | GPT-4o-mini | Leader |
|---|---|---|---|
| AIME | 13.3% | 11.5% | Command A |
| MATH 500 (Vals AI) | 76.2% | 72.6% | Command A |
| MGSM (Vals AI) | 85.7% | 86.2% | GPT-4o-mini |
| AA Math Index | 13.0 | 14.7 | GPT-4o-mini |
| AIME 2025 (Artificial Analysis run) | 13.0% | 14.7% | GPT-4o-mini |
| GSM8K | Not measured | 91.3% | Only GPT-4o-mini 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 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Command A | GPT-4o-mini | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | 62.7% | Command A |
| 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% | 45.5% | Command A |
| TaxEval (Vals AI) | 61.4% | 60.5% | Command A |
| MortgageTax (Vals AI) | Not measured | 54.5% | Only GPT-4o-mini measured |
| MedQA (Vals AI) | 80.5% | 72.4% | Command A |
| MedCode (Vals AI) | 19.7% | Not measured | Only Command A measured |
| MMLU Pro (Artificial Analysis run) | 71.2% | 64.8% | Command A |
| MMLU | Not measured | 81.8% | Only GPT-4o-mini 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 3 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | GPT-4o-mini | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | Command A | GPT-4o-mini | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 55.7% | Not measured | Only Command A measured |
| IFBench | 73.9% | 31.0% | Command A |
| Long context reasoning (AA-LCR) | 52.7% | Not measured | Only Command A measured |
| DTBench | 61.3% | 54.4% | Command A |
| LMCA | 10.3% | 10.4% | GPT-4o-mini |
| PIQA | Not measured | 88.7% | Only GPT-4o-mini 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 | GPT-4o-mini | 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
GPT-4o-mini costs less
Repository review
50K input + 3K output tokens
GPT-4o-mini costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini 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 | OpenAI GPT-4o-mini |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | July 18, 2024 |
| Context window | 256K | 128K |
| Input $/Mtok | $2.50 | $0.15 |
| Output $/Mtok | $10.00 | $0.60 |
| 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 GPT-4o-mini for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Command A has the edge where the context window is larger, 256K against 128K tokens, and where what matters is MGSM (Vals AI) or AA Math Index, where it leads.
Which is cheaper, Command A or GPT-4o-mini?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; GPT-4o-mini costs $0.15 and $0.60. Cost per solved task is known for Command A only.
Which has the bigger context window, Command A or GPT-4o-mini?
Command A, with 256K tokens against 128K.
How many benchmarks is the Command A vs GPT-4o-mini comparison based on?
20 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and GPT-4o-mini's on 0. Where only one model has a result, the page says so instead of naming a winner.