Command A vs GPT-4o
Command A and GPT-4o cost about the same per output token ($10.00 vs $10.00 per million). Updated October 21, 2026; 12 benchmarks have a result for both.
Command A or GPT-4o: 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 (Artificial Analysis run) or MMLU Pro (Artificial Analysis run), where it leads.
- Shared results
- 12
- Command A only
- 50
- GPT-4o only
- 15
- 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
No call
Only Command A has a AA Intelligence Index result, 13.1.
Directional only
Agentic work
AA Agentic Index
No call
Only Command A has a AA Agentic Index result, 1.0.
Directional only
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 GPT-4o has a Design Arena: websites result, 831.
Directional only
Long documents
Listed context window
Command A
Command A takes 256K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
No call
Command A $0.0075, GPT-4o $0.0075 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 not benchmarked.
- Widest category
- Agentic work, 55.6 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Command A 80.7% against GPT-4o 25.1%, 55.6 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, GPT-4o 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 | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 0 | 1 | ||
| General | 2 | 2 | ||
| All shared boards | 8 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | Command A | GPT-4o | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 38.8% | Only GPT-4o measured |
| SWE-bench Lite | Not measured | 38.0% | Only GPT-4o measured |
| SWE-bench (full test set) | Not measured | 18.8% | Only GPT-4o measured |
| SWE-bench Multimodal | Not measured | 30.4% | Only GPT-4o measured |
| AA Coding Index | 27.8 | Not measured | Only Command A measured |
| LiveCodeBench (Vals AI) | 35.1% | Not measured | Only Command A measured |
| 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% | 30.9% | GPT-4o |
| SciCode | 38.5% | Not measured | Only Command A measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 8.3% | Command A |
| LMArena Text: coding | 1330 | Not measured | Only Command A measured |
| LMArena Text: software and IT | 1336 | Not measured | Only Command A measured |
| SciCode | Not measured | 1.5% | Only GPT-4o measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Command A | GPT-4o | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | Not measured | Only Command A measured |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Command A | GPT-4o | 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% | 25.1% | Command A |
| GDPval | Not measured | 9.9% | Only GPT-4o measured |
| Berkeley Function Calling Leaderboard (BFCL) | 46.5% | 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 index20
| Benchmark | Command A | GPT-4o | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | Not measured | Only Command A measured |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 54.3% | Command A |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 2.4% | Command A |
| ARC-AGI | Not measured | 4.5% | Only GPT-4o measured |
| ARC-AGI-2 | Not measured | 0.0% | Only GPT-4o 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 |
| Kagi LLM Benchmark | 28.8% | 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 index20
| Benchmark | Command A | GPT-4o | 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 | 6.0 | Command A |
| AIME 2025 (Artificial Analysis run) | 13.0% | 6.0% | Command A |
| 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 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | GPT-4o | 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% | Not measured | Only Command A measured |
| CorpFin (Vals AI) | 46.0% | Not measured | Only Command A measured |
| TaxEval (Vals AI) | 61.4% | Not measured | Only Command A measured |
| MedQA (Vals AI) | 80.5% | Not measured | Only Command A measured |
| MedCode (Vals AI) | 19.7% | Not measured | Only Command A measured |
| MMLU Pro (Artificial Analysis run) | 71.2% | 74.8% | GPT-4o |
| MMLU | Not measured | 88.1% | Only GPT-4o 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 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Command A | GPT-4o | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 831 | Only GPT-4o measured |
| Design Arena: UI components | Not measured | 885 | Only GPT-4o measured |
| Design Arena: game dev | Not measured | 906 | Only GPT-4o measured |
| Design Arena: data viz | Not measured | 858 | Only GPT-4o measured |
| Design Arena: 3D | Not measured | 869 | Only GPT-4o measured |
| Design Arena: all code categories | Not measured | 861 | Only GPT-4o measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | Command A | GPT-4o | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 55.7% | Not measured | Only Command A measured |
| IFBench | 73.9% | 34.3% | Command A |
| Long context reasoning (AA-LCR) | 52.7% | 49.3% | Command A |
| DTBench | 61.3% | 64.5% | GPT-4o |
| LMCA | 10.3% | 16.6% | GPT-4o |
| 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 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | GPT-4o | 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
Same cost
Repository review
50K input + 3K output tokens
Same cost
Agent loop
200K input + 10K output tokens
Same cost
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 |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | May 13, 2024 |
| Context window | 256K | 128K |
| Input $/Mtok | $2.50 | $2.50 |
| Output $/Mtok | $10.00 | $10.00 |
| 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 GPT-4o 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 (Artificial Analysis run) or MMLU Pro (Artificial Analysis run), where it leads.
Which is cheaper, Command A or GPT-4o?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; GPT-4o costs $2.50 and $10.00. Cost per solved task is known for Command A only.
Which has the bigger context window, Command A or GPT-4o?
Command A, with 256K tokens against 128K.
How many benchmarks is the Command A vs GPT-4o comparison based on?
12 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's on 0. Where only one model has a result, the page says so instead of naming a winner.