Command A vs GPT-4 Turbo
Updated October 21, 2026. 37 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 output tokens cost 3.0x less, and where the context window is larger, 256K against 128K tokens.
- Shared results
- 37
- Command A only
- 23
- GPT-4 Turbo only
- 5
- 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 7.0.
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
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
Long documents
Listed context window
Command A
Command A takes 256K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command A
Command A $0.0075, GPT-4 Turbo $0.025 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-4 Turbo not benchmarked.
- Widest category
- Reasoning, 11.3 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena Text: Korean: Command A 1285 against GPT-4 Turbo 1187, 16.4 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against GPT-4 Turbo $30.00 per million output tokens, 3.0x apart.
- Context window
- Command A takes 256K tokens, GPT-4 Turbo 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-4 Turbo | Ties |
|---|---|---|---|---|
| Coding | 3 | 1 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 5 | 2 | ||
| General | 18 | 1 | ||
| All shared boards | 33 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index31
| Benchmark | Command A | GPT-4 Turbo | Leader |
|---|---|---|---|
| AA Coding Index | 27.8 | 21.5 | Command A |
| 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% | 29.1% | GPT-4 Turbo |
| 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 | 1268 | Command A |
| LMArena Text: software and IT | 1336 | 1267 | Command A |
| 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-4 Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 7.0 | Command A |
| Epoch Capabilities Index | Not measured | 127.3 | Only GPT-4 Turbo measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Command A | GPT-4 Turbo | 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 index40
| Benchmark | Command A | GPT-4 Turbo | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | Not measured | Only Command A measured |
| GPQA Diamond (Artificial Analysis run) | 76.1% | Not measured | Only Command A measured |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 3.1% | Command A |
| LMArena Text: hard prompts | 1326 | 1251 | Command A |
| LMArena Text: expert | 1296 | 1223 | Command A |
| LMArena Text: hard prompts in English | 1335 | 1266 | Command A |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Command A | GPT-4 Turbo | 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 |
| LMArena Text: math | 1300 | 1272 | Command A |
| LMArena Text: mathematical work | 1312 | 1268 | Command A |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index52
| Benchmark | Command A | GPT-4 Turbo | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | Not measured | Only Command A measured |
| LegalBench (Vals AI) | 79.7% | 80.5% | GPT-4 Turbo |
| 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% | 82.0% | GPT-4 Turbo |
| MedCode (Vals AI) | 19.7% | Not measured | Only Command A measured |
| MMLU Pro (Artificial Analysis run) | 71.2% | 69.4% | Command A |
| MMLU | Not measured | 81.3% | Only GPT-4 Turbo measured |
| LMArena Text: science | 1339 | 1256 | Command A |
| LMArena Text: medicine and healthcare | 1334 | 1239 | Command A |
| LMArena Text: legal and government | 1348 | 1265 | Command A |
| LMArena Text: business and finance | 1315 | 1222 | Command A |
| Neither model has a result on the other 4 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | GPT-4 Turbo | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index181
| Benchmark | Command A | GPT-4 Turbo | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 55.7% | Not measured | Only Command A measured |
| IFBench | 73.9% | Not measured | Only Command A measured |
| Long context reasoning (AA-LCR) | 52.7% | Not measured | Only Command A measured |
| DTBench | 61.3% | 61.6% | GPT-4 Turbo |
| LMCA | 10.3% | 9.8% | Command A |
| LMArena Text | 1331 | 1272 | Command A |
| LMArena Text: instruction following | 1309 | 1249 | Command A |
| LMArena Text: creative writing | 1319 | 1268 | Command A |
| LMArena Vision | Not measured | 1090 | Only GPT-4 Turbo measured |
| LMArena Text: English | 1344 | 1291 | Command A |
| LMArena Text: non-English | 1313 | 1245 | Command A |
| LMArena Text: Chinese | 1327 | 1242 | Command A |
| LMArena Text: German | 1342 | 1259 | Command A |
| LMArena Text: Japanese | 1285 | 1194 | Command A |
| LMArena Text: Korean | 1285 | 1187 | Command A |
| LMArena Text: French | 1351 | 1276 | Command A |
| LMArena Text: Spanish | 1347 | 1260 | Command A |
| LMArena Text: Russian | 1314 | 1259 | Command A |
| LMArena Text: Polish | 1324 | 1302 | Command A |
| LMArena Text: multi-turn | 1338 | 1267 | Command A |
| LMArena Text: longer queries | 1334 | 1254 | Command A |
| LMArena Text: writing and language | 1328 | 1279 | Command A |
| LMArena Text: entertainment and media | 1306 | 1258 | Command A |
| LMArena Vision: English | Not measured | 1097 | Only GPT-4 Turbo measured |
| LMArena Vision: Chinese | Not measured | 1045 | Only GPT-4 Turbo measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | GPT-4 Turbo | 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
Command A costs less
Repository review
50K input + 3K output tokens
Command A costs less
Agent loop
200K input + 10K output tokens
Command A 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-4 Turbo |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | April 9, 2024 |
| Context window | 256K | 128K |
| Input $/Mtok | $2.50 | $10.00 |
| Output $/Mtok | $10.00 | $30.00 |
| 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-4 Turbo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Command A has the edge where output tokens cost 3.0x less, and where the context window is larger, 256K against 128K tokens.
Which is cheaper, Command A or GPT-4 Turbo?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; GPT-4 Turbo costs $10.00 and $30.00. Cost per solved task is known for Command A only.
Which has the bigger context window, Command A or GPT-4 Turbo?
Command A, with 256K tokens against 128K.
How many benchmarks is the Command A vs GPT-4 Turbo comparison based on?
37 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and GPT-4 Turbo's on 0. Where only one model has a result, the page says so instead of naming a winner.