Command A vs GPT-5.2
Updated October 21, 2026. 54 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only GPT-5.2 has a coding score, 55.9 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding.
Command A has the edge where output tokens cost 1.4x less.
- Shared results
- 54
- Command A only
- 6
- GPT-5.2 only
- 57
- Like-for-like categories
- 7 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
Only GPT-5.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.2
GPT-5.2 leads, 30.4 against 13.1.
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
GPT-5.2
GPT-5.2 leads, 91.7% against 48.5%.
Same board, both measured
Math
AIME
GPT-5.2
GPT-5.2 leads, 96.9% against 13.3%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.2
GPT-5.2 leads, 86.2% against 69.2%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.2 has a Design Arena: websites result, 1195.
Directional only
Long documents
Listed context window
GPT-5.2
GPT-5.2 takes 400K tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command A
Command A $0.0075, GPT-5.2 $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.2
Command A $2.69, GPT-5.2 $1.91.
Published attempt cost and pass rate
At a glance
- Coding index
- Command A not benchmarked, GPT-5.2 55.9 from 3 benchmarks.
- Widest category
- Math, 51.5 points apart on average over 6 shared benchmarks.
- Widest single gap
- AA Math Index: Command A 13.0 against GPT-5.2 99.0, 86.0 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against GPT-5.2 $14.00 per million output tokens, 1.4x apart.
- Context window
- Command A takes 256K tokens, GPT-5.2 400K.
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-5.2 | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 6 | ||
| Knowledge | 0 | 12 | ||
| General | 0 | 22 | ||
| All shared boards | 0 | 54 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | Command A | GPT-5.2 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 72.8% | Only GPT-5.2 measured |
| SWE-bench Multilingual | Not measured | 66.7% | Only GPT-5.2 measured |
| LMArena WebDev | Not measured | 1417 | Only GPT-5.2 measured |
| AA Coding Index | 27.8 | Not measured | Only Command A measured |
| LiveCodeBench (Vals AI) | 35.1% | 85.4% | GPT-5.2 |
| SWE-bench Verified (Vals AI) | 7.8% | 75.8% | GPT-5.2 |
| Vibe Code Bench (Vals AI) | Not measured | 53.5% | Only GPT-5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | Not measured | Only Command A measured |
| LiveCodeBench (Artificial Analysis run) | 28.7% | 89.4% | GPT-5.2 |
| SciCode | 38.5% | Not measured | Only Command A measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 47.0% | GPT-5.2 |
| Terminal-Bench | Not measured | 64.9% | Only GPT-5.2 measured |
| GSO-Bench | Not measured | 27.5% | Only GPT-5.2 measured |
| WeirdML | Not measured | 72.2% | Only GPT-5.2 measured |
| ALE-Bench | Not measured | 1293.5 | Only GPT-5.2 measured |
| LMArena Text: coding | 1330 | 1442 | GPT-5.2 |
| LMArena Text: software and IT | 1336 | 1434 | GPT-5.2 |
| LMArena WebDev: HTML | Not measured | 1427 | Only GPT-5.2 measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | GPT-5.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 30.4 | GPT-5.2 |
| Epoch Capabilities Index | Not measured | 153.7 | Only GPT-5.2 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | GPT-5.2 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Command A measured |
| Design Arena: agents building web apps | Not measured | 1086 | Only GPT-5.2 measured |
| Design Arena: agents building full-stack apps | Not measured | 1043 | Only GPT-5.2 measured |
| Design Arena: agents building mobile apps | Not measured | 1093 | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | Not measured | 1066 | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GPT-5.2 measured |
| Finance Agent (Vals AI) | 9.0% | Not measured | Only Command A measured |
| τ²-Bench Telecom | 80.7% | 84.8% | GPT-5.2 |
| METR time horizons | Not measured | 75.3% | Only GPT-5.2 measured |
| Remote Labor Index | Not measured | 2.5% | Only GPT-5.2 measured |
| GDPval | Not measured | 49.7% | Only GPT-5.2 measured |
| DeepResearch Bench | Not measured | 41.1% | Only GPT-5.2 measured |
| Vending-Bench 2 | Not measured | $3,591 | Only GPT-5.2 measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Command A | GPT-5.2 | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | 91.7% | GPT-5.2 |
| MMMU Pro (Vals AI) | Not measured | 86.7% | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 90.3% | GPT-5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 37.7% | GPT-5.2 |
| GPQA Diamond (Epoch AI run) | Not measured | 91.4% | Only GPT-5.2 measured |
| ARC-AGI | Not measured | 86.2% | Only GPT-5.2 measured |
| ARC-AGI-2 | Not measured | 52.9% | Only GPT-5.2 measured |
| SimpleBench | Not measured | 45.8% | Only GPT-5.2 measured |
| Chess puzzles | Not measured | 48.0% | Only GPT-5.2 measured |
| Mystery game puzzles | Not measured | 23.0% | Only GPT-5.2 measured |
| EBR-bench | Not measured | 23.0% | Only GPT-5.2 measured |
| VPCT | Not measured | 84.0% | Only GPT-5.2 measured |
| LMArena Text: hard prompts | 1326 | 1428 | GPT-5.2 |
| LMArena Text: expert | 1296 | 1445 | GPT-5.2 |
| LMArena Text: hard prompts in English | 1335 | 1431 | GPT-5.2 |
| LMArena Vision: diagrams | Not measured | 1265 | Only GPT-5.2 measured |
| LMArena Vision: homework | Not measured | 1276 | Only GPT-5.2 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Command A | GPT-5.2 | Leader |
|---|---|---|---|
| AIME | 13.3% | 96.9% | GPT-5.2 |
| MATH 500 (Vals AI) | 76.2% | Not measured | Only Command A measured |
| MGSM (Vals AI) | 85.7% | 94.0% | GPT-5.2 |
| AA Math Index | 13.0 | 99.0 | GPT-5.2 |
| AIME 2025 (Artificial Analysis run) | 13.0% | 99.0% | GPT-5.2 |
| OTIS Mock AIME | Not measured | 96.1% | Only GPT-5.2 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 67.4% | Only GPT-5.2 measured |
| FrontierMath, tier 4 | Not measured | 31.7% | Only GPT-5.2 measured |
| ProofBench | Not measured | 15.0% | Only GPT-5.2 measured |
| LMArena Text: math | 1300 | 1440 | GPT-5.2 |
| LMArena Text: mathematical work | 1312 | 1442 | GPT-5.2 |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index012
| Benchmark | Command A | GPT-5.2 | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | 86.2% | GPT-5.2 |
| LegalBench (Vals AI) | 79.7% | 82.8% | GPT-5.2 |
| CaseLaw (Vals AI) | 64.5% | 66.0% | GPT-5.2 |
| CorpFin (Vals AI) | 46.0% | 65.9% | GPT-5.2 |
| TaxEval (Vals AI) | 61.4% | 75.8% | GPT-5.2 |
| MortgageTax (Vals AI) | Not measured | 67.1% | Only GPT-5.2 measured |
| MedQA (Vals AI) | 80.5% | 94.1% | GPT-5.2 |
| MedCode (Vals AI) | 19.7% | 49.8% | GPT-5.2 |
| MMLU Pro (Artificial Analysis run) | 71.2% | 87.4% | GPT-5.2 |
| SimpleQA Verified | Not measured | 37.1% | Only GPT-5.2 measured |
| LMArena Text: science | 1339 | 1425 | GPT-5.2 |
| LMArena Text: medicine and healthcare | 1334 | 1425 | GPT-5.2 |
| LMArena Text: legal and government | 1348 | 1427 | GPT-5.2 |
| LMArena Text: business and finance | 1315 | 1414 | GPT-5.2 |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Command A | GPT-5.2 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1195 | Only GPT-5.2 measured |
| Design Arena: UI components | Not measured | 1190 | Only GPT-5.2 measured |
| Design Arena: game dev | Not measured | 1195 | Only GPT-5.2 measured |
| Design Arena: data viz | Not measured | 1202 | Only GPT-5.2 measured |
| Design Arena: 3D | Not measured | 1079 | Only GPT-5.2 measured |
| Design Arena: all code categories | Not measured | 1170 | Only GPT-5.2 measured |
| Design Arena: SVG | Not measured | 1139 | Only GPT-5.2 measured |
| Design Arena: ASCII art | Not measured | 1152 | Only GPT-5.2 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 | GPT-5.2 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 49.3% | Only GPT-5.2 measured |
| MedScribe (Vals AI) | 55.7% | 84.4% | GPT-5.2 |
| IFBench | 73.9% | 75.4% | GPT-5.2 |
| Long context reasoning (AA-LCR) | 52.7% | 82.7% | GPT-5.2 |
| DTBench | 61.3% | 90.9% | GPT-5.2 |
| LMCA | 10.3% | 43.9% | GPT-5.2 |
| CL-bench | Not measured | 18.2% | Only GPT-5.2 measured |
| Furniture assembly | Not measured | 38.3% | Only GPT-5.2 measured |
| LMArena Text | 1331 | 1416 | GPT-5.2 |
| LMArena Text: instruction following | 1309 | 1409 | GPT-5.2 |
| LMArena Text: creative writing | 1319 | 1376 | GPT-5.2 |
| LMArena Vision | Not measured | 1246 | Only GPT-5.2 measured |
| LMArena Text: English | 1344 | 1422 | GPT-5.2 |
| LMArena Text: non-English | 1313 | 1405 | GPT-5.2 |
| LMArena Text: Chinese | 1327 | 1447 | GPT-5.2 |
| LMArena Text: German | 1342 | 1444 | GPT-5.2 |
| LMArena Text: Japanese | 1285 | 1420 | GPT-5.2 |
| LMArena Text: Korean | 1285 | 1374 | GPT-5.2 |
| LMArena Text: French | 1351 | 1454 | GPT-5.2 |
| LMArena Text: Spanish | 1347 | 1413 | GPT-5.2 |
| LMArena Text: Russian | 1314 | 1415 | GPT-5.2 |
| LMArena Text: Polish | 1324 | 1426 | GPT-5.2 |
| LMArena Text: multi-turn | 1338 | 1419 | GPT-5.2 |
| LMArena Text: longer queries | 1334 | 1413 | GPT-5.2 |
| LMArena Text: writing and language | 1328 | 1392 | GPT-5.2 |
| LMArena Text: entertainment and media | 1306 | 1378 | GPT-5.2 |
| LMArena Vision: English | Not measured | 1243 | Only GPT-5.2 measured |
| LMArena Vision: Chinese | Not measured | 1305 | Only GPT-5.2 measured |
| LMArena Vision: reading text | Not measured | 1257 | Only GPT-5.2 measured |
| LMArena Vision: humor | Not measured | 1227 | Only GPT-5.2 measured |
| LMArena Vision: captioning | Not measured | 1267 | Only GPT-5.2 measured |
| LMArena Vision: recognizing things | Not measured | 1232 | Only GPT-5.2 measured |
| LMArena Vision: creative writing | Not measured | 1225 | Only GPT-5.2 measured |
| LMArena Document | Not measured | 1405 | Only GPT-5.2 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | GPT-5.2 | 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
GPT-5.2 costs less
Agent loop
200K input + 10K output tokens
GPT-5.2 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-5.2 |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | December 10, 2025 |
| Context window | 256K | 400K |
| Input $/Mtok | $2.50 | $1.75 |
| Output $/Mtok | $10.00 | $14.00 |
| Coding index | Not benchmarked | 55.9 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | $2.69 | $1.91 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than GPT-5.2 for coding?
Only GPT-5.2 has a coding score, 55.9 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding. Command A has the edge where output tokens cost 1.4x less.
Which is cheaper, Command A or GPT-5.2?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; GPT-5.2 costs $1.75 and $14.00. Per solved SWE-bench Verified task, Command A costs $2.69 and GPT-5.2 $1.91.
Which has the bigger context window, Command A or GPT-5.2?
GPT-5.2, with 400K tokens against 256K.
How many benchmarks is the Command A vs GPT-5.2 comparison based on?
54 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and GPT-5.2's on 3. Where only one model has a result, the page says so instead of naming a winner.