Model comparison
Command A vs GPT-5
GPT-5 is the stronger model overall, scoring 50.9 to 36.5 on the Noometry Index.
Last verified . 23 shared benchmarks.
CohereSummary
- They share 23 benchmarks with published results for both. Command A scores higher in 1 category and GPT-5 in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 40.6.
- The biggest single-benchmark swing is Aider Polyglot: 12% for Command A and 88% for GPT-5.
- GPT-5 is cheaper at $1.25 / $10 per million input/output tokens, against $2.50 / $10 for Command A.
- GPT-5 accepts more context: 400K tokens versus 256K.
- Command A has downloadable open weights; the other is API-only.
Side by side
| Command A | GPT-5 | |
|---|---|---|
| Provider | Cohere | OpenAI |
| Noometry Index | 36.5 | 50.9 |
| Released | 2025-03-13 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 256K | 400K |
| Max output | 8K | 128K |
| Input $ / M tokens | $2.50 | $1.25 |
| Output $ / M tokens | $10 | $10 |
| Results tracked | 24 | 69 |
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Category by category
Coding GPT-5 leads
Command A: 27.2 (#322), GPT-5: 50.3 (#47)
| Benchmark | Command A | GPT-5 |
|---|---|---|
| Aider Polyglot | 12% | 88% |
| LMArena Coding | 1330 | 1436 |
| SWE-bench Verified | — | 73.6% |
| SWE-bench Verified (bash only) | — | 65% |
| LMArena WebDev | — | 1418 |
| SciCode | — | 42.9% |
| GSO | — | 6.9% |
| WeirdML | — | 60.7% |
| ALE-Bench | — | 1,162 |
| AlgoTune | — | 1.67 |
Agentic & Tool Use Command A leads
Command A: 35.9 (#40), GPT-5: 33.1 (#56)
| Benchmark | Command A | GPT-5 |
|---|---|---|
| Terminal-Bench | — | 49.6% |
| Berkeley Function Calling Leaderboard | 57.1% | — |
| GDPval | — | 34.8% |
| Remote Labor Index | — | 1.7% |
| DeepResearch Bench | — | 49.6% |
| BALROG | — | 32.8% |
| LMArena Search | — | 1133 |
| METR Time Horizons | — | 69.6% |
Reasoning GPT-5 leads
Command A: 18.3 (#283), GPT-5: 38.3 (#64)
| Benchmark | Command A | GPT-5 |
|---|---|---|
| Kagi LLM Benchmark | 28.8% | 72.7% |
| LMArena Hard Prompts | 1326 | 1416 |
| DTBench | 61.3% | 90.7% |
| LMCA | 10.3% | 40% |
| ARC-AGI-2 | — | 9.9% |
| SimpleBench | — | 56.7% |
| ARC-AGI-1 | — | 65.7% |
| CritPt | — | 12.6% |
| Chess Puzzles | — | 37% |
| EnigmaEval | — | 10.5% |
| EBR-Bench | — | 12.7% |
| Mystery Game Puzzles | — | 23% |
| Epoch Capabilities Index | — | 150 |
| ForecastBench | — | 61.4 |
Math GPT-5 leads
Command A: 36.2 (#171), GPT-5: 55.0 (#44)
| Benchmark | Command A | GPT-5 |
|---|---|---|
| LMArena Math | 1300 | 1407 |
| FrontierMath (Tiers 1-3) | — | 55.4% |
| FrontierMath Tier 4 | — | 22% |
| OTIS Mock AIME 2024-2025 | — | 91.4% |
| ProofBench | — | 18% |
| Omni-MATH | — | 64.7% |
| MATH Level 5 | — | 98.1% |
| FrontierMath (Feb 2025 set) | — | 32.4% |
| FrontierMath Tier 4 (v1) | — | 12.5% |
Knowledge GPT-5 leads
Command A: 37.1 (#159), GPT-5: 56.6 (#43)
| Benchmark | Command A | GPT-5 |
|---|---|---|
| Vectara Hallucination Rate | 9.3% | 14.7% |
| LMArena Expert | 1295 | 1419 |
| GPQA Diamond | — | 86.2% |
| Humanity's Last Exam | — | 25.3% |
| SimpleQA Verified | — | 50.1% |
| MMLU-Pro | — | 86.3% |
| Confabulations | — | 10.3% |
| GPQA (HELM) | — | 79.2% |
Multimodal Not comparable
Command A: —, GPT-5: 46.8 (#13)
| Benchmark | Command A | GPT-5 |
|---|---|---|
| LMArena Vision | — | 1232 |
| GeoBench | — | 81% |
| VPCT | — | 66% |
Multilingual GPT-5 leads
Command A: 45.3 (#170), GPT-5: 51.4 (#110)
| Benchmark | Command A | GPT-5 |
|---|---|---|
| LMArena Non-English | 1313 | 1397 |
| LMArena Chinese | 1327 | 1422 |
| LMArena French | 1351 | 1410 |
| LMArena German | 1341 | 1416 |
| LMArena Japanese | 1285 | 1409 |
| LMArena Korean | 1285 | 1360 |
| LMArena Russian | 1314 | 1406 |
| LMArena Spanish | 1347 | 1399 |
Instruction Following GPT-5 leads
Command A: 69.1 (#177), GPT-5: 73.8 (#113)
| Benchmark | Command A | GPT-5 |
|---|---|---|
| LMArena Instruction Following | 1309 | 1388 |
| IFEval | — | 87.5% |
Long Context GPT-5 leads
Command A: 40.6 (#151), GPT-5: 69.5 (#2)
| Benchmark | Command A | GPT-5 |
|---|---|---|
| LMArena Longer Query | 1334 | 1399 |
| Fiction.LiveBench | — | 97.2% |
Writing & Preference GPT-5 leads
Command A: 47.6 (#208), GPT-5: 63.4 (#65)
| Benchmark | Command A | GPT-5 |
|---|---|---|
| LMArena Text | 1331 | 1406 |
| LMArena Creative Writing | 1319 | 1365 |
| EQ-Bench Creative Writing | 1145 | 1627 |
| LMArena Multi-Turn | 1339 | 1426 |
| Short-Story Creative Writing | — | 86% |
| WildBench | — | 85.7% |
Frequently asked questions
Is Command A better than GPT-5?
GPT-5 is the stronger model overall, scoring 50.9 to 36.5 on the Noometry Index.
Which is cheaper, Command A or GPT-5?
GPT-5 is cheaper. It lists at $1.25 per million input tokens and $10 per million output tokens; Command A lists at $2.50 and $10.
Is Command A or GPT-5 better for coding?
GPT-5 scores higher on coding benchmarks: 50.3 versus 27.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5 does, with 400K tokens against 256K.
How many benchmarks do Command A and GPT-5 share?
23 benchmarks have published results for both models. Command A has 24 scored results on Noometry and GPT-5 has 69.