Model comparison
Command A vs o4-mini
o4-mini is the stronger model overall, scoring 41.6 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 o4-mini in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in coding, where o4-mini leads 40.9 to 27.2.
- The biggest single-benchmark swing is Aider Polyglot: 12% for Command A and 72% for o4-mini.
- o4-mini is cheaper at $1.10 / $4.40 per million input/output tokens, against $2.50 / $10 for Command A.
- Command A accepts more context: 256K tokens versus 200K.
- Command A has downloadable open weights; the other is API-only.
Side by side
| Command A | o4-mini | |
|---|---|---|
| Provider | Cohere | OpenAI |
| Noometry Index | 36.5 | 41.6 |
| Released | 2025-03-13 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | 256K | 200K |
| Max output | 8K | 100K |
| Input $ / M tokens | $2.50 | $1.10 |
| Output $ / M tokens | $10 | $4.40 |
| Results tracked | 24 | 60 |
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Category by category
Coding o4-mini leads
Command A: 27.2 (#322), o4-mini: 40.9 (#127)
| Benchmark | Command A | o4-mini |
|---|---|---|
| Aider Polyglot | 12% | 72% |
| LMArena Coding | 1330 | 1368 |
| SWE-bench Verified (bash only) | — | 45% |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| CadEval | — | 62% |
| ALE-Bench | — | 826.17 |
| AlgoTune | — | 1.72 |
Agentic & Tool Use Command A leads
Command A: 35.9 (#40), o4-mini: 32.6 (#61)
| Benchmark | Command A | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 57.1% | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |
Reasoning o4-mini leads
Command A: 18.3 (#283), o4-mini: 24.6 (#162)
| Benchmark | Command A | o4-mini |
|---|---|---|
| Kagi LLM Benchmark | 28.8% | 67.6% |
| LMArena Hard Prompts | 1326 | 1351 |
| DTBench | 61.3% | 77.6% |
| LMCA | 10.3% | 26.5% |
| ARC-AGI-2 | — | 6.1% |
| SimpleBench | — | 38.7% |
| ARC-AGI-1 | — | 58.7% |
| CritPt | — | 0.6% |
| Chess Puzzles | — | 26% |
| EnigmaEval | — | 9.2% |
| Mystery Game Puzzles | — | 5% |
| Epoch Capabilities Index | — | 145.64 |
| ForecastBench | — | 61.8 |
Math o4-mini leads
Command A: 36.2 (#171), o4-mini: 40.8 (#89)
| Benchmark | Command A | o4-mini |
|---|---|---|
| LMArena Math | 1300 | 1389 |
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| OTIS Mock AIME 2024-2025 | — | 81.7% |
| Omni-MATH | — | 72% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge o4-mini leads
Command A: 37.1 (#159), o4-mini: 43.6 (#91)
| Benchmark | Command A | o4-mini |
|---|---|---|
| Vectara Hallucination Rate | 9.3% | 18.6% |
| LMArena Expert | 1295 | 1343 |
| GPQA Diamond | — | 79.6% |
| Humanity's Last Exam | — | 18.1% |
| SimpleQA Verified | — | 19.6% |
| MMLU-Pro | — | 82% |
| Confabulations | — | 15.8% |
| GPQA (HELM) | — | 73.5% |
Multimodal Not comparable
Command A: —, o4-mini: 40.2 (#49)
| Benchmark | Command A | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
Multilingual o4-mini leads
Command A: 45.3 (#170), o4-mini: 47.0 (#154)
| Benchmark | Command A | o4-mini |
|---|---|---|
| LMArena Non-English | 1313 | 1337 |
| LMArena Chinese | 1327 | 1354 |
| LMArena French | 1351 | 1364 |
| LMArena German | 1341 | 1336 |
| LMArena Japanese | 1285 | 1308 |
| LMArena Korean | 1285 | 1312 |
| LMArena Russian | 1314 | 1334 |
| LMArena Spanish | 1347 | 1347 |
Instruction Following o4-mini leads
Command A: 69.1 (#177), o4-mini: 75.2 (#68)
| Benchmark | Command A | o4-mini |
|---|---|---|
| LMArena Instruction Following | 1309 | 1321 |
| IFEval | — | 92.8% |
Long Context o4-mini leads
Command A: 40.6 (#151), o4-mini: 45.5 (#33)
| Benchmark | Command A | o4-mini |
|---|---|---|
| LMArena Longer Query | 1334 | 1315 |
| Fiction.LiveBench | — | 77.8% |
Writing & Preference o4-mini leads
Command A: 47.6 (#208), o4-mini: 54.0 (#152)
| Benchmark | Command A | o4-mini |
|---|---|---|
| LMArena Text | 1331 | 1353 |
| LMArena Creative Writing | 1319 | 1294 |
| LMArena Multi-Turn | 1339 | 1350 |
| Short-Story Creative Writing | — | 75% |
| EQ-Bench Creative Writing | 1145 | — |
| WildBench | — | 85.4% |
Frequently asked questions
Is Command A better than o4-mini?
o4-mini is the stronger model overall, scoring 41.6 to 36.5 on the Noometry Index.
Which is cheaper, Command A or o4-mini?
o4-mini is cheaper. It lists at $1.10 per million input tokens and $4.40 per million output tokens; Command A lists at $2.50 and $10.
Is Command A or o4-mini better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 27.2 in the Noometry coding category.
Which has the bigger context window?
Command A does, with 256K tokens against 200K.
How many benchmarks do Command A and o4-mini share?
23 benchmarks have published results for both models. Command A has 24 scored results on Noometry and o4-mini has 60.