Model comparison
Command R vs DeepSeek-R1
DeepSeek-R1 is the stronger model overall, scoring 42.0 to 31.0 on the Noometry Index. Command R costs 3.5× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Cohere
DeepSeekSummary
- They share 24 benchmarks with published results for both. Command R scores higher in 0 categories and DeepSeek-R1 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-R1 leads 61.5 to 38.4.
- The biggest single-benchmark swing is LiveBench Math: 19.4% for Command R and 80.7% for DeepSeek-R1.
- Command R is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- DeepSeek-R1 accepts more context: 164K tokens versus 128K.
- Command R has downloadable open weights; the other is API-only.
Side by side
| Command R | DeepSeek-R1 | |
|---|---|---|
| Provider | Cohere | DeepSeek |
| Noometry Index | 31.0 | 42.0 |
| Released | 2024-08-30 | 2025-01-20 |
| Weights | Open | Proprietary |
| Context window | 128K | 164K |
| Max output | 4K | 64K |
| Input $ / M tokens | $0.15 | $0.50 |
| Output $ / M tokens | $0.60 | $2.15 |
| Results tracked | 29 | 52 |
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Category by category
Coding DeepSeek-R1 leads
Command R: 28.8 (#301), DeepSeek-R1: 45.9 (#63)
| Benchmark | Command R | DeepSeek-R1 |
|---|---|---|
| LiveBench Coding | 17.9% | 66.7% |
| LMArena Coding | 1170 | 1427 |
| Aider Polyglot | — | 71.4% |
| SciCode | — | 35.7% |
| WeirdML | — | 41.6% |
| BigCodeBench Instruct | 37.1% | — |
| BigCodeBench Complete | 45.2% | — |
| ALE-Bench | — | 804.12 |
| AlgoTune | — | 1.7 |
Agentic & Tool Use Not comparable
Command R: —, DeepSeek-R1: 30.4 (#68)
| Benchmark | Command R | DeepSeek-R1 |
|---|---|---|
| DeepResearch Bench | — | 35.1% |
| BALROG | — | 34.9% |
| METR Time Horizons | — | 53.8% |
Reasoning DeepSeek-R1 leads
Command R: 13.3 (#329), DeepSeek-R1: 18.7 (#253)
| Benchmark | Command R | DeepSeek-R1 |
|---|---|---|
| LiveBench Reasoning | 21.9% | 83.2% |
| LMArena Hard Prompts | 1164 | 1415 |
| LiveBench Data Analysis | 33.3% | 69.8% |
| LiveBench | 27.5% | 71.6% |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 40.8% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 21.2% |
| CritPt | — | 1.1% |
| DTBench | 46.4% | — |
| LMCA | 9.2% | — |
| Epoch Capabilities Index | — | 141.3 |
| ForecastBench | — | 60 |
Math DeepSeek-R1 leads
Command R: 25.8 (#244), DeepSeek-R1: 42.0 (#70)
| Benchmark | Command R | DeepSeek-R1 |
|---|---|---|
| LiveBench Math | 19.4% | 80.7% |
| LMArena Math | 1155 | 1401 |
| OTIS Mock AIME 2024-2025 | — | 66.4% |
| Omni-MATH | — | 42.4% |
| MATH Level 5 | — | 96.6% |
Knowledge DeepSeek-R1 leads
Command R: 30.5 (#219), DeepSeek-R1: 44.3 (#81)
| Benchmark | Command R | DeepSeek-R1 |
|---|---|---|
| LMArena Expert | 1138 | 1394 |
| GPQA Diamond | — | 76.3% |
| MMLU-Pro | — | 79.3% |
| Confabulations | — | 12.7% |
| Vectara Hallucination Rate | — | 11.3% |
| GPQA (HELM) | — | 66.6% |
| MMLU | 65.2% | — |
Multilingual DeepSeek-R1 leads
Command R: 35.9 (#240), DeepSeek-R1: 52.6 (#80)
| Benchmark | Command R | DeepSeek-R1 |
|---|---|---|
| LMArena Non-English | 1174 | 1412 |
| LMArena Chinese | 1182 | 1443 |
| LMArena French | 1161 | 1415 |
| LMArena German | 1176 | 1405 |
| LMArena Japanese | 1143 | 1394 |
| LMArena Korean | 1162 | 1359 |
| LMArena Russian | 1174 | 1423 |
| LMArena Spanish | 1150 | 1410 |
Instruction Following DeepSeek-R1 leads
Command R: 58.3 (#256), DeepSeek-R1: 72.2 (#138)
| Benchmark | Command R | DeepSeek-R1 |
|---|---|---|
| LiveBench Instruction Following | 55.6% | 80.5% |
| LMArena Instruction Following | 1167 | 1382 |
| IFEval | — | 78.4% |
Long Context DeepSeek-R1 leads
Command R: 36.3 (#226), DeepSeek-R1: 45.4 (#36)
| Benchmark | Command R | DeepSeek-R1 |
|---|---|---|
| LMArena Longer Query | 1198 | 1391 |
| Fiction.LiveBench | — | 75% |
Writing & Preference DeepSeek-R1 leads
Command R: 38.4 (#248), DeepSeek-R1: 61.5 (#84)
| Benchmark | Command R | DeepSeek-R1 |
|---|---|---|
| LMArena Text | 1187 | 1428 |
| LMArena Creative Writing | 1170 | 1406 |
| LMArena Multi-Turn | 1163 | 1404 |
| LiveBench Language | 16.7% | 48.5% |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1500 |
| WildBench | — | 82.8% |
Frequently asked questions
Is Command R better than DeepSeek-R1?
DeepSeek-R1 is the stronger model overall, scoring 42.0 to 31.0 on the Noometry Index. Command R costs 3.5× less per token, which makes it the better buy when DeepSeek-R1's lead doesn't matter for your workload.
Which is cheaper, Command R or DeepSeek-R1?
Command R is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.
Is Command R or DeepSeek-R1 better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 45.9 versus 28.8 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-R1 does, with 164K tokens against 128K.
How many benchmarks do Command R and DeepSeek-R1 share?
24 benchmarks have published results for both models. Command R has 29 scored results on Noometry and DeepSeek-R1 has 52.