# Command R vs DeepSeek V4 Pro

> DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 31.4 on the Noometry Index. Command R costs 3.8× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/command-r-vs-deepseek-v4-pro
- Last updated: 2026-10-11
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. Command R scores higher in 0 categories and DeepSeek V4 Pro in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Pro leads 56.5 to 13.8.
- The biggest single-benchmark swing is DTBench: 46.4% for Command R and 93.9% for DeepSeek V4 Pro.
- Command R is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.66 / $1.98 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 128K.

## Snapshot

| | Command R | DeepSeek V4 Pro |
|---|---|---|
| Provider | Cohere | DeepSeek |
| Noometry Index | 31.4 | 54.3 |
| Rank | 272 | 31 |
| Context | 128K | 1M |
| Input $/M | $0.15 | $0.66 |
| Output $/M | $0.60 | $1.98 |
| Weights | Open | Open |

## Coding

- Command R: 29.3 (#306)
- DeepSeek V4 Pro: 52.4 (#34)

| Benchmark | Command R | DeepSeek V4 Pro |
|---|---|---|
| LMArena Coding | 1169 | 1470 |
| SWE-bench Verified | — | 77.6% |
| FrontierCode | — | 28.6% |
| LMArena WebDev | — | 1582 |
| SciCode | — | 51% |
| WeirdML | — | 66.2% |
| BigCodeBench Instruct | 37.1% | — |
| LiveBench Coding | 17.9% | — |
| BigCodeBench Complete | 45.2% | — |
| ALE-Bench | — | 1,403 |

## Agentic & Tool Use

- Command R: —
- DeepSeek V4 Pro: 32.8 (#58)

| Benchmark | Command R | DeepSeek V4 Pro |
|---|---|---|
| APEX-Agents | — | 47.3% |
| Vending-Bench 2 | — | 3,285 |

## Reasoning

- Command R: 13.8 (#331)
- DeepSeek V4 Pro: 56.5 (#24)

| Benchmark | Command R | DeepSeek V4 Pro |
|---|---|---|
| LMArena Hard Prompts | 1164 | 1461 |
| DTBench | 46.4% | 93.9% |
| LMCA | 9.2% | 45.5% |
| ARC-AGI-2 | — | 61.3% |
| Kagi LLM Benchmark | — | 53.5% |
| NYT Connections (extended) | — | 91.3% |
| ARC-AGI-1 | — | 90.5% |
| CritPt | — | 18% |
| Chess Puzzles | — | 47% |
| LiveBench Reasoning | 21.9% | — |
| Mystery Game Puzzles | — | 43% |
| LiveBench Data Analysis | 33.3% | — |
| Surface Evolver Bench | — | 40% |
| Epoch Capabilities Index | — | 155.31 |
| ForecastBench | — | 56.1 |
| LiveBench | 27.5% | — |

## Math

- Command R: 28.0 (#246)
- DeepSeek V4 Pro: 64.8 (#30)

| Benchmark | Command R | DeepSeek V4 Pro |
|---|---|---|
| LMArena Math | 1155 | 1455 |
| FrontierMath (Tiers 1-3) | — | 64.6% |
| FrontierMath Tier 4 | — | 26.8% |
| MathArena Final-Answer Competitions | — | 76.6% |
| OTIS Mock AIME 2024-2025 | — | 98.6% |
| ProofBench | — | 50% |
| LiveBench Math | 19.4% | — |

## Knowledge

- Command R: 31.0 (#221)
- DeepSeek V4 Pro: 59.5 (#31)

| Benchmark | Command R | DeepSeek V4 Pro |
|---|---|---|
| LMArena Expert | 1138 | 1464 |
| GPQA Diamond | — | 91.7% |
| SimpleQA Verified | — | 52.9% |
| Vectara Hallucination Rate | — | 8.6% |
| MMLU | 65.2% | — |

## Multilingual

- Command R: 35.7 (#245)
- DeepSeek V4 Pro: 54.4 (#45)

| Benchmark | Command R | DeepSeek V4 Pro |
|---|---|---|
| LMArena Non-English | 1174 | 1439 |
| LMArena Chinese | 1182 | 1486 |
| LMArena French | 1162 | 1472 |
| LMArena German | 1176 | 1458 |
| LMArena Japanese | 1143 | 1445 |
| LMArena Korean | 1163 | 1447 |
| LMArena Russian | 1174 | 1453 |
| LMArena Spanish | 1151 | 1458 |

## Instruction Following

- Command R: 58.1 (#261)
- DeepSeek V4 Pro: 76.1 (#47)

| Benchmark | Command R | DeepSeek V4 Pro |
|---|---|---|
| LMArena Instruction Following | 1167 | 1448 |
| LiveBench Instruction Following | 55.6% | — |

## Long Context

- Command R: 36.3 (#231)
- DeepSeek V4 Pro: 45.0 (#51)

| Benchmark | Command R | DeepSeek V4 Pro |
|---|---|---|
| LMArena Longer Query | 1198 | 1458 |
| CL-bench Life | — | 13.5% |

## Writing & Preference

- Command R: 38.2 (#254)
- DeepSeek V4 Pro: 65.5 (#46)

| Benchmark | Command R | DeepSeek V4 Pro |
|---|---|---|
| LMArena Text | 1187 | 1451 |
| LMArena Creative Writing | 1170 | 1446 |
| LMArena Multi-Turn | 1163 | 1467 |
| EQ-Bench Creative Writing | — | 1553 |
| EQ-Bench 4 | — | 1166 |
| LiveBench Language | 16.7% | — |

## FAQ

### Is Command R better than DeepSeek V4 Pro?

DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 31.4 on the Noometry Index. Command R costs 3.8× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.

### Which is cheaper, Command R or DeepSeek V4 Pro?

Command R is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; DeepSeek V4 Pro lists at $0.66 and $1.98.

### Is Command R or DeepSeek V4 Pro better for coding?

DeepSeek V4 Pro scores higher on coding benchmarks: 52.4 versus 29.3 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek V4 Pro does, with 1M tokens against 128K.

### How many benchmarks do Command R and DeepSeek V4 Pro share?

19 benchmarks have published results for both models. Command R has 29 scored results on Noometry and DeepSeek V4 Pro has 48.