Model comparison
Command R+ vs DeepSeek-V3.2-Exp
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 32.4 on the Noometry Index.
Last verified . 21 shared benchmarks.
Cohere
DeepSeekSummary
- They share 21 benchmarks with published results for both. Command R+ scores higher in 0 categories and DeepSeek-V3.2-Exp in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.2-Exp leads 62.4 to 43.5.
- The biggest single-benchmark swing is DTBench: 54.9% for Command R+ and 87.7% for DeepSeek-V3.2-Exp.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $2.50 / $10 for Command R+.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 128K.
Side by side
| Command R+ | DeepSeek-V3.2-Exp | |
|---|---|---|
| Provider | Cohere | DeepSeek |
| Noometry Index | 32.4 | 44.3 |
| Released | 2024-08-30 | 2025-09-29 |
| Weights | Open | Open |
| Context window | 128K | 164K |
| Max output | 4K | 66K |
| Input $ / M tokens | $2.50 | $0.26 |
| Output $ / M tokens | $10 | $0.38 |
| Results tracked | 34 | 49 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
Command R+: 29.1 (#309), DeepSeek-V3.2-Exp: 46.5 (#65)
| Benchmark | Command R+ | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Coding | 1187 | 1454 |
| SWE-bench Verified (bash only) | — | 70% |
| Aider Polyglot | — | 74.2% |
| LMArena WebDev | — | 1362 |
| SWE-bench Multilingual | — | 59% |
| SciCode | — | 38.9% |
| WeirdML | — | 39.5% |
| BigCodeBench Instruct | 33.8% | — |
| LiveBench Coding | 19.1% | — |
| BigCodeBench Complete | 41.9% | — |
| HumanEval+ | 56.7% | — |
| MBPP+ | 63.5% | — |
Agentic & Tool Use Not comparable
Command R+: —, DeepSeek-V3.2-Exp: 32.7 (#59)
| Benchmark | Command R+ | DeepSeek-V3.2-Exp |
|---|---|---|
| Terminal-Bench | — | 39.6% |
| APEX-Agents | — | 21.3% |
| Berkeley Function Calling Leaderboard | — | 56.7% |
| TheAgentCompany | — | 42.9% |
| Vending-Bench 2 | — | 1,034 |
Reasoning DeepSeek-V3.2-Exp leads
Command R+: 9.2 (#344), DeepSeek-V3.2-Exp: 22.1 (#208)
| Benchmark | Command R+ | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Hard Prompts | 1186 | 1434 |
| DTBench | 54.9% | 87.7% |
| LMCA | 5% | 29.1% |
| Epoch Capabilities Index | 119.34 | 146.27 |
| ARC-AGI-2 | — | 4% |
| SimpleBench | 17.4% | — |
| Kagi LLM Benchmark | — | 52.2% |
| NYT Connections (extended) | — | 36.7% |
| ARC-AGI-1 | — | 57% |
| CritPt | — | 2.9% |
| Chess Puzzles | — | 14% |
| Thematic Generalization | — | 65% |
| LiveBench Reasoning | 24.8% | — |
| LiveBench Data Analysis | 38.1% | — |
| LiveBench | 31.8% | — |
Math DeepSeek-V3.2-Exp leads
Command R+: 28.9 (#242), DeepSeek-V3.2-Exp: 41.7 (#87)
| Benchmark | Command R+ | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Math | 1188 | 1435 |
| MathArena Final-Answer Competitions | — | 57.7% |
| OTIS Mock AIME 2024-2025 | — | 87.8% |
| ProofBench | — | 8% |
| LiveBench Math | 21.3% | — |
| FrontierMath (Feb 2025 set) | — | 22.1% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge DeepSeek-V3.2-Exp leads
Command R+: 36.4 (#169), DeepSeek-V3.2-Exp: 51.7 (#66)
| Benchmark | Command R+ | DeepSeek-V3.2-Exp |
|---|---|---|
| Vectara Hallucination Rate | 6.9% | 5.3% |
| LMArena Expert | 1174 | 1436 |
| GPQA Diamond | — | 83.4% |
| MMLU | 69.4% | — |
Multilingual DeepSeek-V3.2-Exp leads
Command R+: 38.6 (#227), DeepSeek-V3.2-Exp: 52.2 (#90)
| Benchmark | Command R+ | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Non-English | 1216 | 1409 |
| LMArena Chinese | 1226 | 1461 |
| LMArena French | 1209 | 1433 |
| LMArena German | 1216 | 1440 |
| LMArena Japanese | 1166 | 1374 |
| LMArena Korean | 1138 | 1371 |
| LMArena Russian | 1227 | 1424 |
| LMArena Spanish | 1189 | 1440 |
Instruction Following DeepSeek-V3.2-Exp leads
Command R+: 60.0 (#254), DeepSeek-V3.2-Exp: 74.5 (#93)
| Benchmark | Command R+ | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Instruction Following | 1197 | 1413 |
| LiveBench Instruction Following | 57.6% | — |
Long Context DeepSeek-V3.2-Exp leads
Command R+: 37.3 (#219), DeepSeek-V3.2-Exp: 47.6 (#16)
| Benchmark | Command R+ | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Longer Query | 1230 | 1428 |
| Fiction.LiveBench | — | 83.3% |
| CL-bench | — | 13.2% |
| CL-bench Life | — | 9.5% |
Writing & Preference DeepSeek-V3.2-Exp leads
Command R+: 43.5 (#228), DeepSeek-V3.2-Exp: 62.4 (#77)
| Benchmark | Command R+ | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Text | 1229 | 1425 |
| LMArena Creative Writing | 1235 | 1403 |
| LMArena Multi-Turn | 1213 | 1427 |
| EQ-Bench Creative Writing | — | 1515 |
| LiveBench Language | 29.7% | — |
Frequently asked questions
Is Command R+ better than DeepSeek-V3.2-Exp?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 32.4 on the Noometry Index.
Which is cheaper, Command R+ or DeepSeek-V3.2-Exp?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Command R+ lists at $2.50 and $10.
Is Command R+ or DeepSeek-V3.2-Exp better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 29.1 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.2-Exp does, with 164K tokens against 128K.
How many benchmarks do Command R+ and DeepSeek-V3.2-Exp share?
21 benchmarks have published results for both models. Command R+ has 34 scored results on Noometry and DeepSeek-V3.2-Exp has 49.