Model comparison
Deepseek Coder v2 vs DeepSeek-R1
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 35.9 on the Noometry Index.
Last verified . 17 shared benchmarks.
DeepSeekSummary
- They share 17 benchmarks with published results for both. Deepseek Coder v2 scores higher in 1 category and DeepSeek-R1 in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-R1 leads 61.4 to 38.2.
- Deepseek Coder v2 has downloadable open weights; the other is API-only.
Side by side
| Deepseek Coder v2 | DeepSeek-R1 | |
|---|---|---|
| Provider | DeepSeek | DeepSeek |
| Noometry Index | 35.9 | 42.3 |
| Released | 2024-06-17 | 2025-01-20 |
| Weights | Open | Proprietary |
| Context window | — | 164K |
| Max output | — | 64K |
| Input $ / M tokens | — | $0.50 |
| Output $ / M tokens | — | $2.15 |
| Results tracked | 24 | 52 |
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Category by category
Coding DeepSeek-R1 leads
Deepseek Coder v2: 38.1 (#183), DeepSeek-R1: 46.3 (#68)
| Benchmark | Deepseek Coder v2 | DeepSeek-R1 |
|---|---|---|
| LMArena Coding | 1251 | 1427 |
| Aider Polyglot | — | 71.4% |
| SciCode | — | 35.7% |
| WeirdML | — | 41.6% |
| BigCodeBench Instruct | 48.2% | — |
| LiveBench Coding | — | 66.7% |
| BigCodeBench Complete | 59.7% | — |
| ALE-Bench | — | 804.12 |
| AlgoTune | — | 1.7 |
| HumanEval+ | 82.3% | — |
| MBPP+ | 75.1% | — |
Agentic & Tool Use Not comparable
Deepseek Coder v2: —, DeepSeek-R1: 30.7 (#75)
| Benchmark | Deepseek Coder v2 | DeepSeek-R1 |
|---|---|---|
| DeepResearch Bench | — | 35.1% |
| BALROG | — | 34.9% |
| METR Time Horizons | — | 53.8% |
Reasoning Deepseek Coder v2 leads
Deepseek Coder v2: 23.6 (#176), DeepSeek-R1: 18.6 (#278)
| Benchmark | Deepseek Coder v2 | DeepSeek-R1 |
|---|---|---|
| LMArena Hard Prompts | 1207 | 1416 |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 40.8% |
| Kagi LLM Benchmark | — | 69.4% |
| ARC-AGI-1 | — | 21.2% |
| CritPt | — | 1.1% |
| LiveBench Reasoning | — | 83.2% |
| LiveBench Data Analysis | — | 69.8% |
| Epoch Capabilities Index | — | 141.29 |
| ForecastBench | — | 60 |
| LiveBench | — | 71.6% |
| WinoGrande | 83.7% | — |
Math DeepSeek-R1 leads
Deepseek Coder v2: 34.9 (#190), DeepSeek-R1: 43.8 (#79)
| Benchmark | Deepseek Coder v2 | DeepSeek-R1 |
|---|---|---|
| LMArena Math | 1241 | 1400 |
| OTIS Mock AIME 2024-2025 | — | 66.4% |
| Omni-MATH | — | 42.4% |
| LiveBench Math | — | 80.7% |
| MATH Level 5 | — | 96.6% |
| GSM8K | 94.5% | — |
Knowledge DeepSeek-R1 leads
Deepseek Coder v2: 32.3 (#212), DeepSeek-R1: 44.5 (#87)
| Benchmark | Deepseek Coder v2 | DeepSeek-R1 |
|---|---|---|
| LMArena Expert | 1181 | 1394 |
| GPQA Diamond | — | 76.3% |
| MMLU-Pro | — | 79.3% |
| Confabulations | — | 12.7% |
| Vectara Hallucination Rate | — | 11.3% |
| GPQA (HELM) | — | 66.6% |
| ARC (AI2) Challenge | 64.3% | — |
Multilingual DeepSeek-R1 leads
Deepseek Coder v2: 36.3 (#240), DeepSeek-R1: 52.4 (#85)
| Benchmark | Deepseek Coder v2 | DeepSeek-R1 |
|---|---|---|
| LMArena Non-English | 1182 | 1412 |
| LMArena Chinese | 1201 | 1442 |
| LMArena French | 1185 | 1417 |
| LMArena German | 1164 | 1404 |
| LMArena Japanese | 1126 | 1391 |
| LMArena Korean | 1104 | 1360 |
| LMArena Russian | 1188 | 1423 |
| LMArena Spanish | 1153 | 1411 |
Instruction Following DeepSeek-R1 leads
Deepseek Coder v2: 61.7 (#242), DeepSeek-R1: 72.0 (#143)
| Benchmark | Deepseek Coder v2 | DeepSeek-R1 |
|---|---|---|
| LMArena Instruction Following | 1180 | 1382 |
| LiveBench Instruction Following | — | 80.5% |
| IFEval | — | 78.4% |
Long Context DeepSeek-R1 leads
Deepseek Coder v2: 37.0 (#224), DeepSeek-R1: 45.4 (#36)
| Benchmark | Deepseek Coder v2 | DeepSeek-R1 |
|---|---|---|
| LMArena Longer Query | 1219 | 1391 |
| Fiction.LiveBench | — | 75% |
Writing & Preference DeepSeek-R1 leads
Deepseek Coder v2: 38.2 (#253), DeepSeek-R1: 61.4 (#88)
| Benchmark | Deepseek Coder v2 | DeepSeek-R1 |
|---|---|---|
| LMArena Text | 1191 | 1428 |
| LMArena Creative Writing | 1120 | 1405 |
| LMArena Multi-Turn | 1177 | 1405 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1500 |
| WildBench | — | 82.8% |
| LiveBench Language | — | 48.5% |
Frequently asked questions
Is Deepseek Coder v2 better than DeepSeek-R1?
DeepSeek-R1 is the stronger model overall, scoring 42.3 to 35.9 on the Noometry Index.
Is Deepseek Coder v2 or DeepSeek-R1 better for coding?
DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 38.1 in the Noometry coding category.
How many benchmarks do Deepseek Coder v2 and DeepSeek-R1 share?
17 benchmarks have published results for both models. Deepseek Coder v2 has 24 scored results on Noometry and DeepSeek-R1 has 52.