Model comparison
Deepseek Coder v2 vs DeepSeek-V3.2-Exp
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 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-V3.2-Exp in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.2-Exp leads 62.4 to 38.2.
Side by side
| Deepseek Coder v2 | DeepSeek-V3.2-Exp | |
|---|---|---|
| Provider | DeepSeek | DeepSeek |
| Noometry Index | 35.9 | 44.3 |
| Released | 2024-06-17 | 2025-09-29 |
| Weights | Open | Open |
| Context window | — | 164K |
| Max output | — | 66K |
| Input $ / M tokens | — | $0.26 |
| Output $ / M tokens | — | $0.38 |
| Results tracked | 24 | 49 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
Deepseek Coder v2: 38.1 (#183), DeepSeek-V3.2-Exp: 46.5 (#65)
| Benchmark | Deepseek Coder v2 | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Coding | 1251 | 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 | 48.2% | — |
| BigCodeBench Complete | 59.7% | — |
| HumanEval+ | 82.3% | — |
| MBPP+ | 75.1% | — |
Agentic & Tool Use Not comparable
Deepseek Coder v2: —, DeepSeek-V3.2-Exp: 32.7 (#59)
| Benchmark | Deepseek Coder v2 | 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 Coder v2 leads
Deepseek Coder v2: 23.6 (#176), DeepSeek-V3.2-Exp: 22.1 (#208)
| Benchmark | Deepseek Coder v2 | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Hard Prompts | 1207 | 1434 |
| ARC-AGI-2 | — | 4% |
| Kagi LLM Benchmark | — | 52.2% |
| NYT Connections (extended) | — | 36.7% |
| ARC-AGI-1 | — | 57% |
| CritPt | — | 2.9% |
| Chess Puzzles | — | 14% |
| Thematic Generalization | — | 65% |
| DTBench | — | 87.7% |
| LMCA | — | 29.1% |
| Epoch Capabilities Index | — | 146.27 |
| WinoGrande | 83.7% | — |
Math DeepSeek-V3.2-Exp leads
Deepseek Coder v2: 34.9 (#190), DeepSeek-V3.2-Exp: 41.7 (#87)
| Benchmark | Deepseek Coder v2 | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Math | 1241 | 1435 |
| MathArena Final-Answer Competitions | — | 57.7% |
| OTIS Mock AIME 2024-2025 | — | 87.8% |
| ProofBench | — | 8% |
| FrontierMath (Feb 2025 set) | — | 22.1% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
| GSM8K | 94.5% | — |
Knowledge DeepSeek-V3.2-Exp leads
Deepseek Coder v2: 32.3 (#212), DeepSeek-V3.2-Exp: 51.7 (#66)
| Benchmark | Deepseek Coder v2 | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Expert | 1181 | 1436 |
| GPQA Diamond | — | 83.4% |
| Vectara Hallucination Rate | — | 5.3% |
| ARC (AI2) Challenge | 64.3% | — |
Multilingual DeepSeek-V3.2-Exp leads
Deepseek Coder v2: 36.3 (#240), DeepSeek-V3.2-Exp: 52.2 (#90)
| Benchmark | Deepseek Coder v2 | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Non-English | 1182 | 1409 |
| LMArena Chinese | 1201 | 1461 |
| LMArena French | 1185 | 1433 |
| LMArena German | 1164 | 1440 |
| LMArena Japanese | 1126 | 1374 |
| LMArena Korean | 1104 | 1371 |
| LMArena Russian | 1188 | 1424 |
| LMArena Spanish | 1153 | 1440 |
Instruction Following DeepSeek-V3.2-Exp leads
Deepseek Coder v2: 61.7 (#242), DeepSeek-V3.2-Exp: 74.5 (#93)
| Benchmark | Deepseek Coder v2 | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Instruction Following | 1180 | 1413 |
Long Context DeepSeek-V3.2-Exp leads
Deepseek Coder v2: 37.0 (#224), DeepSeek-V3.2-Exp: 47.6 (#16)
| Benchmark | Deepseek Coder v2 | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Longer Query | 1219 | 1428 |
| Fiction.LiveBench | — | 83.3% |
| CL-bench | — | 13.2% |
| CL-bench Life | — | 9.5% |
Writing & Preference DeepSeek-V3.2-Exp leads
Deepseek Coder v2: 38.2 (#253), DeepSeek-V3.2-Exp: 62.4 (#77)
| Benchmark | Deepseek Coder v2 | DeepSeek-V3.2-Exp |
|---|---|---|
| LMArena Text | 1191 | 1425 |
| LMArena Creative Writing | 1120 | 1403 |
| LMArena Multi-Turn | 1177 | 1427 |
| EQ-Bench Creative Writing | — | 1515 |
Frequently asked questions
Is Deepseek Coder v2 better than DeepSeek-V3.2-Exp?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 35.9 on the Noometry Index.
Is Deepseek Coder v2 or DeepSeek-V3.2-Exp better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 38.1 in the Noometry coding category.
How many benchmarks do Deepseek Coder v2 and DeepSeek-V3.2-Exp share?
17 benchmarks have published results for both models. Deepseek Coder v2 has 24 scored results on Noometry and DeepSeek-V3.2-Exp has 49.