Model comparison
Deepseek Coder v2 vs Qwen3.5 122B-A10B
Qwen3.5 122B-A10B is the stronger model overall, scoring 42.1 to 35.9 on the Noometry Index.
Last verified . 17 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 17 benchmarks with published results for both. Deepseek Coder v2 scores higher in 0 categories and Qwen3.5 122B-A10B in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3.5 122B-A10B leads 60.0 to 38.2.
Side by side
| Deepseek Coder v2 | Qwen3.5 122B-A10B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 35.9 | 42.1 |
| Released | 2024-06-17 | 2026-02-23 |
| Weights | Open | Open |
| Context window | — | 262K |
| Max output | — | 66K |
| Input $ / M tokens | — | $0.40 |
| Output $ / M tokens | — | $3.20 |
| Results tracked | 24 | 27 |
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Category by category
Coding Qwen3.5 122B-A10B leads
Deepseek Coder v2: 38.1 (#183), Qwen3.5 122B-A10B: 39.1 (#162)
| Benchmark | Deepseek Coder v2 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Coding | 1251 | 1436 |
| LMArena WebDev | — | 1360 |
| SciCode | — | 35.6% |
| BigCodeBench Instruct | 48.2% | — |
| BigCodeBench Complete | 59.7% | — |
| HumanEval+ | 82.3% | — |
| MBPP+ | 75.1% | — |
Reasoning Qwen3.5 122B-A10B leads
Deepseek Coder v2: 23.6 (#176), Qwen3.5 122B-A10B: 27.2 (#123)
| Benchmark | Deepseek Coder v2 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Hard Prompts | 1207 | 1421 |
| NYT Connections (extended) | — | 51.7% |
| CritPt | — | 0.9% |
| Thematic Generalization | — | 51.2% |
| Mystery Game Puzzles | — | 17% |
| DTBench | — | 84.3% |
| LMCA | — | 32.2% |
| WinoGrande | 83.7% | — |
Math Qwen3.5 122B-A10B leads
Deepseek Coder v2: 34.9 (#190), Qwen3.5 122B-A10B: 39.1 (#112)
| Benchmark | Deepseek Coder v2 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Math | 1241 | 1432 |
| GSM8K | 94.5% | — |
Knowledge Qwen3.5 122B-A10B leads
Deepseek Coder v2: 32.3 (#212), Qwen3.5 122B-A10B: 38.8 (#142)
| Benchmark | Deepseek Coder v2 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Expert | 1181 | 1432 |
| Vectara Hallucination Rate | — | 11.2% |
| ARC (AI2) Challenge | 64.3% | — |
Multimodal Not comparable
Deepseek Coder v2: —, Qwen3.5 122B-A10B: 39.6 (#57)
| Benchmark | Deepseek Coder v2 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Vision | — | 1245 |
Multilingual Qwen3.5 122B-A10B leads
Deepseek Coder v2: 36.3 (#240), Qwen3.5 122B-A10B: 51.6 (#107)
| Benchmark | Deepseek Coder v2 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Non-English | 1182 | 1400 |
| LMArena Chinese | 1201 | 1462 |
| LMArena French | 1185 | 1442 |
| LMArena German | 1164 | 1426 |
| LMArena Japanese | 1126 | 1367 |
| LMArena Korean | 1104 | 1352 |
| LMArena Russian | 1188 | 1400 |
| LMArena Spanish | 1153 | 1424 |
Instruction Following Qwen3.5 122B-A10B leads
Deepseek Coder v2: 61.7 (#242), Qwen3.5 122B-A10B: 73.8 (#115)
| Benchmark | Deepseek Coder v2 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Instruction Following | 1180 | 1399 |
Long Context Qwen3.5 122B-A10B leads
Deepseek Coder v2: 37.0 (#224), Qwen3.5 122B-A10B: 43.0 (#109)
| Benchmark | Deepseek Coder v2 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Longer Query | 1219 | 1410 |
Writing & Preference Qwen3.5 122B-A10B leads
Deepseek Coder v2: 38.2 (#253), Qwen3.5 122B-A10B: 60.0 (#105)
| Benchmark | Deepseek Coder v2 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Text | 1191 | 1417 |
| LMArena Creative Writing | 1120 | 1368 |
| LMArena Multi-Turn | 1177 | 1416 |
Frequently asked questions
Is Deepseek Coder v2 better than Qwen3.5 122B-A10B?
Qwen3.5 122B-A10B is the stronger model overall, scoring 42.1 to 35.9 on the Noometry Index.
Is Deepseek Coder v2 or Qwen3.5 122B-A10B better for coding?
Qwen3.5 122B-A10B scores higher on coding benchmarks: 39.1 versus 38.1 in the Noometry coding category.
How many benchmarks do Deepseek Coder v2 and Qwen3.5 122B-A10B share?
17 benchmarks have published results for both models. Deepseek Coder v2 has 24 scored results on Noometry and Qwen3.5 122B-A10B has 27.