Model comparison
DeepSeek-V2.5 (Sep 2024) vs Qwen3.8 Max
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 37.6 on the Noometry Index.
Last verified . 17 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 17 benchmarks with published results for both. DeepSeek-V2.5 (Sep 2024) scores higher in 0 categories and Qwen3.8 Max in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.8 Max leads 73.2 to 35.9.
- DeepSeek-V2.5 (Sep 2024) has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V2.5 (Sep 2024) | Qwen3.8 Max | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 37.6 | 56.8 |
| Released | 2024-09-06 | 2026-08-02 |
| Weights | Open | Proprietary |
| Context window | — | 1M |
| Max output | — | 131K |
| Input $ / M tokens | — | $2 |
| Output $ / M tokens | — | $6 |
| Results tracked | 22 | 39 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen3.8 Max leads
DeepSeek-V2.5 (Sep 2024): 31.7 (#281), Qwen3.8 Max: 53.5 (#29)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.8 Max |
|---|---|---|
| LMArena Coding | 1309 | 1502 |
| DeepSWE | — | 57.5% |
| Aider Polyglot | 17.8% | — |
| LMArena WebDev | — | 1674 |
| FrontierSWE | — | 17.8% |
| SciCode | — | 53.2% |
| BigCodeBench Instruct | 48.6% | — |
| BigCodeBench Complete | 53.2% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |
Agentic & Tool Use Not comparable
DeepSeek-V2.5 (Sep 2024): —, Qwen3.8 Max: 45.4 (#14)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.8 Max |
|---|---|---|
| APEX-Agents | — | 63.3% |
| τ²-bench Banking | — | 55.1% |
| GDP.pdf | — | 23.2% |
Reasoning Qwen3.8 Max leads
DeepSeek-V2.5 (Sep 2024): 25.6 (#145), Qwen3.8 Max: 54.4 (#26)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.8 Max |
|---|---|---|
| LMArena Hard Prompts | 1289 | 1496 |
| NYT Connections (extended) | — | 88.3% |
| CritPt | — | 20% |
| Chess Puzzles | — | 40% |
| Mystery Game Puzzles | — | 38% |
| DTBench | — | 92% |
| LMCA | — | 46.2% |
| Epoch Capabilities Index | — | 156.41 |
Math Qwen3.8 Max leads
DeepSeek-V2.5 (Sep 2024): 35.9 (#177), Qwen3.8 Max: 73.2 (#20)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.8 Max |
|---|---|---|
| LMArena Math | 1288 | 1499 |
| FrontierMath (Tiers 1-3) | — | 74.7% |
| FrontierMath Tier 4 | — | 46.3% |
| OTIS Mock AIME 2024-2025 | — | 100% |
| ProofBench | — | 58% |
Knowledge Qwen3.8 Max leads
DeepSeek-V2.5 (Sep 2024): 34.8 (#193), Qwen3.8 Max: 61.7 (#27)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.8 Max |
|---|---|---|
| LMArena Expert | 1266 | 1507 |
| GPQA Diamond | — | 92.7% |
| SimpleQA Verified | — | 47.3% |
Multimodal Not comparable
DeepSeek-V2.5 (Sep 2024): —, Qwen3.8 Max: 37.2 (#75)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | — | 1314 |
| Furniture Assembly | — | 20% |
Multilingual Qwen3.8 Max leads
DeepSeek-V2.5 (Sep 2024): 42.5 (#193), Qwen3.8 Max: 56.7 (#18)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | 1273 | 1472 |
| LMArena Chinese | 1318 | 1538 |
| LMArena French | 1289 | 1503 |
| LMArena German | 1258 | 1483 |
| LMArena Japanese | 1228 | 1467 |
| LMArena Korean | 1209 | 1461 |
| LMArena Russian | 1289 | 1481 |
| LMArena Spanish | 1248 | 1492 |
Instruction Following Qwen3.8 Max leads
DeepSeek-V2.5 (Sep 2024): 67.5 (#194), Qwen3.8 Max: 77.6 (#17)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | 1280 | 1479 |
Long Context Qwen3.8 Max leads
DeepSeek-V2.5 (Sep 2024): 39.5 (#174), Qwen3.8 Max: 45.6 (#31)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | 1301 | 1489 |
Writing & Preference Qwen3.8 Max leads
DeepSeek-V2.5 (Sep 2024): 49.8 (#187), Qwen3.8 Max: 67.1 (#30)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | Qwen3.8 Max |
|---|---|---|
| LMArena Text | 1294 | 1483 |
| LMArena Creative Writing | 1285 | 1479 |
| LMArena Multi-Turn | 1297 | 1489 |
Frequently asked questions
Is DeepSeek-V2.5 (Sep 2024) better than Qwen3.8 Max?
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 37.6 on the Noometry Index.
Is DeepSeek-V2.5 (Sep 2024) or Qwen3.8 Max better for coding?
Qwen3.8 Max scores higher on coding benchmarks: 53.5 versus 31.7 in the Noometry coding category.
How many benchmarks do DeepSeek-V2.5 (Sep 2024) and Qwen3.8 Max share?
17 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and Qwen3.8 Max has 39.