Model comparison
DeepSeek-V3.1-Terminus vs Qwen3 235B-A22B
DeepSeek-V3.1-Terminus and Qwen3 235B-A22B score almost the same on the Noometry Index (43.1 vs 43.5), so choose on price, context window or the category you care about most.
Last verified . 15 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 15 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 3 categories and Qwen3 235B-A22B in 4 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3 235B-A22B leads 50.4 to 38.5.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 57.4% for DeepSeek-V3.1-Terminus and 69.4% for Qwen3 235B-A22B.
- DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
- DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 131K.
Side by side
| DeepSeek-V3.1-Terminus | Qwen3 235B-A22B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 43.1 | 43.5 |
| Released | 2025-09-22 | 2025-04 |
| Weights | Open | Open |
| Context window | 164K | 131K |
| Max output | 147K | 16K |
| Input $ / M tokens | $0.27 | $0.70 |
| Output $ / M tokens | $1 | $2.80 |
| Results tracked | 16 | 49 |
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Category by category
Coding Qwen3 235B-A22B leads
DeepSeek-V3.1-Terminus: 42.0 (#113), Qwen3 235B-A22B: 44.3 (#75)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3 235B-A22B |
|---|---|---|
| SciCode | 40.6% | 42.4% |
| LMArena Coding | 1426 | 1445 |
| Aider Polyglot | — | 59.6% |
| WeirdML | — | 41% |
| ALE-Bench | 745.17 | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.1-Terminus: —, Qwen3 235B-A22B: 33.9 (#51)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 26.4 (#133), Qwen3 235B-A22B: 15.7 (#311)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3 235B-A22B |
|---|---|---|
| Kagi LLM Benchmark | 57.4% | 69.4% |
| CritPt | 1.7% | 0% |
| LMArena Hard Prompts | 1426 | 1433 |
| DTBench | 81.3% | 80.3% |
| LMCA | 28.6% | 29.3% |
| ARC-AGI-2 | — | 1.3% |
| SimpleBench | — | 31% |
| ARC-AGI-1 | — | 11% |
| Chess Puzzles | — | 12% |
| Mystery Game Puzzles | — | 9% |
| Epoch Capabilities Index | — | 143.85 |
| ForecastBench | — | 59.7 |
Math Qwen3 235B-A22B leads
DeepSeek-V3.1-Terminus: 38.5 (#137), Qwen3 235B-A22B: 50.4 (#57)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3 235B-A22B |
|---|---|---|
| LMArena Math | 1402 | 1432 |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| Omni-MATH | — | 71.8% |
| MATH Level 5 | — | 68.9% |
| FrontierMath (Feb 2025 set) | — | 8.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, Qwen3 235B-A22B: 49.6 (#73)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3 235B-A22B |
|---|---|---|
| GPQA Diamond | — | 80.1% |
| SimpleQA Verified | — | 40.4% |
| MMLU-Pro | — | 84.4% |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 72.7% |
| LMArena Expert | — | 1463 |
Multilingual Too close to call
DeepSeek-V3.1-Terminus: 52.1 (#92), Qwen3 235B-A22B: 52.3 (#89)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1407 | 1409 |
| LMArena Russian | 1436 | 1411 |
| LMArena Chinese | — | 1481 |
| LMArena French | — | 1445 |
| LMArena German | — | 1433 |
| LMArena Japanese | — | 1399 |
| LMArena Korean | — | 1391 |
| LMArena Spanish | — | 1430 |
Instruction Following DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 74.0 (#106), Qwen3 235B-A22B: 72.6 (#136)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3 235B-A22B |
|---|---|---|
| LMArena Instruction Following | 1404 | 1408 |
| IFEval | — | 83.5% |
Long Context Qwen3 235B-A22B leads
DeepSeek-V3.1-Terminus: 43.4 (#97), Qwen3 235B-A22B: 46.1 (#26)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3 235B-A22B |
|---|---|---|
| LMArena Longer Query | 1421 | 1426 |
| Fiction.LiveBench | — | 75% |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.0 (#92), Qwen3 235B-A22B: 59.6 (#108)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1419 | 1419 |
| LMArena Creative Writing | 1403 | 1384 |
| LMArena Multi-Turn | 1411 | 1432 |
| Short-Story Creative Writing | — | 83% |
| EQ-Bench Creative Writing | — | 1366 |
| WildBench | — | 86.6% |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Qwen3 235B-A22B?
DeepSeek-V3.1-Terminus and Qwen3 235B-A22B score almost the same on the Noometry Index (43.1 vs 43.5), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-V3.1-Terminus or Qwen3 235B-A22B?
DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.
Is DeepSeek-V3.1-Terminus or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 44.3 versus 42.0 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.1-Terminus does, with 164K tokens against 131K.
How many benchmarks do DeepSeek-V3.1-Terminus and Qwen3 235B-A22B share?
15 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Qwen3 235B-A22B has 49.