Model comparison
DeepSeek-V3.1-Terminus vs Qwen3.7 Max
Qwen3.7 Max is the stronger model overall, scoring 51.4 to 42.6 on the Noometry Index. DeepSeek-V3.1-Terminus costs 7.9× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.
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 0 categories and Qwen3.7 Max in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.7 Max leads 62.3 to 35.7.
- The biggest single-benchmark swing is LMCA: 28.6% for DeepSeek-V3.1-Terminus and 44% for Qwen3.7 Max.
- DeepSeek-V3.1-Terminus is cheaper at $0.30 / $1 per million input/output tokens, against $2.50 / $7.50 for Qwen3.7 Max.
- Qwen3.7 Max accepts more context: 1M tokens versus 164K.
- DeepSeek-V3.1-Terminus has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1-Terminus | Qwen3.7 Max | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.6 | 51.4 |
| Released | 2025-09-22 | 2026-05-19 |
| Weights | Open | Proprietary |
| Context window | 164K | 1M |
| Max output | 66K | 131K |
| Input $ / M tokens | $0.30 | $2.50 |
| Output $ / M tokens | $1 | $7.50 |
| Results tracked | 16 | 33 |
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Category by category
Coding Qwen3.7 Max leads
DeepSeek-V3.1-Terminus: 41.5 (#114), Qwen3.7 Max: 50.2 (#43)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3.7 Max |
|---|---|---|
| SciCode | 40.6% | 48.8% |
| LMArena Coding | 1426 | 1498 |
| ALE-Bench | 745.17 | 1,189 |
| SWE-bench Verified | — | 77.3% |
| LMArena WebDev | — | 1515 |
Agentic & Tool Use Not comparable
DeepSeek-V3.1-Terminus: —, Qwen3.7 Max: 21.8 (#129)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3.7 Max |
|---|---|---|
| GBAEval | — | 0.4% |
Reasoning Qwen3.7 Max leads
DeepSeek-V3.1-Terminus: 25.7 (#126), Qwen3.7 Max: 48.4 (#36)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3.7 Max |
|---|---|---|
| CritPt | 1.7% | 13.4% |
| LMArena Hard Prompts | 1427 | 1483 |
| DTBench | 81.3% | 92.3% |
| LMCA | 28.6% | 44% |
| SimpleBench | — | 70.4% |
| Kagi LLM Benchmark | 57.4% | — |
| NYT Connections (extended) | — | 85.1% |
| Chess Puzzles | — | 19% |
| EBR-Bench | — | 9.5% |
| Mystery Game Puzzles | — | 32% |
| Epoch Capabilities Index | — | 153.74 |
Math Qwen3.7 Max leads
DeepSeek-V3.1-Terminus: 35.7 (#138), Qwen3.7 Max: 62.3 (#26)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3.7 Max |
|---|---|---|
| LMArena Math | 1402 | 1488 |
| FrontierMath (Tiers 1-3) | — | 64.6% |
| FrontierMath Tier 4 | — | 34.1% |
| OTIS Mock AIME 2024-2025 | — | 95.6% |
| ProofBench | — | 26% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, Qwen3.7 Max: 61.7 (#24)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | — | 90.9% |
| SimpleQA Verified | — | 55.8% |
| LMArena Expert | — | 1488 |
Multilingual Qwen3.7 Max leads
DeepSeek-V3.1-Terminus: 52.3 (#87), Qwen3.7 Max: 57.1 (#13)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1407 | 1475 |
| LMArena Russian | 1434 | 1482 |
| LMArena Chinese | — | 1533 |
Instruction Following Qwen3.7 Max leads
DeepSeek-V3.1-Terminus: 74.4 (#96), Qwen3.7 Max: 76.9 (#34)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1406 | 1460 |
Long Context Qwen3.7 Max leads
DeepSeek-V3.1-Terminus: 43.5 (#88), Qwen3.7 Max: 45.4 (#34)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1426 | 1484 |
Writing & Preference Qwen3.7 Max leads
DeepSeek-V3.1-Terminus: 61.3 (#87), Qwen3.7 Max: 65.2 (#52)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1420 | 1476 |
| LMArena Creative Writing | 1404 | 1450 |
| LMArena Multi-Turn | 1411 | 1482 |
| EQ-Bench 4 | — | 1110 |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Qwen3.7 Max?
Qwen3.7 Max is the stronger model overall, scoring 51.4 to 42.6 on the Noometry Index. DeepSeek-V3.1-Terminus costs 7.9× less per token, which makes it the better buy when Qwen3.7 Max's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1-Terminus or Qwen3.7 Max?
DeepSeek-V3.1-Terminus is cheaper. It lists at $0.30 per million input tokens and $1 per million output tokens; Qwen3.7 Max lists at $2.50 and $7.50.
Is DeepSeek-V3.1-Terminus or Qwen3.7 Max better for coding?
Qwen3.7 Max scores higher on coding benchmarks: 50.2 versus 41.5 in the Noometry coding category.
Which has the bigger context window?
Qwen3.7 Max does, with 1M tokens against 164K.
How many benchmarks do DeepSeek-V3.1-Terminus and Qwen3.7 Max share?
15 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Qwen3.7 Max has 33.