Model comparison
DeepSeek-V3.1-Terminus vs Qwen3-1.7B
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 26.6 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- The widest gap is in math, where DeepSeek-V3.1-Terminus leads 38.5 to 16.3.
Side by side
| DeepSeek-V3.1-Terminus | Qwen3-1.7B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 43.1 | 26.6 |
| Released | 2025-09-22 | 2025-04-29 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 147K | — |
| Input $ / M tokens | $0.27 | — |
| Output $ / M tokens | $1 | — |
| Results tracked | 16 | 4 |
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Category by category
Coding Not comparable
DeepSeek-V3.1-Terminus: 42.0 (#113), Qwen3-1.7B: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3-1.7B |
|---|---|---|
| SciCode | 40.6% | — |
| LMArena Coding | 1426 | — |
| ALE-Bench | 745.17 | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.1-Terminus: —, Qwen3-1.7B: 24.7 (#115)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3-1.7B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.4% |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 26.4 (#133), Qwen3-1.7B: 19.2 (#267)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3-1.7B |
|---|---|---|
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1426 | — |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
Math DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 38.5 (#137), Qwen3-1.7B: 16.3 (#294)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3-1.7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 8.1% |
| LMArena Math | 1402 | — |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, Qwen3-1.7B: 19.6 (#278)
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3-1.7B |
|---|---|---|
| GPQA Diamond | — | 38% |
Multilingual Not comparable
DeepSeek-V3.1-Terminus: 52.1 (#92), Qwen3-1.7B: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3-1.7B |
|---|---|---|
| LMArena Non-English | 1407 | — |
| LMArena Russian | 1436 | — |
Instruction Following Not comparable
DeepSeek-V3.1-Terminus: 74.0 (#106), Qwen3-1.7B: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3-1.7B |
|---|---|---|
| LMArena Instruction Following | 1404 | — |
Long Context Not comparable
DeepSeek-V3.1-Terminus: 43.4 (#97), Qwen3-1.7B: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3-1.7B |
|---|---|---|
| LMArena Longer Query | 1421 | — |
Writing & Preference Not comparable
DeepSeek-V3.1-Terminus: 61.0 (#92), Qwen3-1.7B: —
| Benchmark | DeepSeek-V3.1-Terminus | Qwen3-1.7B |
|---|---|---|
| LMArena Text | 1419 | — |
| LMArena Creative Writing | 1403 | — |
| LMArena Multi-Turn | 1411 | — |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Qwen3-1.7B?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 26.6 on the Noometry Index.
How many benchmarks do DeepSeek-V3.1-Terminus and Qwen3-1.7B share?
0 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Qwen3-1.7B has 4.