Model comparison
DeepSeek-V3.1-Terminus vs Yi-1.5-34B
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 30.6 on the Noometry Index.
Last verified . 10 shared benchmarks.
DeepSeekSummary
- They share 10 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and Yi-1.5-34B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.0 to 37.4.
Side by side
| DeepSeek-V3.1-Terminus | Yi-1.5-34B | |
|---|---|---|
| Provider | DeepSeek | 01.AI |
| Noometry Index | 43.1 | 30.6 |
| Released | 2025-09-22 | 2024-05-13 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 147K | — |
| Input $ / M tokens | $0.27 | — |
| Output $ / M tokens | $1 | — |
| Results tracked | 16 | 21 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 42.0 (#113), Yi-1.5-34B: 32.4 (#272)
| Benchmark | DeepSeek-V3.1-Terminus | Yi-1.5-34B |
|---|---|---|
| LMArena Coding | 1426 | 1169 |
| SciCode | 40.6% | — |
| BigCodeBench Instruct | — | 33.9% |
| BigCodeBench Complete | — | 43.8% |
| ALE-Bench | 745.17 | — |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 26.4 (#133), Yi-1.5-34B: 22.5 (#191)
| Benchmark | DeepSeek-V3.1-Terminus | Yi-1.5-34B |
|---|---|---|
| LMArena Hard Prompts | 1426 | 1160 |
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
Math DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 38.5 (#137), Yi-1.5-34B: 27.5 (#249)
| Benchmark | DeepSeek-V3.1-Terminus | Yi-1.5-34B |
|---|---|---|
| LMArena Math | 1402 | 1182 |
| MATH Level 5 | — | 25.5% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, Yi-1.5-34B: 14.8 (#295)
| Benchmark | DeepSeek-V3.1-Terminus | Yi-1.5-34B |
|---|---|---|
| GPQA Diamond | — | 32% |
| LMArena Expert | — | 1144 |
Multilingual DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 52.1 (#92), Yi-1.5-34B: 32.3 (#256)
| Benchmark | DeepSeek-V3.1-Terminus | Yi-1.5-34B |
|---|---|---|
| LMArena Non-English | 1407 | 1121 |
| LMArena Russian | 1436 | 1091 |
| LMArena Chinese | — | 1213 |
| LMArena French | — | 1156 |
| LMArena German | — | 1111 |
| LMArena Japanese | — | 1021 |
| LMArena Korean | — | 1005 |
| LMArena Spanish | — | 1121 |
Instruction Following DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 74.0 (#106), Yi-1.5-34B: 59.2 (#257)
| Benchmark | DeepSeek-V3.1-Terminus | Yi-1.5-34B |
|---|---|---|
| LMArena Instruction Following | 1404 | 1139 |
Long Context DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 43.4 (#97), Yi-1.5-34B: 34.6 (#248)
| Benchmark | DeepSeek-V3.1-Terminus | Yi-1.5-34B |
|---|---|---|
| LMArena Longer Query | 1421 | 1143 |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.0 (#92), Yi-1.5-34B: 37.4 (#257)
| Benchmark | DeepSeek-V3.1-Terminus | Yi-1.5-34B |
|---|---|---|
| LMArena Text | 1419 | 1173 |
| LMArena Creative Writing | 1403 | 1135 |
| LMArena Multi-Turn | 1411 | 1153 |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Yi-1.5-34B?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 30.6 on the Noometry Index.
Is DeepSeek-V3.1-Terminus or Yi-1.5-34B better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 32.4 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1-Terminus and Yi-1.5-34B share?
10 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Yi-1.5-34B has 21.