Model comparison
DeepSeek-V3.2-Exp vs Yi-1.5-34B
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 30.6 on the Noometry Index.
Last verified . 18 shared benchmarks.
DeepSeekSummary
- They share 18 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 7 categories and Yi-1.5-34B in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 14.8.
- The biggest single-benchmark swing is GPQA Diamond: 83.4% for DeepSeek-V3.2-Exp and 32% for Yi-1.5-34B.
Side by side
| DeepSeek-V3.2-Exp | Yi-1.5-34B | |
|---|---|---|
| Provider | DeepSeek | 01.AI |
| Noometry Index | 44.3 | 30.6 |
| Released | 2025-09-29 | 2024-05-13 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 66K | — |
| Input $ / M tokens | $0.26 | — |
| Output $ / M tokens | $0.38 | — |
| Results tracked | 49 | 21 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), Yi-1.5-34B: 32.4 (#272)
| Benchmark | DeepSeek-V3.2-Exp | Yi-1.5-34B |
|---|---|---|
| LMArena Coding | 1454 | 1169 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| BigCodeBench Instruct | — | 33.9% |
| BigCodeBench Complete | — | 43.8% |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 32.7 (#59), Yi-1.5-34B: —
| Benchmark | DeepSeek-V3.2-Exp | Yi-1.5-34B |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning Too close to call
DeepSeek-V3.2-Exp: 22.1 (#208), Yi-1.5-34B: 22.5 (#191)
| Benchmark | DeepSeek-V3.2-Exp | Yi-1.5-34B |
|---|---|---|
| LMArena Hard Prompts | 1434 | 1160 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.27 | — |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 41.7 (#87), Yi-1.5-34B: 27.5 (#249)
| Benchmark | DeepSeek-V3.2-Exp | Yi-1.5-34B |
|---|---|---|
| LMArena Math | 1435 | 1182 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| MATH Level 5 | — | 25.5% |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.7 (#66), Yi-1.5-34B: 14.8 (#295)
| Benchmark | DeepSeek-V3.2-Exp | Yi-1.5-34B |
|---|---|---|
| GPQA Diamond | 83.4% | 32% |
| LMArena Expert | 1436 | 1144 |
| Vectara Hallucination Rate | 5.3% | — |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), Yi-1.5-34B: 32.3 (#256)
| Benchmark | DeepSeek-V3.2-Exp | Yi-1.5-34B |
|---|---|---|
| LMArena Non-English | 1409 | 1121 |
| LMArena Chinese | 1461 | 1213 |
| LMArena French | 1433 | 1156 |
| LMArena German | 1440 | 1111 |
| LMArena Japanese | 1374 | 1021 |
| LMArena Korean | 1371 | 1005 |
| LMArena Russian | 1424 | 1091 |
| LMArena Spanish | 1440 | 1121 |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.5 (#93), Yi-1.5-34B: 59.2 (#257)
| Benchmark | DeepSeek-V3.2-Exp | Yi-1.5-34B |
|---|---|---|
| LMArena Instruction Following | 1413 | 1139 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), Yi-1.5-34B: 34.6 (#248)
| Benchmark | DeepSeek-V3.2-Exp | Yi-1.5-34B |
|---|---|---|
| LMArena Longer Query | 1428 | 1143 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.4 (#77), Yi-1.5-34B: 37.4 (#257)
| Benchmark | DeepSeek-V3.2-Exp | Yi-1.5-34B |
|---|---|---|
| LMArena Text | 1425 | 1173 |
| LMArena Creative Writing | 1403 | 1135 |
| LMArena Multi-Turn | 1427 | 1153 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Yi-1.5-34B?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 30.6 on the Noometry Index.
Is DeepSeek-V3.2-Exp or Yi-1.5-34B better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 32.4 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Exp and Yi-1.5-34B share?
18 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Yi-1.5-34B has 21.