Model comparison
o3-pro vs Yi-1.5-34B
o3-pro is the stronger model overall, scoring 42.9 to 30.6 on the Noometry Index.
Last verified . 0 shared benchmarks.
Summary
- The widest gap is in long context, where o3-pro leads 72.2 to 34.6.
- Yi-1.5-34B has downloadable open weights; the other is API-only.
Side by side
| o3-pro | Yi-1.5-34B | |
|---|---|---|
| Provider | OpenAI | 01.AI |
| Noometry Index | 42.9 | 30.6 |
| Released | 2025-06-10 | 2024-05-13 |
| Weights | Proprietary | Open |
| Context window | 200K | — |
| Max output | 100K | — |
| Input $ / M tokens | $20 | — |
| Output $ / M tokens | $80 | — |
| Results tracked | 12 | 21 |
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Category by category
Coding o3-pro leads
o3-pro: 55.5 (#24), Yi-1.5-34B: 32.4 (#272)
| Benchmark | o3-pro | Yi-1.5-34B |
|---|---|---|
| Aider Polyglot | 84.9% | — |
| WeirdML | 58.2% | — |
| BigCodeBench Instruct | — | 33.9% |
| LMArena Coding | — | 1169 |
| BigCodeBench Complete | — | 43.8% |
Reasoning o3-pro leads
o3-pro: 23.8 (#171), Yi-1.5-34B: 22.5 (#191)
| Benchmark | o3-pro | Yi-1.5-34B |
|---|---|---|
| ARC-AGI-2 | 4.9% | — |
| Kagi LLM Benchmark | 72.1% | — |
| ARC-AGI-1 | 59.3% | — |
| LMArena Hard Prompts | — | 1160 |
| DTBench | 86.9% | — |
| LMCA | 38.5% | — |
| Epoch Capabilities Index | 147.42 | — |
Math Not comparable
o3-pro: —, Yi-1.5-34B: 27.5 (#249)
| Benchmark | o3-pro | Yi-1.5-34B |
|---|---|---|
| LMArena Math | — | 1182 |
| MATH Level 5 | — | 25.5% |
Knowledge o3-pro leads
o3-pro: 29.5 (#238), Yi-1.5-34B: 14.8 (#295)
| Benchmark | o3-pro | Yi-1.5-34B |
|---|---|---|
| GPQA Diamond | — | 32% |
| Confabulations | 14.2% | — |
| Vectara Hallucination Rate | 23.3% | — |
| LMArena Expert | — | 1144 |
Multilingual Not comparable
o3-pro: —, Yi-1.5-34B: 32.3 (#256)
| Benchmark | o3-pro | Yi-1.5-34B |
|---|---|---|
| LMArena Non-English | — | 1121 |
| LMArena Chinese | — | 1213 |
| LMArena French | — | 1156 |
| LMArena German | — | 1111 |
| LMArena Japanese | — | 1021 |
| LMArena Korean | — | 1005 |
| LMArena Russian | — | 1091 |
| LMArena Spanish | — | 1121 |
Instruction Following Not comparable
o3-pro: —, Yi-1.5-34B: 59.2 (#257)
| Benchmark | o3-pro | Yi-1.5-34B |
|---|---|---|
| LMArena Instruction Following | — | 1139 |
Long Context o3-pro leads
o3-pro: 72.2 (#1), Yi-1.5-34B: 34.6 (#248)
| Benchmark | o3-pro | Yi-1.5-34B |
|---|---|---|
| Fiction.LiveBench | 97.2% | — |
| LMArena Longer Query | — | 1143 |
Writing & Preference o3-pro leads
o3-pro: 57.1 (#133), Yi-1.5-34B: 37.4 (#257)
| Benchmark | o3-pro | Yi-1.5-34B |
|---|---|---|
| LMArena Text | — | 1173 |
| LMArena Creative Writing | — | 1135 |
| Short-Story Creative Writing | 84.4% | — |
| LMArena Multi-Turn | — | 1153 |
Frequently asked questions
Is o3-pro better than Yi-1.5-34B?
o3-pro is the stronger model overall, scoring 42.9 to 30.6 on the Noometry Index.
Is o3-pro or Yi-1.5-34B better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 32.4 in the Noometry coding category.
How many benchmarks do o3-pro and Yi-1.5-34B share?
0 benchmarks have published results for both models. o3-pro has 12 scored results on Noometry and Yi-1.5-34B has 21.