Model comparison
o3 vs Yi-1.5-34B
o3 is the stronger model overall, scoring 47.5 to 30.6 on the Noometry Index.
Last verified . 19 shared benchmarks.
Summary
- They share 19 benchmarks with published results for both. o3 scores higher in 8 categories and Yi-1.5-34B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o3 leads 54.6 to 14.8.
- The biggest single-benchmark swing is MATH Level 5: 97.8% for o3 and 25.5% for Yi-1.5-34B.
- Yi-1.5-34B has downloadable open weights; the other is API-only.
Side by side
| o3 | Yi-1.5-34B | |
|---|---|---|
| Provider | OpenAI | 01.AI |
| Noometry Index | 47.5 | 30.6 |
| Released | 2025-04-16 | 2024-05-13 |
| Weights | Proprietary | Open |
| Context window | 200K | — |
| Max output | 100K | — |
| Input $ / M tokens | $2 | — |
| Output $ / M tokens | $8 | — |
| Results tracked | 63 | 21 |
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Category by category
Coding o3 leads
o3: 46.8 (#64), Yi-1.5-34B: 32.4 (#272)
| Benchmark | o3 | Yi-1.5-34B |
|---|---|---|
| LMArena Coding | 1408 | 1169 |
| SWE-bench Verified | 62.3% | — |
| SWE-bench Verified (bash only) | 58.4% | — |
| Aider Polyglot | 81.3% | — |
| GSO | 8.8% | — |
| WeirdML | 52.4% | — |
| BigCodeBench Instruct | — | 33.9% |
| BigCodeBench Complete | — | 43.8% |
| CadEval | 74% | — |
| ALE-Bench | 933.55 | — |
Agentic & Tool Use Not comparable
o3: 34.5 (#44), Yi-1.5-34B: —
| Benchmark | o3 | Yi-1.5-34B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 63% | — |
| GDPval | 30.8% | — |
| DeepResearch Bench | 45.2% | — |
| OSWorld | 23% | — |
| LMArena Search | 1144 | — |
| METR Time Horizons | 65.4% | — |
Reasoning o3 leads
o3: 32.0 (#78), Yi-1.5-34B: 22.5 (#191)
| Benchmark | o3 | Yi-1.5-34B |
|---|---|---|
| LMArena Hard Prompts | 1402 | 1160 |
| ARC-AGI-2 | 6.5% | — |
| SimpleBench | 53.1% | — |
| Kagi LLM Benchmark | 67.6% | — |
| ARC-AGI-1 | 60.8% | — |
| CritPt | 1.4% | — |
| Chess Puzzles | 38% | — |
| EnigmaEval | 13.1% | — |
| Mystery Game Puzzles | 29% | — |
| DTBench | 84.8% | — |
| LMCA | 39.7% | — |
| Epoch Capabilities Index | 146.86 | — |
| ForecastBench | 62.5 | — |
Math o3 leads
o3: 50.2 (#58), Yi-1.5-34B: 27.5 (#249)
| Benchmark | o3 | Yi-1.5-34B |
|---|---|---|
| LMArena Math | 1426 | 1182 |
| MATH Level 5 | 97.8% | 25.5% |
| FrontierMath (Tiers 1-3) | 33.3% | — |
| OTIS Mock AIME 2024-2025 | 84.4% | — |
| Omni-MATH | 71.4% | — |
| FrontierMath (Feb 2025 set) | 18.7% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge o3 leads
o3: 54.6 (#52), Yi-1.5-34B: 14.8 (#295)
| Benchmark | o3 | Yi-1.5-34B |
|---|---|---|
| GPQA Diamond | 81.8% | 32% |
| LMArena Expert | 1402 | 1144 |
| Humanity's Last Exam | 20.3% | — |
| SimpleQA Verified | 49.4% | — |
| MMLU-Pro | 85.9% | — |
| Confabulations | 14.4% | — |
| GPQA (HELM) | 75.3% | — |
Multimodal Not comparable
o3: 41.4 (#36), Yi-1.5-34B: —
| Benchmark | o3 | Yi-1.5-34B |
|---|---|---|
| LMArena Vision | 1214 | — |
| GeoBench | 74% | — |
| VPCT | 52% | — |
Multilingual o3 leads
o3: 51.7 (#105), Yi-1.5-34B: 32.3 (#256)
| Benchmark | o3 | Yi-1.5-34B |
|---|---|---|
| LMArena Non-English | 1401 | 1121 |
| LMArena Chinese | 1437 | 1213 |
| LMArena French | 1430 | 1156 |
| LMArena German | 1420 | 1111 |
| LMArena Japanese | 1403 | 1021 |
| LMArena Korean | 1370 | 1005 |
| LMArena Russian | 1406 | 1091 |
| LMArena Spanish | 1395 | 1121 |
Instruction Following o3 leads
o3: 72.8 (#127), Yi-1.5-34B: 59.2 (#257)
| Benchmark | o3 | Yi-1.5-34B |
|---|---|---|
| LMArena Instruction Following | 1368 | 1139 |
| IFEval | 86.9% | — |
Long Context o3 leads
o3: 53.3 (#6), Yi-1.5-34B: 34.6 (#248)
| Benchmark | o3 | Yi-1.5-34B |
|---|---|---|
| LMArena Longer Query | 1372 | 1143 |
| Fiction.LiveBench | 88.9% | — |
| CL-bench | 17.8% | — |
Writing & Preference o3 leads
o3: 63.5 (#64), Yi-1.5-34B: 37.4 (#257)
| Benchmark | o3 | Yi-1.5-34B |
|---|---|---|
| LMArena Text | 1410 | 1173 |
| LMArena Creative Writing | 1359 | 1135 |
| LMArena Multi-Turn | 1405 | 1153 |
| Short-Story Creative Writing | 83.9% | — |
| EQ-Bench Creative Writing | 1676 | — |
| WildBench | 86.1% | — |
Frequently asked questions
Is o3 better than Yi-1.5-34B?
o3 is the stronger model overall, scoring 47.5 to 30.6 on the Noometry Index.
Is o3 or Yi-1.5-34B better for coding?
o3 scores higher on coding benchmarks: 46.8 versus 32.4 in the Noometry coding category.
How many benchmarks do o3 and Yi-1.5-34B share?
19 benchmarks have published results for both models. o3 has 63 scored results on Noometry and Yi-1.5-34B has 21.