Model comparison
GPT-4o vs Yi-1.5-34B
Yi-1.5-34B is the stronger model overall, scoring 30.6 to 28.6 on the Noometry Index.
Last verified . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. GPT-4o scores higher in 5 categories and Yi-1.5-34B in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Yi-1.5-34B leads 27.5 to 10.6.
- The biggest single-benchmark swing is MATH Level 5: 53.3% for GPT-4o and 25.5% for Yi-1.5-34B.
- Yi-1.5-34B has downloadable open weights; the other is API-only.
Side by side
| GPT-4o | Yi-1.5-34B | |
|---|---|---|
| Provider | OpenAI | 01.AI |
| Noometry Index | 28.6 | 30.6 |
| Released | 2024-05-13 | 2024-05-13 |
| Weights | Proprietary | Open |
| Context window | 128K | — |
| Max output | 16K | — |
| Input $ / M tokens | $2.50 | — |
| Output $ / M tokens | $10 | — |
| Results tracked | 72 | 21 |
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Category by category
Coding Yi-1.5-34B leads
GPT-4o: 24.8 (#328), Yi-1.5-34B: 32.4 (#272)
| Benchmark | GPT-4o | Yi-1.5-34B |
|---|---|---|
| BigCodeBench Instruct | 51.1% | 33.9% |
| LMArena Coding | 1297 | 1169 |
| BigCodeBench Complete | 61.1% | 43.8% |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| GSO | 0% | — |
| WeirdML | 25.1% | — |
| LiveBench Coding | 51.4% | — |
| CadEval | 26% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Not comparable
GPT-4o: 21.0 (#141), Yi-1.5-34B: —
| Benchmark | GPT-4o | Yi-1.5-34B |
|---|---|---|
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| BALROG | 32.3% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |
Reasoning Yi-1.5-34B leads
GPT-4o: 9.4 (#343), Yi-1.5-34B: 22.5 (#191)
| Benchmark | GPT-4o | Yi-1.5-34B |
|---|---|---|
| LMArena Hard Prompts | 1281 | 1160 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 17.8% | — |
| ARC-AGI-1 | 4.5% | — |
| CritPt | 0% | — |
| Chess Puzzles | 13% | — |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| DTBench | 64.5% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 16.6% | — |
| Epoch Capabilities Index | 128.97 | — |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |
Math Yi-1.5-34B leads
GPT-4o: 10.6 (#312), Yi-1.5-34B: 27.5 (#249)
| Benchmark | GPT-4o | Yi-1.5-34B |
|---|---|---|
| LMArena Math | 1285 | 1182 |
| MATH Level 5 | 53.3% | 25.5% |
| FrontierMath (Tiers 1-3) | 0.4% | — |
| OTIS Mock AIME 2024-2025 | 6.4% | — |
| Omni-MATH | 29.3% | — |
| LiveBench Math | 49.5% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge GPT-4o leads
GPT-4o: 28.8 (#242), Yi-1.5-34B: 14.8 (#295)
| Benchmark | GPT-4o | Yi-1.5-34B |
|---|---|---|
| GPQA Diamond | 49.2% | 32% |
| LMArena Expert | 1250 | 1144 |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| MMLU-Pro | 71.3% | — |
| Confabulations | 15.3% | — |
| Vectara Hallucination Rate | 9.6% | — |
| GPQA (HELM) | 52% | — |
| MMLU | 88.1% | — |
Multimodal Not comparable
GPT-4o: 34.5 (#91), Yi-1.5-34B: —
| Benchmark | GPT-4o | Yi-1.5-34B |
|---|---|---|
| LMArena Vision | 1137 | — |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| ScienceQA | 88.5% | — |
Multilingual GPT-4o leads
GPT-4o: 43.2 (#186), Yi-1.5-34B: 32.3 (#256)
| Benchmark | GPT-4o | Yi-1.5-34B |
|---|---|---|
| LMArena Non-English | 1283 | 1121 |
| LMArena Chinese | 1277 | 1213 |
| LMArena French | 1304 | 1156 |
| LMArena German | 1282 | 1111 |
| LMArena Japanese | 1257 | 1021 |
| LMArena Korean | 1234 | 1005 |
| LMArena Russian | 1286 | 1091 |
| LMArena Spanish | 1292 | 1121 |
Instruction Following GPT-4o leads
GPT-4o: 66.6 (#207), Yi-1.5-34B: 59.2 (#257)
| Benchmark | GPT-4o | Yi-1.5-34B |
|---|---|---|
| LMArena Instruction Following | 1278 | 1139 |
| LiveBench Instruction Following | 68.6% | — |
| IFEval | 81.7% | — |
Long Context GPT-4o leads
GPT-4o: 39.4 (#179), Yi-1.5-34B: 34.6 (#248)
| Benchmark | GPT-4o | Yi-1.5-34B |
|---|---|---|
| LMArena Longer Query | 1289 | 1143 |
| Fiction.LiveBench | 66.7% | — |
Writing & Preference GPT-4o leads
GPT-4o: 52.6 (#166), Yi-1.5-34B: 37.4 (#257)
| Benchmark | GPT-4o | Yi-1.5-34B |
|---|---|---|
| LMArena Text | 1300 | 1173 |
| LMArena Creative Writing | 1292 | 1135 |
| LMArena Multi-Turn | 1302 | 1153 |
| Short-Story Creative Writing | 81.8% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 47.6% | — |
Frequently asked questions
Is GPT-4o better than Yi-1.5-34B?
Yi-1.5-34B is the stronger model overall, scoring 30.6 to 28.6 on the Noometry Index.
Is GPT-4o or Yi-1.5-34B better for coding?
Yi-1.5-34B scores higher on coding benchmarks: 32.4 versus 24.8 in the Noometry coding category.
How many benchmarks do GPT-4o and Yi-1.5-34B share?
21 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and Yi-1.5-34B has 21.