Model comparison
o4-mini vs Yi-1.5-34B
o4-mini is the stronger model overall, scoring 41.6 to 30.6 on the Noometry Index.
Last verified . 19 shared benchmarks.
Summary
- They share 19 benchmarks with published results for both. o4-mini 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 o4-mini leads 43.6 to 14.8.
- The biggest single-benchmark swing is MATH Level 5: 97.8% for o4-mini and 25.5% for Yi-1.5-34B.
- Yi-1.5-34B has downloadable open weights; the other is API-only.
Side by side
| o4-mini | Yi-1.5-34B | |
|---|---|---|
| Provider | OpenAI | 01.AI |
| Noometry Index | 41.6 | 30.6 |
| Released | 2025-04-16 | 2024-05-13 |
| Weights | Proprietary | Open |
| Context window | 200K | — |
| Max output | 100K | — |
| Input $ / M tokens | $1.10 | — |
| Output $ / M tokens | $4.40 | — |
| Results tracked | 60 | 21 |
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Category by category
Coding o4-mini leads
o4-mini: 40.9 (#127), Yi-1.5-34B: 32.4 (#272)
| Benchmark | o4-mini | Yi-1.5-34B |
|---|---|---|
| LMArena Coding | 1368 | 1169 |
| SWE-bench Verified (bash only) | 45% | — |
| Aider Polyglot | 72% | — |
| GSO | 3.6% | — |
| WeirdML | 52.6% | — |
| BigCodeBench Instruct | — | 33.9% |
| BigCodeBench Complete | — | 43.8% |
| CadEval | 62% | — |
| ALE-Bench | 826.17 | — |
| AlgoTune | 1.72 | — |
Agentic & Tool Use Not comparable
o4-mini: 32.6 (#61), Yi-1.5-34B: —
| Benchmark | o4-mini | Yi-1.5-34B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 53.2% | — |
| GDPval | 25.3% | — |
| METR Time Horizons | 63.9% | — |
Reasoning o4-mini leads
o4-mini: 24.6 (#162), Yi-1.5-34B: 22.5 (#191)
| Benchmark | o4-mini | Yi-1.5-34B |
|---|---|---|
| LMArena Hard Prompts | 1351 | 1160 |
| ARC-AGI-2 | 6.1% | — |
| SimpleBench | 38.7% | — |
| Kagi LLM Benchmark | 67.6% | — |
| ARC-AGI-1 | 58.7% | — |
| CritPt | 0.6% | — |
| Chess Puzzles | 26% | — |
| EnigmaEval | 9.2% | — |
| Mystery Game Puzzles | 5% | — |
| DTBench | 77.6% | — |
| LMCA | 26.5% | — |
| Epoch Capabilities Index | 145.64 | — |
| ForecastBench | 61.8 | — |
Math o4-mini leads
o4-mini: 40.8 (#89), Yi-1.5-34B: 27.5 (#249)
| Benchmark | o4-mini | Yi-1.5-34B |
|---|---|---|
| LMArena Math | 1389 | 1182 |
| MATH Level 5 | 97.8% | 25.5% |
| FrontierMath (Tiers 1-3) | 36.1% | — |
| FrontierMath Tier 4 | 4.9% | — |
| OTIS Mock AIME 2024-2025 | 81.7% | — |
| Omni-MATH | 72% | — |
| FrontierMath (Feb 2025 set) | 24.8% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge o4-mini leads
o4-mini: 43.6 (#91), Yi-1.5-34B: 14.8 (#295)
| Benchmark | o4-mini | Yi-1.5-34B |
|---|---|---|
| GPQA Diamond | 79.6% | 32% |
| LMArena Expert | 1343 | 1144 |
| Humanity's Last Exam | 18.1% | — |
| SimpleQA Verified | 19.6% | — |
| MMLU-Pro | 82% | — |
| Confabulations | 15.8% | — |
| Vectara Hallucination Rate | 18.6% | — |
| GPQA (HELM) | 73.5% | — |
Multimodal Not comparable
o4-mini: 40.2 (#49), Yi-1.5-34B: —
| Benchmark | o4-mini | Yi-1.5-34B |
|---|---|---|
| LMArena Vision | 1194 | — |
| GeoBench | 64% | — |
| VPCT | 57.5% | — |
Multilingual o4-mini leads
o4-mini: 47.0 (#154), Yi-1.5-34B: 32.3 (#256)
| Benchmark | o4-mini | Yi-1.5-34B |
|---|---|---|
| LMArena Non-English | 1337 | 1121 |
| LMArena Chinese | 1354 | 1213 |
| LMArena French | 1364 | 1156 |
| LMArena German | 1336 | 1111 |
| LMArena Japanese | 1308 | 1021 |
| LMArena Korean | 1312 | 1005 |
| LMArena Russian | 1334 | 1091 |
| LMArena Spanish | 1347 | 1121 |
Instruction Following o4-mini leads
o4-mini: 75.2 (#68), Yi-1.5-34B: 59.2 (#257)
| Benchmark | o4-mini | Yi-1.5-34B |
|---|---|---|
| LMArena Instruction Following | 1321 | 1139 |
| IFEval | 92.8% | — |
Long Context o4-mini leads
o4-mini: 45.5 (#33), Yi-1.5-34B: 34.6 (#248)
| Benchmark | o4-mini | Yi-1.5-34B |
|---|---|---|
| LMArena Longer Query | 1315 | 1143 |
| Fiction.LiveBench | 77.8% | — |
Writing & Preference o4-mini leads
o4-mini: 54.0 (#152), Yi-1.5-34B: 37.4 (#257)
| Benchmark | o4-mini | Yi-1.5-34B |
|---|---|---|
| LMArena Text | 1353 | 1173 |
| LMArena Creative Writing | 1294 | 1135 |
| LMArena Multi-Turn | 1350 | 1153 |
| Short-Story Creative Writing | 75% | — |
| WildBench | 85.4% | — |
Frequently asked questions
Is o4-mini better than Yi-1.5-34B?
o4-mini is the stronger model overall, scoring 41.6 to 30.6 on the Noometry Index.
Is o4-mini or Yi-1.5-34B better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 32.4 in the Noometry coding category.
How many benchmarks do o4-mini and Yi-1.5-34B share?
19 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Yi-1.5-34B has 21.