Model comparison
Gemini 1.5 Pro (May 2024) vs gpt-oss-120b
gpt-oss-120b is the stronger model overall, scoring 36.3 to 32.1 on the Noometry Index.
Last verified . 29 shared benchmarks.
GoogleSummary
- They share 29 benchmarks with published results for both. Gemini 1.5 Pro (May 2024) scores higher in 4 categories and gpt-oss-120b in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 52.5 to 25.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 23.1% for Gemini 1.5 Pro (May 2024) and 88.9% for gpt-oss-120b.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| Gemini 1.5 Pro (May 2024) | gpt-oss-120b | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 32.1 | 36.3 |
| Released | 2024-02-15 | 2025-08-05 |
| Weights | Proprietary | Open |
| Context window | — | 131K |
| Max output | — | 41K |
| Input $ / M tokens | — | $0.037 |
| Output $ / M tokens | — | $0.17 |
| Results tracked | 45 | 48 |
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Category by category
Coding Too close to call
Gemini 1.5 Pro (May 2024): 34.2 (#241), gpt-oss-120b: 33.5 (#256)
| Benchmark | Gemini 1.5 Pro (May 2024) | gpt-oss-120b |
|---|---|---|
| WeirdML | 22.2% | 48.2% |
| LMArena Coding | 1294 | 1380 |
| SWE-bench Verified (bash only) | — | 26% |
| Aider Polyglot | — | 41.8% |
| SciCode | — | 36% |
| BigCodeBench Instruct | 43.8% | — |
| BigCodeBench Complete | 57.5% | — |
| CadEval | 34% | — |
| ALE-Bench | — | 575.62 |
| AlgoTune | — | 1.41 |
| HumanEval+ | 79.3% | — |
| MBPP+ | 74.6% | — |
Agentic & Tool Use Gemini 1.5 Pro (May 2024) leads
Gemini 1.5 Pro (May 2024): 17.9 (#145), gpt-oss-120b: 12.2 (#153)
| Benchmark | Gemini 1.5 Pro (May 2024) | gpt-oss-120b |
|---|---|---|
| Terminal-Bench | — | 18.7% |
| APEX-Agents | — | 4.4% |
| TheAgentCompany | 3.4% | — |
| Cybench | 7.5% | — |
| BALROG | 21% | — |
| METR Time Horizons | — | 56.6% |
| Vending-Bench 2 | — | -21.53 |
Reasoning gpt-oss-120b leads
Gemini 1.5 Pro (May 2024): 12.3 (#338), gpt-oss-120b: 20.0 (#245)
| Benchmark | Gemini 1.5 Pro (May 2024) | gpt-oss-120b |
|---|---|---|
| SimpleBench | 27.1% | 22.1% |
| LMArena Hard Prompts | 1296 | 1364 |
| DTBench | 59% | 76.3% |
| Epoch Capabilities Index | 131.73 | 139.93 |
| ARC-AGI-2 | 0.8% | — |
| Kagi LLM Benchmark | — | 58.6% |
| CritPt | — | 1.1% |
| Chess Puzzles | — | 20% |
| Mystery Game Puzzles | — | 2% |
| LMCA | — | 22.1% |
| Surface Evolver Bench | — | 25% |
| BIG-Bench Hard | 89.2% | — |
| ForecastBench | 58.4 | — |
Math gpt-oss-120b leads
Gemini 1.5 Pro (May 2024): 25.8 (#266), gpt-oss-120b: 52.5 (#50)
| Benchmark | Gemini 1.5 Pro (May 2024) | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 23.1% | 88.9% |
| Omni-MATH | 36.4% | 68.8% |
| LMArena Math | 1315 | 1389 |
| MATH Level 5 | 70.4% | — |
Knowledge gpt-oss-120b leads
Gemini 1.5 Pro (May 2024): 29.4 (#239), gpt-oss-120b: 42.4 (#96)
| Benchmark | Gemini 1.5 Pro (May 2024) | gpt-oss-120b |
|---|---|---|
| GPQA Diamond | 57.2% | 75.8% |
| MMLU-Pro | 73.7% | 79.5% |
| Confabulations | 13.5% | 15.7% |
| GPQA (HELM) | 53.4% | 68.4% |
| LMArena Expert | 1279 | 1356 |
| Humanity's Last Exam | 4.6% | — |
| Vectara Hallucination Rate | — | 14.2% |
| MMLU | 86.9% | — |
Multimodal Not comparable
Gemini 1.5 Pro (May 2024): 36.8 (#77), gpt-oss-120b: —
| Benchmark | Gemini 1.5 Pro (May 2024) | gpt-oss-120b |
|---|---|---|
| LMArena Vision | 1161 | — |
| Video-MME | 75% | — |
Multilingual gpt-oss-120b leads
Gemini 1.5 Pro (May 2024): 45.3 (#174), gpt-oss-120b: 48.0 (#147)
| Benchmark | Gemini 1.5 Pro (May 2024) | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1312 | 1351 |
| LMArena Chinese | 1331 | 1385 |
| LMArena French | 1302 | 1369 |
| LMArena German | 1286 | 1353 |
| LMArena Japanese | 1292 | 1331 |
| LMArena Korean | 1298 | 1282 |
| LMArena Russian | 1320 | 1343 |
| LMArena Spanish | 1311 | 1389 |
Instruction Following Too close to call
Gemini 1.5 Pro (May 2024): 68.6 (#185), gpt-oss-120b: 69.3 (#173)
| Benchmark | Gemini 1.5 Pro (May 2024) | gpt-oss-120b |
|---|---|---|
| IFEval | 83.7% | 83.6% |
| LMArena Instruction Following | 1297 | 1318 |
Long Context Gemini 1.5 Pro (May 2024) leads
Gemini 1.5 Pro (May 2024): 39.8 (#169), gpt-oss-120b: 31.4 (#278)
| Benchmark | Gemini 1.5 Pro (May 2024) | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1308 | 1319 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference Gemini 1.5 Pro (May 2024) leads
Gemini 1.5 Pro (May 2024): 52.4 (#172), gpt-oss-120b: 46.5 (#217)
| Benchmark | Gemini 1.5 Pro (May 2024) | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1319 | 1365 |
| LMArena Creative Writing | 1333 | 1275 |
| WildBench | 81.3% | 84.5% |
| LMArena Multi-Turn | 1296 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| EQ-Bench Creative Writing | — | 961 |
Frequently asked questions
Is Gemini 1.5 Pro (May 2024) better than gpt-oss-120b?
gpt-oss-120b is the stronger model overall, scoring 36.3 to 32.1 on the Noometry Index.
Is Gemini 1.5 Pro (May 2024) or gpt-oss-120b better for coding?
They score almost the same on coding (34.2 vs 33.5); test both on your own repository before choosing.
How many benchmarks do Gemini 1.5 Pro (May 2024) and gpt-oss-120b share?
29 benchmarks have published results for both models. Gemini 1.5 Pro (May 2024) has 45 scored results on Noometry and gpt-oss-120b has 48.