Model comparison
Gemini 1.5 Flash (May 2024) vs gpt-oss-120b
gpt-oss-120b is the stronger model overall, scoring 36.3 to 33.2 on the Noometry Index.
Last verified . 27 shared benchmarks.
GoogleSummary
- They share 27 benchmarks with published results for both. Gemini 1.5 Flash (May 2024) scores higher in 5 categories and gpt-oss-120b in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 52.5 to 22.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 16.3% for Gemini 1.5 Flash (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 Flash (May 2024) | gpt-oss-120b | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 33.2 | 36.3 |
| Released | 2024-05-14 | 2025-08-05 |
| Weights | Proprietary | Open |
| Context window | — | 131K |
| Max output | — | 41K |
| Input $ / M tokens | — | $0.037 |
| Output $ / M tokens | — | $0.17 |
| Results tracked | 42 | 48 |
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Category by category
Coding Too close to call
Gemini 1.5 Flash (May 2024): 34.4 (#236), gpt-oss-120b: 33.5 (#256)
| Benchmark | Gemini 1.5 Flash (May 2024) | gpt-oss-120b |
|---|---|---|
| WeirdML | 24.9% | 48.2% |
| LMArena Coding | 1261 | 1380 |
| SWE-bench Verified (bash only) | — | 26% |
| Aider Polyglot | — | 41.8% |
| SciCode | — | 36% |
| BigCodeBench Instruct | 43.5% | — |
| BigCodeBench Complete | 55.1% | — |
| ALE-Bench | — | 575.62 |
| AlgoTune | — | 1.41 |
| HumanEval+ | 75.6% | — |
| MBPP+ | 67.5% | — |
Agentic & Tool Use Gemini 1.5 Flash (May 2024) leads
Gemini 1.5 Flash (May 2024): 26.6 (#102), gpt-oss-120b: 12.2 (#153)
| Benchmark | Gemini 1.5 Flash (May 2024) | gpt-oss-120b |
|---|---|---|
| Terminal-Bench | — | 18.7% |
| APEX-Agents | — | 4.4% |
| BALROG | 14.6% | — |
| METR Time Horizons | — | 56.6% |
| Vending-Bench 2 | — | -21.53 |
Reasoning Gemini 1.5 Flash (May 2024) leads
Gemini 1.5 Flash (May 2024): 21.7 (#215), gpt-oss-120b: 20.0 (#245)
| Benchmark | Gemini 1.5 Flash (May 2024) | gpt-oss-120b |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1364 |
| DTBench | 53.8% | 76.3% |
| Epoch Capabilities Index | 129.36 | 139.93 |
| SimpleBench | — | 22.1% |
| Kagi LLM Benchmark | — | 58.6% |
| CritPt | — | 1.1% |
| Chess Puzzles | — | 20% |
| Mystery Game Puzzles | — | 2% |
| LMCA | — | 22.1% |
| Surface Evolver Bench | — | 25% |
| ForecastBench | 53.9 | — |
| PIQA | 87.5% | — |
Math gpt-oss-120b leads
Gemini 1.5 Flash (May 2024): 22.1 (#281), gpt-oss-120b: 52.5 (#50)
| Benchmark | Gemini 1.5 Flash (May 2024) | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 16.3% | 88.9% |
| Omni-MATH | 30.4% | 68.8% |
| LMArena Math | 1269 | 1389 |
| MATH Level 5 | 61.9% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
| GSM8K | 82.4% | — |
Knowledge gpt-oss-120b leads
Gemini 1.5 Flash (May 2024): 26.2 (#260), gpt-oss-120b: 42.4 (#96)
| Benchmark | Gemini 1.5 Flash (May 2024) | gpt-oss-120b |
|---|---|---|
| GPQA Diamond | 47.3% | 75.8% |
| MMLU-Pro | 67.8% | 79.5% |
| GPQA (HELM) | 43.7% | 68.4% |
| LMArena Expert | 1233 | 1356 |
| Confabulations | — | 15.7% |
| Vectara Hallucination Rate | — | 14.2% |
| BoolQ | 85.8% | — |
| MMLU | 77.9% | — |
Multimodal Not comparable
Gemini 1.5 Flash (May 2024): 36.0 (#81), gpt-oss-120b: —
| Benchmark | Gemini 1.5 Flash (May 2024) | gpt-oss-120b |
|---|---|---|
| LMArena Vision | 1141 | — |
| Video-MME | 70.3% | — |
| GeoBench | 76% | — |
Multilingual gpt-oss-120b leads
Gemini 1.5 Flash (May 2024): 42.9 (#189), gpt-oss-120b: 48.0 (#147)
| Benchmark | Gemini 1.5 Flash (May 2024) | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1278 | 1351 |
| LMArena Chinese | 1295 | 1385 |
| LMArena French | 1258 | 1369 |
| LMArena German | 1262 | 1353 |
| LMArena Japanese | 1252 | 1331 |
| LMArena Korean | 1221 | 1282 |
| LMArena Russian | 1288 | 1343 |
| LMArena Spanish | 1243 | 1389 |
Instruction Following gpt-oss-120b leads
Gemini 1.5 Flash (May 2024): 66.8 (#205), gpt-oss-120b: 69.3 (#173)
| Benchmark | Gemini 1.5 Flash (May 2024) | gpt-oss-120b |
|---|---|---|
| IFEval | 83.1% | 83.6% |
| LMArena Instruction Following | 1258 | 1318 |
Long Context Gemini 1.5 Flash (May 2024) leads
Gemini 1.5 Flash (May 2024): 39.0 (#187), gpt-oss-120b: 31.4 (#278)
| Benchmark | Gemini 1.5 Flash (May 2024) | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1284 | 1319 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference Gemini 1.5 Flash (May 2024) leads
Gemini 1.5 Flash (May 2024): 48.7 (#196), gpt-oss-120b: 46.5 (#217)
| Benchmark | Gemini 1.5 Flash (May 2024) | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1287 | 1365 |
| LMArena Creative Writing | 1285 | 1275 |
| WildBench | 79.2% | 84.5% |
| LMArena Multi-Turn | 1253 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| EQ-Bench Creative Writing | — | 961 |
Frequently asked questions
Is Gemini 1.5 Flash (May 2024) better than gpt-oss-120b?
gpt-oss-120b is the stronger model overall, scoring 36.3 to 33.2 on the Noometry Index.
Is Gemini 1.5 Flash (May 2024) or gpt-oss-120b better for coding?
They score almost the same on coding (34.4 vs 33.5); test both on your own repository before choosing.
How many benchmarks do Gemini 1.5 Flash (May 2024) and gpt-oss-120b share?
27 benchmarks have published results for both models. Gemini 1.5 Flash (May 2024) has 42 scored results on Noometry and gpt-oss-120b has 48.