Model comparison
Gemini 2.5 Flash-Lite vs gpt-oss-120b
Gemini 2.5 Flash-Lite and gpt-oss-120b score almost the same on the Noometry Index (37.0 vs 36.3), so choose on price, context window or the category you care about most.
Last verified . 30 shared benchmarks.
GoogleSummary
- They share 30 benchmarks with published results for both. Gemini 2.5 Flash-Lite scores higher in 7 categories and gpt-oss-120b in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where Gemini 2.5 Flash-Lite leads 28.0 to 12.2.
- The biggest single-benchmark swing is GPQA (HELM): 30.9% for Gemini 2.5 Flash-Lite and 68.4% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.10 / $0.40 for Gemini 2.5 Flash-Lite.
- Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.5 Flash-Lite | gpt-oss-120b | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 37.0 | 36.3 |
| Released | 2025-06-17 | 2025-08-05 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 66K | 41K |
| Input $ / M tokens | $0.10 | $0.037 |
| Output $ / M tokens | $0.40 | $0.17 |
| Results tracked | 33 | 48 |
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Category by category
Coding Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 38.5 (#173), gpt-oss-120b: 33.5 (#256)
| Benchmark | Gemini 2.5 Flash-Lite | gpt-oss-120b |
|---|---|---|
| WeirdML | 35.2% | 48.2% |
| LMArena Coding | 1373 | 1380 |
| ALE-Bench | 325.9 | 575.62 |
| SWE-bench Verified (bash only) | — | 26% |
| Aider Polyglot | — | 41.8% |
| SciCode | — | 36% |
| AlgoTune | — | 1.41 |
Agentic & Tool Use Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 28.0 (#96), gpt-oss-120b: 12.2 (#153)
| Benchmark | Gemini 2.5 Flash-Lite | gpt-oss-120b |
|---|---|---|
| Terminal-Bench | — | 18.7% |
| APEX-Agents | — | 4.4% |
| Berkeley Function Calling Leaderboard | 36.9% | — |
| METR Time Horizons | — | 56.6% |
| Vending-Bench 2 | — | -21.53 |
Reasoning Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 22.2 (#205), gpt-oss-120b: 20.0 (#245)
| Benchmark | Gemini 2.5 Flash-Lite | gpt-oss-120b |
|---|---|---|
| Kagi LLM Benchmark | 40.5% | 58.6% |
| LMArena Hard Prompts | 1377 | 1364 |
| DTBench | 62.8% | 76.3% |
| LMCA | 18.1% | 22.1% |
| Epoch Capabilities Index | 133.94 | 139.93 |
| SimpleBench | — | 22.1% |
| CritPt | — | 1.1% |
| Chess Puzzles | — | 20% |
| Mystery Game Puzzles | — | 2% |
| Surface Evolver Bench | — | 25% |
Math gpt-oss-120b leads
Gemini 2.5 Flash-Lite: 38.0 (#144), gpt-oss-120b: 52.5 (#50)
| Benchmark | Gemini 2.5 Flash-Lite | gpt-oss-120b |
|---|---|---|
| Omni-MATH | 48% | 68.8% |
| LMArena Math | 1373 | 1389 |
| OTIS Mock AIME 2024-2025 | — | 88.9% |
Knowledge gpt-oss-120b leads
Gemini 2.5 Flash-Lite: 32.5 (#210), gpt-oss-120b: 42.4 (#96)
| Benchmark | Gemini 2.5 Flash-Lite | gpt-oss-120b |
|---|---|---|
| MMLU-Pro | 53.7% | 79.5% |
| Vectara Hallucination Rate | 3.3% | 14.2% |
| GPQA (HELM) | 30.9% | 68.4% |
| LMArena Expert | 1373 | 1356 |
| GPQA Diamond | — | 75.8% |
| Confabulations | — | 15.7% |
Multimodal Not comparable
Gemini 2.5 Flash-Lite: 29.1 (#114), gpt-oss-120b: —
| Benchmark | Gemini 2.5 Flash-Lite | gpt-oss-120b |
|---|---|---|
| LMArena Vision | 1198 | — |
| VPCT | 30% | — |
Multilingual Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 49.3 (#134), gpt-oss-120b: 48.0 (#147)
| Benchmark | Gemini 2.5 Flash-Lite | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1369 | 1351 |
| LMArena Chinese | 1404 | 1385 |
| LMArena French | 1388 | 1369 |
| LMArena German | 1389 | 1353 |
| LMArena Japanese | 1359 | 1331 |
| LMArena Korean | 1360 | 1282 |
| LMArena Russian | 1373 | 1343 |
| LMArena Spanish | 1396 | 1389 |
Instruction Following Too close to call
Gemini 2.5 Flash-Lite: 70.0 (#168), gpt-oss-120b: 69.3 (#173)
| Benchmark | Gemini 2.5 Flash-Lite | gpt-oss-120b |
|---|---|---|
| IFEval | 81% | 83.6% |
| LMArena Instruction Following | 1367 | 1318 |
Long Context Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 33.3 (#262), gpt-oss-120b: 31.4 (#278)
| Benchmark | Gemini 2.5 Flash-Lite | gpt-oss-120b |
|---|---|---|
| Fiction.LiveBench | 47.2% | 44.4% |
| LMArena Longer Query | 1373 | 1319 |
Writing & Preference Gemini 2.5 Flash-Lite leads
Gemini 2.5 Flash-Lite: 56.8 (#135), gpt-oss-120b: 46.5 (#217)
| Benchmark | Gemini 2.5 Flash-Lite | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1379 | 1365 |
| LMArena Creative Writing | 1367 | 1275 |
| WildBench | 81.8% | 84.5% |
| LMArena Multi-Turn | 1366 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| EQ-Bench Creative Writing | — | 961 |
Frequently asked questions
Is Gemini 2.5 Flash-Lite better than gpt-oss-120b?
Gemini 2.5 Flash-Lite and gpt-oss-120b score almost the same on the Noometry Index (37.0 vs 36.3), so choose on price, context window or the category you care about most.
Which is cheaper, Gemini 2.5 Flash-Lite or gpt-oss-120b?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Gemini 2.5 Flash-Lite lists at $0.10 and $0.40.
Is Gemini 2.5 Flash-Lite or gpt-oss-120b better for coding?
Gemini 2.5 Flash-Lite scores higher on coding benchmarks: 38.5 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
Gemini 2.5 Flash-Lite does, with 1.05M tokens against 131K.
How many benchmarks do Gemini 2.5 Flash-Lite and gpt-oss-120b share?
30 benchmarks have published results for both models. Gemini 2.5 Flash-Lite has 33 scored results on Noometry and gpt-oss-120b has 48.