Model comparison
Gemini 2.0 Flash (Feb 2025) vs gpt-oss-20b
Gemini 2.0 Flash (Feb 2025) is the stronger model overall, scoring 35.1 to 32.5 on the Noometry Index.
Last verified . 28 shared benchmarks.
GoogleSummary
- They share 28 benchmarks with published results for both. Gemini 2.0 Flash (Feb 2025) scores higher in 5 categories and gpt-oss-20b in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where Gemini 2.0 Flash (Feb 2025) leads 28.1 to 9.3.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 37.8% for Gemini 2.0 Flash (Feb 2025) and 53.2% for gpt-oss-20b.
- gpt-oss-20b has downloadable open weights; the other is API-only.
Side by side
| Gemini 2.0 Flash (Feb 2025) | gpt-oss-20b | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 35.1 | 32.5 |
| Released | 2024-12-06 | 2025-08-05 |
| Weights | Proprietary | Open |
| Context window | — | 131K |
| Max output | — | 16K |
| Input $ / M tokens | — | $0.018 |
| Output $ / M tokens | — | $0.09 |
| Results tracked | 54 | 34 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding gpt-oss-20b leads
Gemini 2.0 Flash (Feb 2025): 28.4 (#315), gpt-oss-20b: 37.6 (#192)
| Benchmark | Gemini 2.0 Flash (Feb 2025) | gpt-oss-20b |
|---|---|---|
| WeirdML | 25.8% | 40.9% |
| LMArena Coding | 1350 | 1306 |
| SWE-bench Verified (bash only) | 13.5% | — |
| Aider Polyglot | 38.2% | — |
| SciCode | — | 34.4% |
| BigCodeBench Instruct | 45.9% | — |
| LiveBench Coding | 63.4% | — |
| BigCodeBench Complete | 59.9% | — |
| CadEval | 30% | — |
| ALE-Bench | — | 566.05 |
Agentic & Tool Use Gemini 2.0 Flash (Feb 2025) leads
Gemini 2.0 Flash (Feb 2025): 28.1 (#92), gpt-oss-20b: 9.3 (#154)
| Benchmark | Gemini 2.0 Flash (Feb 2025) | gpt-oss-20b |
|---|---|---|
| Terminal-Bench | — | 3.4% |
| TheAgentCompany | 11.4% | — |
Reasoning gpt-oss-20b leads
Gemini 2.0 Flash (Feb 2025): 15.2 (#318), gpt-oss-20b: 19.3 (#261)
| Benchmark | Gemini 2.0 Flash (Feb 2025) | gpt-oss-20b |
|---|---|---|
| Kagi LLM Benchmark | 37.8% | 53.2% |
| LMArena Hard Prompts | 1346 | 1274 |
| DTBench | 63.2% | 68% |
| Epoch Capabilities Index | 135.36 | 137.82 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 31.1% | — |
| CritPt | — | 1.4% |
| Chess Puzzles | — | 4% |
| EnigmaEval | 1.1% | — |
| LiveBench Reasoning | 78.2% | — |
| LiveBench Data Analysis | 69.4% | — |
| LMCA | — | 14.5% |
| LiveBench | 66.9% | — |
Math gpt-oss-20b leads
Gemini 2.0 Flash (Feb 2025): 37.9 (#146), gpt-oss-20b: 39.4 (#103)
| Benchmark | Gemini 2.0 Flash (Feb 2025) | gpt-oss-20b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 57.8% | 65.3% |
| Omni-MATH | 45.9% | 56.5% |
| LMArena Math | 1352 | 1317 |
| LiveBench Math | 75.8% | — |
| MATH Level 5 | 82.2% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge gpt-oss-20b leads
Gemini 2.0 Flash (Feb 2025): 32.0 (#213), gpt-oss-20b: 34.6 (#195)
| Benchmark | Gemini 2.0 Flash (Feb 2025) | gpt-oss-20b |
|---|---|---|
| GPQA Diamond | 64.1% | 60.8% |
| MMLU-Pro | 73.7% | 74% |
| GPQA (HELM) | 55.6% | 59.4% |
| LMArena Expert | 1339 | 1258 |
| Humanity's Last Exam | 6.6% | — |
| Confabulations | 12.4% | — |
| MMLU | 79.7% | — |
Multimodal Not comparable
Gemini 2.0 Flash (Feb 2025): 36.5 (#79), gpt-oss-20b: —
| Benchmark | Gemini 2.0 Flash (Feb 2025) | gpt-oss-20b |
|---|---|---|
| LMArena Vision | 1158 | — |
| GeoBench | 77% | — |
Multilingual Gemini 2.0 Flash (Feb 2025) leads
Gemini 2.0 Flash (Feb 2025): 47.4 (#149), gpt-oss-20b: 42.2 (#197)
| Benchmark | Gemini 2.0 Flash (Feb 2025) | gpt-oss-20b |
|---|---|---|
| LMArena Non-English | 1342 | 1268 |
| LMArena Chinese | 1373 | 1314 |
| LMArena German | 1353 | 1255 |
| LMArena Japanese | 1294 | 1244 |
| LMArena Korean | 1313 | 1236 |
| LMArena Russian | 1351 | 1278 |
| LMArena Spanish | 1363 | 1267 |
| LMArena French | 1391 | — |
Instruction Following Gemini 2.0 Flash (Feb 2025) leads
Gemini 2.0 Flash (Feb 2025): 74.4 (#97), gpt-oss-20b: 61.8 (#240)
| Benchmark | Gemini 2.0 Flash (Feb 2025) | gpt-oss-20b |
|---|---|---|
| IFEval | 84.1% | 73.2% |
| LMArena Instruction Following | 1336 | 1236 |
| LiveBench Instruction Following | 85.8% | — |
Long Context Too close to call
Gemini 2.0 Flash (Feb 2025): 38.1 (#203), gpt-oss-20b: 37.9 (#209)
| Benchmark | Gemini 2.0 Flash (Feb 2025) | gpt-oss-20b |
|---|---|---|
| LMArena Longer Query | 1344 | 1250 |
| Fiction.LiveBench | 61.1% | — |
Writing & Preference Gemini 2.0 Flash (Feb 2025) leads
Gemini 2.0 Flash (Feb 2025): 49.5 (#190), gpt-oss-20b: 35.5 (#265)
| Benchmark | Gemini 2.0 Flash (Feb 2025) | gpt-oss-20b |
|---|---|---|
| LMArena Text | 1354 | 1287 |
| LMArena Creative Writing | 1340 | 1201 |
| EQ-Bench Creative Writing | 1128 | 666 |
| WildBench | 80% | 73.7% |
| LMArena Multi-Turn | 1350 | 1268 |
| Short-Story Creative Writing | 73.8% | — |
| LiveBench Language | 51.3% | — |
Frequently asked questions
Is Gemini 2.0 Flash (Feb 2025) better than gpt-oss-20b?
Gemini 2.0 Flash (Feb 2025) is the stronger model overall, scoring 35.1 to 32.5 on the Noometry Index.
Is Gemini 2.0 Flash (Feb 2025) or gpt-oss-20b better for coding?
gpt-oss-20b scores higher on coding benchmarks: 37.6 versus 28.4 in the Noometry coding category.
How many benchmarks do Gemini 2.0 Flash (Feb 2025) and gpt-oss-20b share?
28 benchmarks have published results for both models. Gemini 2.0 Flash (Feb 2025) has 54 scored results on Noometry and gpt-oss-20b has 34.