Model comparison
Gemma 2 27B vs GPT-4.1 nano
Gemma 2 27B is the stronger model overall, scoring 29.2 to 27.7 on the Noometry Index. GPT-4.1 nano costs 3.7× less per token, which makes it the better buy when Gemma 2 27B's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
GoogleSummary
- They share 20 benchmarks with published results for both. Gemma 2 27B scores higher in 4 categories and GPT-4.1 nano in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-4.1 nano leads 25.5 to 10.0.
- The biggest single-benchmark swing is MATH Level 5: 27.9% for Gemma 2 27B and 70% for GPT-4.1 nano.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.65 / $0.65 for Gemma 2 27B.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 8K.
- Gemma 2 27B has downloadable open weights; the other is API-only.
Side by side
| Gemma 2 27B | GPT-4.1 nano | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 29.2 | 27.7 |
| Released | 2024-06-24 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 8K | 1.05M |
| Max output | 2K | 33K |
| Input $ / M tokens | $0.65 | $0.10 |
| Output $ / M tokens | $0.65 | $0.40 |
| Results tracked | 34 | 38 |
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Category by category
Coding Gemma 2 27B leads
Gemma 2 27B: 33.5 (#243), GPT-4.1 nano: 23.8 (#325)
| Benchmark | Gemma 2 27B | GPT-4.1 nano |
|---|---|---|
| LMArena Coding | 1211 | 1306 |
| Aider Polyglot | — | 8.9% |
| SciCode | — | 25.9% |
| WeirdML | — | 19% |
| BigCodeBench Instruct | 42.8% | — |
| LiveBench Coding | 36% | — |
| BigCodeBench Complete | 52.5% | — |
Agentic & Tool Use Not comparable
Gemma 2 27B: —, GPT-4.1 nano: 26.3 (#98)
| Benchmark | Gemma 2 27B | GPT-4.1 nano |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 33% |
Reasoning Gemma 2 27B leads
Gemma 2 27B: 14.8 (#313), GPT-4.1 nano: 8.6 (#344)
| Benchmark | Gemma 2 27B | GPT-4.1 nano |
|---|---|---|
| LMArena Hard Prompts | 1199 | 1286 |
| DTBench | 48% | 52.5% |
| LMCA | 7.1% | 5.5% |
| Epoch Capabilities Index | 122.06 | 129.62 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 33.3% |
| ARC-AGI-1 | — | 0% |
| CritPt | — | 0% |
| LiveBench Reasoning | 28.1% | — |
| LiveBench Data Analysis | 47.9% | — |
| LiveBench | 38.2% | — |
Math GPT-4.1 nano leads
Gemma 2 27B: 10.0 (#306), GPT-4.1 nano: 25.5 (#246)
| Benchmark | Gemma 2 27B | GPT-4.1 nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.4% | 28.9% |
| LMArena Math | 1212 | 1274 |
| MATH Level 5 | 27.9% | 70% |
| Omni-MATH | — | 36.7% |
| LiveBench Math | 26.5% | — |
| FrontierMath (Feb 2025 set) | — | 1% |
Knowledge GPT-4.1 nano leads
Gemma 2 27B: 18.9 (#275), GPT-4.1 nano: 21.8 (#268)
| Benchmark | Gemma 2 27B | GPT-4.1 nano |
|---|---|---|
| GPQA Diamond | 36.5% | 48.9% |
| LMArena Expert | 1172 | 1272 |
| SimpleQA Verified | — | 6% |
| MMLU-Pro | — | 55% |
| Confabulations | 27.1% | — |
| GPQA (HELM) | — | 50.7% |
| MMLU | 75.7% | — |
Multimodal Not comparable
Gemma 2 27B: —, GPT-4.1 nano: 28.6 (#108)
| Benchmark | Gemma 2 27B | GPT-4.1 nano |
|---|---|---|
| LMArena Vision | — | 1063 |
Multilingual GPT-4.1 nano leads
Gemma 2 27B: 38.8 (#221), GPT-4.1 nano: 41.8 (#200)
| Benchmark | Gemma 2 27B | GPT-4.1 nano |
|---|---|---|
| LMArena Non-English | 1217 | 1260 |
| LMArena Chinese | 1221 | 1270 |
| LMArena German | 1209 | 1288 |
| LMArena Japanese | 1175 | 1199 |
| LMArena Russian | 1234 | 1261 |
| LMArena French | 1246 | — |
| LMArena Korean | 1174 | — |
| LMArena Spanish | 1228 | — |
Instruction Following GPT-4.1 nano leads
Gemma 2 27B: 60.7 (#244), GPT-4.1 nano: 68.0 (#188)
| Benchmark | Gemma 2 27B | GPT-4.1 nano |
|---|---|---|
| LMArena Instruction Following | 1206 | 1267 |
| LiveBench Instruction Following | 58.1% | — |
| IFEval | — | 84.3% |
Long Context Gemma 2 27B leads
Gemma 2 27B: 37.3 (#213), GPT-4.1 nano: 23.7 (#291)
| Benchmark | Gemma 2 27B | GPT-4.1 nano |
|---|---|---|
| LMArena Longer Query | 1231 | 1283 |
| Fiction.LiveBench | — | 25% |
Writing & Preference Gemma 2 27B leads
Gemma 2 27B: 44.4 (#220), GPT-4.1 nano: 40.7 (#238)
| Benchmark | Gemma 2 27B | GPT-4.1 nano |
|---|---|---|
| LMArena Text | 1231 | 1285 |
| LMArena Creative Writing | 1241 | 1260 |
| LMArena Multi-Turn | 1224 | 1277 |
| EQ-Bench Creative Writing | — | 946 |
| WildBench | — | 81.2% |
| LiveBench Language | 32.6% | — |
Frequently asked questions
Is Gemma 2 27B better than GPT-4.1 nano?
Gemma 2 27B is the stronger model overall, scoring 29.2 to 27.7 on the Noometry Index. GPT-4.1 nano costs 3.7× less per token, which makes it the better buy when Gemma 2 27B's lead doesn't matter for your workload.
Which is cheaper, Gemma 2 27B or GPT-4.1 nano?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Gemma 2 27B lists at $0.65 and $0.65.
Is Gemma 2 27B or GPT-4.1 nano better for coding?
Gemma 2 27B scores higher on coding benchmarks: 33.5 versus 23.8 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 8K.
How many benchmarks do Gemma 2 27B and GPT-4.1 nano share?
20 benchmarks have published results for both models. Gemma 2 27B has 34 scored results on Noometry and GPT-4.1 nano has 38.