Model comparison
Gemini 3.7 Flash vs GPT-4.1 nano
Gemini 3.7 Flash is the stronger model overall, scoring 59.8 to 27.7 on the Noometry Index. GPT-4.1 nano costs 8.6× less per token, which makes it the better buy when Gemini 3.7 Flash's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
GoogleSummary
- They share 26 benchmarks with published results for both. Gemini 3.7 Flash scores higher in 10 categories and GPT-4.1 nano in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Gemini 3.7 Flash leads 70.1 to 8.6.
- The biggest single-benchmark swing is ARC-AGI-1: 95.5% for Gemini 3.7 Flash and 0% for GPT-4.1 nano.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.75 / $3.75 for Gemini 3.7 Flash.
- Gemini 3.7 Flash accepts more context: 1.05M tokens versus 1.05M.
Side by side
| Gemini 3.7 Flash | GPT-4.1 nano | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 59.8 | 27.7 |
| Released | 2026-08-13 | 2025-04-14 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 33K |
| Input $ / M tokens | $0.75 | $0.10 |
| Output $ / M tokens | $3.75 | $0.40 |
| Results tracked | 44 | 38 |
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Category by category
Coding Gemini 3.7 Flash leads
Gemini 3.7 Flash: 55.7 (#19), GPT-4.1 nano: 23.8 (#325)
| Benchmark | Gemini 3.7 Flash | GPT-4.1 nano |
|---|---|---|
| SciCode | 59.8% | 25.9% |
| LMArena Coding | 1499 | 1306 |
| DeepSWE | 65.5% | — |
| FrontierCode | 43.6% | — |
| Aider Polyglot | — | 8.9% |
| LMArena WebDev | 1593 | — |
| FrontierSWE | 20.3% | — |
| WeirdML | — | 19% |
| ALE-Bench | 904.3 | — |
Agentic & Tool Use Gemini 3.7 Flash leads
Gemini 3.7 Flash: 42.2 (#18), GPT-4.1 nano: 26.3 (#98)
| Benchmark | Gemini 3.7 Flash | GPT-4.1 nano |
|---|---|---|
| APEX-Agents | 67.8% | — |
| Berkeley Function Calling Leaderboard | — | 33% |
| Remote Labor Index | 5% | — |
| GDP.pdf | 23.8% | — |
Reasoning Gemini 3.7 Flash leads
Gemini 3.7 Flash: 70.1 (#12), GPT-4.1 nano: 8.6 (#344)
| Benchmark | Gemini 3.7 Flash | GPT-4.1 nano |
|---|---|---|
| ARC-AGI-2 | 84.6% | 0% |
| ARC-AGI-1 | 95.5% | 0% |
| CritPt | 14.3% | 0% |
| LMArena Hard Prompts | 1497 | 1286 |
| DTBench | 96.8% | 52.5% |
| LMCA | 50.4% | 5.5% |
| Epoch Capabilities Index | 157.72 | 129.62 |
| Kagi LLM Benchmark | — | 33.3% |
| NYT Connections (extended) | 94% | — |
| Chess Puzzles | 47% | — |
| Mystery Game Puzzles | 37% | — |
Math Gemini 3.7 Flash leads
Gemini 3.7 Flash: 69.5 (#18), GPT-4.1 nano: 25.5 (#246)
| Benchmark | Gemini 3.7 Flash | GPT-4.1 nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 97.2% | 28.9% |
| LMArena Math | 1511 | 1274 |
| FrontierMath (Tiers 1-3) | 71.6% | — |
| FrontierMath Tier 4 | 36.6% | — |
| ProofBench | 58% | — |
| Omni-MATH | — | 36.7% |
| MATH Level 5 | — | 70% |
| FrontierMath (Feb 2025 set) | — | 1% |
Knowledge Gemini 3.7 Flash leads
Gemini 3.7 Flash: 69.8 (#4), GPT-4.1 nano: 21.8 (#268)
| Benchmark | Gemini 3.7 Flash | GPT-4.1 nano |
|---|---|---|
| GPQA Diamond | 94.8% | 48.9% |
| SimpleQA Verified | 69.2% | 6% |
| LMArena Expert | 1508 | 1272 |
| MMLU-Pro | — | 55% |
| GPQA (HELM) | — | 50.7% |
Multimodal Gemini 3.7 Flash leads
Gemini 3.7 Flash: 36.9 (#69), GPT-4.1 nano: 28.6 (#108)
| Benchmark | Gemini 3.7 Flash | GPT-4.1 nano |
|---|---|---|
| LMArena Vision | 1314 | 1063 |
| Furniture Assembly | 26.7% | — |
Multilingual Gemini 3.7 Flash leads
Gemini 3.7 Flash: 57.8 (#7), GPT-4.1 nano: 41.8 (#200)
| Benchmark | Gemini 3.7 Flash | GPT-4.1 nano |
|---|---|---|
| LMArena Non-English | 1485 | 1260 |
| LMArena Chinese | 1553 | 1270 |
| LMArena German | 1485 | 1288 |
| LMArena Japanese | 1506 | 1199 |
| LMArena Russian | 1519 | 1261 |
| LMArena French | 1512 | — |
| LMArena Korean | 1486 | — |
| LMArena Spanish | 1507 | — |
Instruction Following Gemini 3.7 Flash leads
Gemini 3.7 Flash: 78.0 (#12), GPT-4.1 nano: 68.0 (#188)
| Benchmark | Gemini 3.7 Flash | GPT-4.1 nano |
|---|---|---|
| LMArena Instruction Following | 1486 | 1267 |
| IFEval | — | 84.3% |
Long Context Gemini 3.7 Flash leads
Gemini 3.7 Flash: 45.8 (#28), GPT-4.1 nano: 23.7 (#291)
| Benchmark | Gemini 3.7 Flash | GPT-4.1 nano |
|---|---|---|
| LMArena Longer Query | 1493 | 1283 |
| Fiction.LiveBench | — | 25% |
Writing & Preference Gemini 3.7 Flash leads
Gemini 3.7 Flash: 71.4 (#19), GPT-4.1 nano: 40.7 (#238)
| Benchmark | Gemini 3.7 Flash | GPT-4.1 nano |
|---|---|---|
| LMArena Text | 1487 | 1285 |
| LMArena Creative Writing | 1490 | 1260 |
| EQ-Bench Creative Writing | 1723 | 946 |
| LMArena Multi-Turn | 1495 | 1277 |
| WildBench | — | 81.2% |
Frequently asked questions
Is Gemini 3.7 Flash better than GPT-4.1 nano?
Gemini 3.7 Flash is the stronger model overall, scoring 59.8 to 27.7 on the Noometry Index. GPT-4.1 nano costs 8.6× less per token, which makes it the better buy when Gemini 3.7 Flash's lead doesn't matter for your workload.
Which is cheaper, Gemini 3.7 Flash 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; Gemini 3.7 Flash lists at $0.75 and $3.75.
Is Gemini 3.7 Flash or GPT-4.1 nano better for coding?
Gemini 3.7 Flash scores higher on coding benchmarks: 55.7 versus 23.8 in the Noometry coding category.
Which has the bigger context window?
Gemini 3.7 Flash does, with 1.05M tokens against 1.05M.
How many benchmarks do Gemini 3.7 Flash and GPT-4.1 nano share?
26 benchmarks have published results for both models. Gemini 3.7 Flash has 44 scored results on Noometry and GPT-4.1 nano has 38.