Model comparison
Gemma 3n E4b IT vs GPT-5 Mini
GPT-5 Mini is the stronger model overall, scoring 41.7 to 36.7 on the Noometry Index. Gemma 3n E4b IT costs 9.2× less per token, which makes it the better buy when GPT-5 Mini's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
GoogleSummary
- They share 18 benchmarks with published results for both. Gemma 3n E4b IT scores higher in 0 categories and GPT-5 Mini in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5 Mini leads 46.3 to 32.5.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 31.5% for Gemma 3n E4b IT and 70.3% for GPT-5 Mini.
- Gemma 3n E4b IT is cheaper at $0.06 / $0.12 per million input/output tokens, against $0.25 / $2 for GPT-5 Mini.
- GPT-5 Mini accepts more context: 400K tokens versus 33K.
- Gemma 3n E4b IT has downloadable open weights; the other is API-only.
Side by side
| Gemma 3n E4b IT | GPT-5 Mini | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 36.7 | 41.7 |
| Released | 2025-05-20 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 33K | 400K |
| Max output | 33K | 128K |
| Input $ / M tokens | $0.06 | $0.25 |
| Output $ / M tokens | $0.12 | $2 |
| Results tracked | 18 | 60 |
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Category by category
Coding GPT-5 Mini leads
Gemma 3n E4b IT: 36.4 (#193), GPT-5 Mini: 40.0 (#134)
| Benchmark | Gemma 3n E4b IT | GPT-5 Mini |
|---|---|---|
| LMArena Coding | 1269 | 1406 |
| SWE-bench Verified | — | 64.7% |
| SWE-bench Verified (bash only) | — | 59.8% |
| SWE-bench Multilingual | — | 39.7% |
| SciCode | — | 39.2% |
| WeirdML | — | 52.7% |
| ALE-Bench | — | 799.77 |
| AlgoTune | — | 1.38 |
Agentic & Tool Use Not comparable
Gemma 3n E4b IT: —, GPT-5 Mini: 31.0 (#64)
| Benchmark | Gemma 3n E4b IT | GPT-5 Mini |
|---|---|---|
| Terminal-Bench | — | 34.8% |
| Berkeley Function Calling Leaderboard | — | 55.5% |
| Vending-Bench 2 | — | -31.18 |
Reasoning GPT-5 Mini leads
Gemma 3n E4b IT: 19.2 (#239), GPT-5 Mini: 24.1 (#153)
| Benchmark | Gemma 3n E4b IT | GPT-5 Mini |
|---|---|---|
| Kagi LLM Benchmark | 31.5% | 70.3% |
| LMArena Hard Prompts | 1284 | 1380 |
| ARC-AGI-2 | — | 4.4% |
| ARC-AGI-1 | — | 54.3% |
| CritPt | — | 0% |
| Chess Puzzles | — | 30% |
| EnigmaEval | — | 8.2% |
| Mystery Game Puzzles | — | 10% |
| DTBench | — | 80.5% |
| LMCA | — | 34.2% |
| Epoch Capabilities Index | — | 145.51 |
| ForecastBench | — | 61 |
Math GPT-5 Mini leads
Gemma 3n E4b IT: 32.5 (#186), GPT-5 Mini: 46.3 (#59)
| Benchmark | Gemma 3n E4b IT | GPT-5 Mini |
|---|---|---|
| LMArena Math | 1251 | 1377 |
| FrontierMath (Tiers 1-3) | — | 46.7% |
| FrontierMath Tier 4 | — | 12.2% |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| ProofBench | — | 9% |
| Omni-MATH | — | 72.2% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 27.2% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge GPT-5 Mini leads
Gemma 3n E4b IT: 33.6 (#193), GPT-5 Mini: 45.5 (#80)
| Benchmark | Gemma 3n E4b IT | GPT-5 Mini |
|---|---|---|
| LMArena Expert | 1245 | 1377 |
| GPQA Diamond | — | 75% |
| Humanity's Last Exam | — | 19.4% |
| SimpleQA Verified | — | 21.6% |
| MMLU-Pro | — | 83.5% |
| Confabulations | — | 13.3% |
| Vectara Hallucination Rate | — | 12.9% |
| GPQA (HELM) | — | 75.6% |
Multimodal Not comparable
Gemma 3n E4b IT: —, GPT-5 Mini: 35.0 (#80)
| Benchmark | Gemma 3n E4b IT | GPT-5 Mini |
|---|---|---|
| LMArena Vision | — | 1202 |
| VPCT | — | 40.2% |
Multilingual GPT-5 Mini leads
Gemma 3n E4b IT: 43.5 (#179), GPT-5 Mini: 49.1 (#131)
| Benchmark | Gemma 3n E4b IT | GPT-5 Mini |
|---|---|---|
| LMArena Non-English | 1285 | 1363 |
| LMArena Chinese | 1310 | 1387 |
| LMArena French | 1330 | 1385 |
| LMArena German | 1312 | 1366 |
| LMArena Japanese | 1274 | 1342 |
| LMArena Korean | 1259 | 1306 |
| LMArena Russian | 1288 | 1362 |
| LMArena Spanish | 1305 | 1355 |
Instruction Following GPT-5 Mini leads
Gemma 3n E4b IT: 66.3 (#205), GPT-5 Mini: 76.4 (#43)
| Benchmark | Gemma 3n E4b IT | GPT-5 Mini |
|---|---|---|
| LMArena Instruction Following | 1255 | 1357 |
| IFEval | — | 92.7% |
Long Context GPT-5 Mini leads
Gemma 3n E4b IT: 38.8 (#185), GPT-5 Mini: 41.9 (#127)
| Benchmark | Gemma 3n E4b IT | GPT-5 Mini |
|---|---|---|
| LMArena Longer Query | 1277 | 1355 |
| Fiction.LiveBench | — | 69.4% |
Writing & Preference GPT-5 Mini leads
Gemma 3n E4b IT: 50.3 (#181), GPT-5 Mini: 55.4 (#143)
| Benchmark | Gemma 3n E4b IT | GPT-5 Mini |
|---|---|---|
| LMArena Text | 1306 | 1373 |
| LMArena Creative Writing | 1287 | 1325 |
| LMArena Multi-Turn | 1276 | 1363 |
| Short-Story Creative Writing | — | 83.1% |
| EQ-Bench Creative Writing | — | 1313 |
| WildBench | — | 85.5% |
Frequently asked questions
Is Gemma 3n E4b IT better than GPT-5 Mini?
GPT-5 Mini is the stronger model overall, scoring 41.7 to 36.7 on the Noometry Index. Gemma 3n E4b IT costs 9.2× less per token, which makes it the better buy when GPT-5 Mini's lead doesn't matter for your workload.
Which is cheaper, Gemma 3n E4b IT or GPT-5 Mini?
Gemma 3n E4b IT is cheaper. It lists at $0.06 per million input tokens and $0.12 per million output tokens; GPT-5 Mini lists at $0.25 and $2.
Is Gemma 3n E4b IT or GPT-5 Mini better for coding?
GPT-5 Mini scores higher on coding benchmarks: 40.0 versus 36.4 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Mini does, with 400K tokens against 33K.
How many benchmarks do Gemma 3n E4b IT and GPT-5 Mini share?
18 benchmarks have published results for both models. Gemma 3n E4b IT has 18 scored results on Noometry and GPT-5 Mini has 60.