Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Gemma 4 E2B
~39
0/8 categoriesGPT-5.2
82
Winner · 4/8 categoriesGemma 4 E2B· GPT-5.2
Pick GPT-5.2 if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if you want the cheaper token bill.
GPT-5.2 is clearly ahead on the aggregate, 82 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2's sharpest advantage is in reasoning, where it averages 82.4 against 19.1. The single biggest benchmark swing on the page is BBH, 21.9% to 96%.
GPT-5.2 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 E2B. That is roughly Infinityx on output cost alone. GPT-5.2 gives you the larger context window at 400K, compared with 128K for Gemma 4 E2B.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | Gemma 4 E2B | GPT-5.2 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 83% |
| BrowseComp | — | 65.8% |
| OSWorld-Verified | — | 47.3% |
| CodingGPT-5.2 wins | ||
| LiveCodeBench | 44% | 79% |
| SWE-bench Verified | — | 80% |
| SWE-bench Pro | — | 55.6% |
| Multimodal & GroundedGPT-5.2 wins | ||
| MMMU-Pro | 44.2% | 79.5% |
| MMMU | — | 86.7% |
| OfficeQA Pro | — | 95% |
| RealWorldQA | — | 83.3% |
| OmniDocBench 1.5 | — | 85.7% |
| Video-MME (with subtitle) | — | 86.0% |
| Video-MME (w/o subtitle) | — | 85.8% |
| MathVision | — | 83.0% |
| We-Math | — | 79.0% |
| DynaMath | — | 86.8% |
| MStar | — | 77.1% |
| SimpleVQA | — | 55.8% |
| ChatCVQA | — | 82.1% |
| CC-OCR | — | 70.3% |
| AI2D_TEST | — | 92.2% |
| CountBench | — | 91.9% |
| ERQA | — | 59.8% |
| VideoMMMU | — | 85.9% |
| MLVU (M-Avg) | — | 85.6% |
| ReasoningGPT-5.2 wins | ||
| BBH | 21.9% | 96% |
| MRCRv2 | 19.1% | 93% |
| MuSR | — | 93% |
| LongBench v2 | — | 91% |
| ARC-AGI-2 | — | 52.9% |
| KnowledgeGPT-5.2 wins | ||
| GPQA | 43.4% | 92.4% |
| MMLU-Pro | 60% | 88% |
| MMLU | — | 99% |
| SuperGPQA | — | 95% |
| HLE | — | 42% |
| FrontierScience | — | 91% |
| SimpleQA | — | 95% |
| Instruction Following | ||
| IFEval | — | 94% |
| Multilingual | ||
| MGSM | — | 95% |
| MMLU-ProX | — | 91% |
| Mathematics | ||
| AIME 2023 | — | 99% |
| AIME 2024 | — | 99% |
| AIME 2025 | — | 98% |
| HMMT Feb 2023 | — | 95% |
| HMMT Feb 2024 | — | 97% |
| HMMT Feb 2025 | — | 96% |
| BRUMO 2025 | — | 96% |
| MATH-500 | — | 98% |
GPT-5.2 is ahead overall, 82 to 39. The biggest single separator in this matchup is BBH, where the scores are 21.9% and 96%.
GPT-5.2 has the edge for knowledge tasks in this comparison, averaging 80.2 versus 54.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for coding in this comparison, averaging 70.2 versus 44. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for reasoning in this comparison, averaging 82.4 versus 19.1. Inside this category, BBH is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for multimodal and grounded tasks in this comparison, averaging 86.5 versus 44.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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