Gemma 4 E2B vs GPT-5 Nano
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | Gemma 4 E2B | GPT-5 Nano |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Family | Gemma 4 | Gpt 5 |
| Model | Gemma 4 E2B | GPT-5 Nano |
| Version | Gemma 4 E2B | GPT-5 Nano |
| Lifecycle | active | active |
| Released | 2026-03-02 | 2025-08-07 |
| Knowledge cutoff | Unknown | 2024-05-31 |
| Input modalities | Text, Image, Video, Audio | Text, Image |
| Output modalities | Text | Text |
| Context window | 131,072 | 400,000 |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Together Ai (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, reasoning, structured_outputs, tools |
14 comparable fields · 10 material differences · Pair passes the primary-source comparison gate
Gemma 4 E2B Capabilities
GPT-5 Nano Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
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Gemma 4 E2BGoogle DeepMind | vs |  Muse Spark 1.2Meta | cross-developer peersaudio, image, text, video |
Gemma 4 E2BGoogle DeepMind | vs |  Qwen3.7-PlusQwen | cross-developer peersimage, text, video |
GPT-5 NanoOpenAI | vs | GPT-5.4 NanoOpenAI | family variantsimage, text |
Gemma 4 26B-A4BGoogle DeepMind | vs | Gemma 4 E2BGoogle DeepMind | family variantsimage, text |
Gemma 4 31BGoogle DeepMind | vs | Gemma 4 E2BGoogle DeepMind | family variantsimage, text |
GPT-5 NanoOpenAI | vs | GPT-5.3-CodexOpenAI | family variantsimage, text |
GPT-5 NanoOpenAI | vs | Grok 4.5xAI | cross-developer peersimage, text |
Gemma 4 E2BGoogle DeepMind | vs | o3-proOpenAI | cross-developer peersimage, text |
GPT-5 NanoOpenAI | vs | Qwen3 VL PlusQwen | cross-developer peersimage, text |
Claude Sonnet 4.5Anthropic | vs | GPT-5 NanoOpenAI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Gemma 4 E2B vs GPT-5 Nano FAQs
Is Gemma 4 E2B or GPT-5 Nano better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemma 4 E2B and GPT-5 Nano, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemma 4 E2B or GPT-5 Nano?+
Only GPT-5 Nano has a directly sourced input price: $0.025 per million tokens. Only GPT-5 Nano has a directly sourced output price: $0.20 per million tokens.
Which has a larger context window, Gemma 4 E2B or GPT-5 Nano?+
GPT-5 Nano has the larger sourced context window. Gemma 4 E2B supports 131,072 and GPT-5 Nano supports 400,000.
Which performs better in benchmarks, Gemma 4 E2B or GPT-5 Nano?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Gemma 4 E2B or GPT-5 Nano be self-hosted?+
Gemma 4 E2B is the only model in this pair currently marked as self-hostable. Gemma 4 E2B is open weight; GPT-5 Nano is not marked open weight.
Can Gemma 4 E2B and GPT-5 Nano understand images?+
Gemma 4 E2B is documented with image input; GPT-5 Nano is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemma 4 E2B or GPT-5 Nano?+
Neither has a larger sourced maximum output. Gemma 4 E2B is — and GPT-5 Nano is 128,000.
Do Gemma 4 E2B and GPT-5 Nano support reasoning and tool use?+
Gemma 4 E2B: reasoning, tool calling, and image input. GPT-5 Nano: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Gemma 4 E2B or GPT-5 Nano?+
Gemma 4 E2B has 1 sourced provider route; GPT-5 Nano has 2, so GPT-5 Nano has broader tracked availability.
Which offers better value, Gemma 4 E2B or GPT-5 Nano?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.