Gemma 4 E2B vs GPT-5.6 Luna
At a Glance
| Compare |  Gemma 4 E2BGoogle DeepMind |  GPT-5.6 LunaOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #31 of 4652.7 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #6 of 44$0.026 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #4 of 3864.8 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.20Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $1.20Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 131K | 1,050K |
| Model facts checked | Sep 3, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Gemma 4 E2B | GPT-5.6 Luna |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Family | Gemma 4 | Gpt 5 6 |
| Model | Gemma 4 E2B | GPT-5.6 Luna |
| Version | Gemma 4 E2B | GPT-5.6 Luna |
| Lifecycle | active | active |
| Released | 2026-03-02 | Unknown |
| Knowledge cutoff | Unknown | 2026-02-16 |
| Input modalities | Text, Image, Video, Audio | Text, Image |
| Output modalities | Text | Text |
| Context window | 131K | 1,050K |
| 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, tools |
Gemma 4 E2B Capabilities
GPT-5.6 Luna Capabilities
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Primary Evidence
Sources and Freshness
Questions
Gemma 4 E2B vs GPT-5.6 Luna FAQs
Is Gemma 4 E2B or GPT-5.6 Luna better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemma 4 E2B and GPT-5.6 Luna, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemma 4 E2B or GPT-5.6 Luna?+
Only GPT-5.6 Luna has a directly sourced input price: $0.20 per million tokens. Only GPT-5.6 Luna has a directly sourced output price: $1.20 per million tokens.
Which has a larger context window, Gemma 4 E2B or GPT-5.6 Luna?+
GPT-5.6 Luna has the larger sourced context window. Gemma 4 E2B supports 131K and GPT-5.6 Luna supports 1,050K.
Which performs better in benchmarks, Gemma 4 E2B or GPT-5.6 Luna?+
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-5.6 Luna 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-5.6 Luna is not marked open weight.
Can Gemma 4 E2B and GPT-5.6 Luna understand images?+
Gemma 4 E2B is documented with image input; GPT-5.6 Luna is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Gemma 4 E2B or GPT-5.6 Luna?+
Neither has a larger sourced maximum output. Gemma 4 E2B is — and GPT-5.6 Luna is 128K.
Do Gemma 4 E2B and GPT-5.6 Luna support reasoning and tool use?+
Gemma 4 E2B: reasoning, tool calling, and image input. GPT-5.6 Luna: 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-5.6 Luna?+
Gemma 4 E2B has 1 sourced provider route; GPT-5.6 Luna has 2, so GPT-5.6 Luna has broader tracked availability.
Which offers better value, Gemma 4 E2B or GPT-5.6 Luna?+
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.