Gemini 3.1 Flash-Lite vs GPT-5.6 Luna
Benchmark Performance
Available Benchmarks
| Benchmark | Gemini 3.1 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| ToneBench2026-08-14-10-task · overall_score · leader | 69.5083% of row best · points · Gemini 3.1 Flash-Lite · 1,571 output tokens / case | 83.59100% of row best · points · GPT-5.6 Luna (xhigh) · 6,093 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 1 benchmark winOverall lead |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Technical Differences
Side-by-Side Facts
| Field | Gemini 3.1 Flash-Lite | GPT-5.6 Luna |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Family | Gemini 3 | Gpt 5 6 |
| Model | Gemini 3.1 Flash-Lite | GPT-5.6 Luna |
| Version | Gemini 3.1 Flash-Lite | GPT-5.6 Luna |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | 2026-02-16 |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,048,576 | 1,050,000 |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Google AI (Standard) | OpenAI (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
13 comparable fields · 7 material differences · Pair passes the primary-source comparison gate
Gemini 3.1 Flash-Lite Capabilities
GPT-5.6 Luna Capabilities
Internal Comparison Graph
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Primary Evidence
Sources and Freshness
Questions
Gemini 3.1 Flash-Lite vs GPT-5.6 Luna FAQs
Is Gemini 3.1 Flash-Lite or GPT-5.6 Luna better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.1 Flash-Lite and GPT-5.6 Luna, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Gemini 3.1 Flash-Lite or GPT-5.6 Luna?+
Gemini 3.1 Flash-Lite is $0.25 and GPT-5.6 Luna is $0.20 per million tokens, so GPT-5.6 Luna is cheaper on this metric. Gemini 3.1 Flash-Lite is $1.50 and GPT-5.6 Luna is $1.20 per million tokens, so GPT-5.6 Luna is cheaper on this metric.
Which has a larger context window, Gemini 3.1 Flash-Lite or GPT-5.6 Luna?+
GPT-5.6 Luna has the larger sourced context window. Gemini 3.1 Flash-Lite supports 1,048,576 and GPT-5.6 Luna supports 1,050,000.
Which performs better in benchmarks, Gemini 3.1 Flash-Lite 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 Gemini 3.1 Flash-Lite or GPT-5.6 Luna be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Gemini 3.1 Flash-Lite is not marked open weight; GPT-5.6 Luna is not marked open weight.
Can Gemini 3.1 Flash-Lite and GPT-5.6 Luna understand images?+
Gemini 3.1 Flash-Lite 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, Gemini 3.1 Flash-Lite or GPT-5.6 Luna?+
GPT-5.6 Luna has the larger sourced maximum output: Gemini 3.1 Flash-Lite supports 65,536 and GPT-5.6 Luna supports 128,000 output tokens.
Do Gemini 3.1 Flash-Lite and GPT-5.6 Luna support reasoning and tool use?+
Gemini 3.1 Flash-Lite: 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, Gemini 3.1 Flash-Lite or GPT-5.6 Luna?+
Gemini 3.1 Flash-Lite has 1 sourced provider route; GPT-5.6 Luna has 2, so GPT-5.6 Luna has broader tracked availability.
Which offers better value, Gemini 3.1 Flash-Lite 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.