Gemini 3.8 Flash vs GPT-5.6 Luna
At a Glance
| Compare |  Gemini 3.8 FlashGoogle DeepMind |  GPT-5.6 LunaOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #8 of 4682.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 54.7–88.0 | #31 of 4652.7 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | #26 of 44$0.160 per LiveBench case | #6 of 44$0.026 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #8 of 3860.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 46.7–63.4 | #4 of 3864.8 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.75Google AI ↗ · Sep 2, 2026 | $0.20Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $3.75Google AI ↗ · Sep 2, 2026 | $1.20Openai ↗ · Sep 3, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,050K |
| Model facts checked | Sep 2, 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
| Benchmark | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader | 4.71100% of row best · score · Gemini 3.8 Flash (High); 95% CI [2.80246501, 6.61533867]; sessions 12534; observations 712246; rank 13 | -0.4495% of row best · score · GPT 5.6 Luna (xHigh); 95% CI [-1.27310649, 0.39719069]; sessions 29186; observations 2307801; rank 27 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,494.67100% of row best · rating · gemini-3.8-flash-high; 95% CI [1486.13928353, 1503.20869643]; votes 5076; rank 6 | 1,429.8996% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1425.11549148, 1434.66063238]; votes 28547; rank 86 |
| LiveBench2026-06-25 · overall · leader | 80.85100% of row best · percent · gemini-3.8-flash-high · 42,786 output tokens / case | 77.0595% of row best · percent · gpt-5.6-luna-max · 21,799 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 77.8292% of row best · points · Gemini 3.8 Flash (high thinking) · 13,616 output tokens / case | 84.47100% of row best · points · GPT-5.6 Luna (ultra) · 17,605 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 3 benchmark winsOverall lead | 0 benchmark wins |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Quality Versus Estimated Output Cost
Side-by-Side Facts
| Field | Gemini 3.8 Flash | GPT-5.6 Luna |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Family | Gemini 3 | Gpt 5 6 |
| Model | Gemini 3.8 Flash | GPT-5.6 Luna |
| Version | Gemini 3.8 Flash | GPT-5.6 Luna |
| Lifecycle | active | active |
| Released | 2026-09-02 | Unknown |
| Knowledge cutoff | Unknown | 2026-02-16 |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,050K |
| 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), Google Gemini (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, code_execution, computer_use, generation, reasoning, structured_outputs, tools | chat, generation, reasoning, tools |
Gemini 3.8 Flash Capabilities
GPT-5.6 Luna Capabilities
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|---|---|---|---|
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Seed 2.1 ProByteDance Seed | vs | Gemini 3.8 FlashGoogle DeepMind | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Gemini 3.8 Flash vs GPT-5.6 Luna FAQs
Is Gemini 3.8 Flash or GPT-5.6 Luna better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.8 Flash 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 3.8 Flash or GPT-5.6 Luna?+
Gemini 3.8 Flash is $0.75 and GPT-5.6 Luna is $0.20 per million tokens, so GPT-5.6 Luna is cheaper on this metric. Gemini 3.8 Flash is $3.75 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 3.8 Flash or GPT-5.6 Luna?+
GPT-5.6 Luna has the larger sourced context window. Gemini 3.8 Flash supports 1,049K and GPT-5.6 Luna supports 1,050K.
Which performs better in benchmarks, Gemini 3.8 Flash or GPT-5.6 Luna?+
Gemini 3.8 Flash leads the current overall benchmark count. The result uses 3 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can Gemini 3.8 Flash or GPT-5.6 Luna be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Gemini 3.8 Flash is not marked open weight; GPT-5.6 Luna is not marked open weight.
Can Gemini 3.8 Flash and GPT-5.6 Luna understand images?+
Gemini 3.8 Flash 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 3.8 Flash or GPT-5.6 Luna?+
GPT-5.6 Luna has the larger sourced maximum output: Gemini 3.8 Flash supports 66K and GPT-5.6 Luna supports 128K output tokens.
Do Gemini 3.8 Flash and GPT-5.6 Luna support reasoning and tool use?+
Gemini 3.8 Flash: 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 3.8 Flash or GPT-5.6 Luna?+
Gemini 3.8 Flash has 2 sourced provider routes; GPT-5.6 Luna has 2, a tie.
Which offers better value, Gemini 3.8 Flash 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.