DeepSeek V4 Flash Vision Exp vs GPT-5.6 Luna
At a Glance
| Compare |  DeepSeek V4 Flash Vision ExpDeepSeek |  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 | $0.22DeepSeek ↗ · Sep 2, 2026 | $0.20Openai ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $0.66DeepSeek ↗ · 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 | DeepSeek-V4-Flash-Vision-Exp | GPT-5.6 Luna |
|---|---|---|
| LiveBench2026-06-25 · overall · leader | 79.67100% of row best · percent · deepseek-v4-flash-vision-exp · 52,644 output tokens / case | 77.0597% of row best · percent · gpt-5.6-luna-max · 21,799 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 1 benchmark winNo overall winner | 0 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | DeepSeek-V4-Flash-Vision-Exp | GPT-5.6 Luna |
|---|---|---|
| Developer | DeepSeek | OpenAI |
| Family | Deepseek V4 Flash Vision Exp | Gpt 5 6 |
| Model | DeepSeek-V4-Flash-Vision-Exp | GPT-5.6 Luna |
| Version | DeepSeek-V4-Flash-Vision-Exp | GPT-5.6 Luna |
| Lifecycle | retired | active |
| Released | 2026-08-21 | Unknown |
| Knowledge cutoff | Unknown | 2026-02-16 |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,050K |
| Total parameters | 304.6B | Unknown |
| Active parameters | Unknown | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Fireworks Ai (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, reasoning, tools |
DeepSeek V4 Flash Vision Exp Capabilities
GPT-5.6 Luna Capabilities
Related Comparisons
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|---|---|---|---|
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DeepSeek-V4-Flash-Vision-ExpDeepSeek | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
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DeepSeek-V4-Flash-Vision-ExpDeepSeek | vs | Grok 4.6xAI | cross-developer peersimage, text |
DeepSeek-V4-Flash-Vision-ExpDeepSeek | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
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 MiniMax-M3MiniMax | vs | DeepSeek-V4-Flash-Vision-ExpDeepSeek | cross-developer peersimage, text |
DeepSeek-V4-Flash-Vision-ExpDeepSeek | vs |  GLM-5.3Z.ai | cross-developer peerstext |
DeepSeek-V4-Flash-Vision-ExpDeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | DeepSeek-V4-Flash-Vision-ExpDeepSeek | cross-developer peersimage, text |
DeepSeek-V4-Flash-Vision-ExpDeepSeek | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Flash Vision Exp vs GPT-5.6 Luna FAQs
Is DeepSeek V4 Flash Vision Exp or GPT-5.6 Luna better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Flash Vision Exp and GPT-5.6 Luna, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4 Flash Vision Exp or GPT-5.6 Luna?+
DeepSeek V4 Flash Vision Exp is $0.22 and GPT-5.6 Luna is $0.20 per million tokens, so GPT-5.6 Luna is cheaper on this metric. DeepSeek V4 Flash Vision Exp is $0.66 and GPT-5.6 Luna is $1.20 per million tokens, so DeepSeek V4 Flash Vision Exp is cheaper on this metric.
Which has a larger context window, DeepSeek V4 Flash Vision Exp or GPT-5.6 Luna?+
GPT-5.6 Luna has the larger sourced context window. DeepSeek V4 Flash Vision Exp supports 1,049K and GPT-5.6 Luna supports 1,050K.
Which performs better in benchmarks, DeepSeek V4 Flash Vision Exp 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 DeepSeek V4 Flash Vision Exp or GPT-5.6 Luna be self-hosted?+
DeepSeek V4 Flash Vision Exp is the only model in this pair currently marked as self-hostable. DeepSeek V4 Flash Vision Exp is open weight; GPT-5.6 Luna is not marked open weight.
Can DeepSeek V4 Flash Vision Exp and GPT-5.6 Luna understand images?+
DeepSeek V4 Flash Vision Exp 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, DeepSeek V4 Flash Vision Exp or GPT-5.6 Luna?+
DeepSeek V4 Flash Vision Exp has the larger sourced maximum output: DeepSeek V4 Flash Vision Exp supports 393K and GPT-5.6 Luna supports 128K output tokens.
Do DeepSeek V4 Flash Vision Exp and GPT-5.6 Luna support reasoning and tool use?+
DeepSeek V4 Flash Vision Exp: 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, DeepSeek V4 Flash Vision Exp or GPT-5.6 Luna?+
DeepSeek V4 Flash Vision Exp has 3 sourced provider routes; GPT-5.6 Luna has 2, so DeepSeek V4 Flash Vision Exp has broader tracked availability.
Which offers better value, DeepSeek V4 Flash Vision Exp 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.