DeepSeek V4 Flash vs GPT-5.6 Luna
Two fast, multimodal reasoning models go head-to-head: DeepSeek's V4 Flash 0731 and OpenAI's GPT-5.6 Luna. Both share a 1.05M-token context window and full text, image, audio, video and file support, but they split on economics: DeepSeek undercuts on output tokens and latency, while GPT-5.6 Luna charges less for input and guarantees no training on user prompts.
Spec Comparison
| Metric | DeepSeek V4 Flash 0731 | GPT-5.6 Luna |
|---|---|---|
| 📋 Overview | ||
| Developer | DeepSeek | OpenAI |
| Context window | 1.05M tokens | 1.05M tokens |
| Reasoning | Yes | Yes |
| Input modalities | Text, image, audio, video, file | Text, image, audio, video, file |
| Output modalities | Text, image, audio, video | Text, image, audio, video |
| Providers | 20 | 3 |
| 💰 Pricing (per 1M tokens) | ||
| Input | $0.14 | $0.10 |
| Output | $0.28 | $0.60 |
| Cached input | $0.0028 | $0.01 |
| Weighted avg input | $0.03251 | $0.03833 |
| Cache write | None | From $0.125 (≤272K $0.125, >272K $0.25) |
| âš¡ Performance | ||
| Latency (p50) | 1.45 s | 2.35 s |
| Throughput (p50) | 74.0 tok/s | 55.0 tok/s |
| 🔧 Features | ||
| Quantization | FP8 | Unknown |
| Max output tokens | 384K | 128K |
| Stream cancellation | Yes | Yes |
| Tool use | Yes | Yes |
| No prompt training | No | Yes |
| Caching | Yes | Yes |
Highlighted cells mark the better value on each metric. Latency is measured as median time to first token; throughput as median tokens per second. Pricing figures are list prices per 1M tokens.
A short read of the numbers
DeepSeek V4 Flash is the value pick on almost every cost and speed metric. Output tokens cost $0.28 per million versus $0.60 for GPT-5.6 Luna, cached input is roughly 3.6 times cheaper at $0.0028, and it charges no cache-write fee where OpenAI charges from $0.125. It is also faster on the wire: 1.45 s median latency versus 2.35 s, and 74.0 tok/s throughput versus 55.0 tok/s. Its 384K max output dwarfs Luna's 128K, and it is served by 20 providers compared with 3 for OpenAI.
GPT-5.6 Luna wins where economics and privacy matter for input-heavy workloads. Its input price of $0.10 per million undercuts DeepSeek's $0.14, and it is the only one of the two to guarantee that user prompts are not used for training. DeepSeek makes no such commitment.
The takeaway: DeepSeek V4 Flash is the better default for output-heavy, latency-sensitive applications on a budget, while GPT-5.6 Luna is the pick when cheap input tokens and strict prompt privacy outweigh speed and output pricing. On everything else, the two models are remarkably even: identical 1.05M context, identical multimodal input and output support, and both support tool use, caching and stream cancellation.