DeepSeek V4 Flash vs GPT-5.6 Luna

Spec Comparison

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.

Note: AI Model Tiers, Context Windows, and the Speed vs Price Tradeoff

What model tiers and Flash names mean

AI labs ship several versions of each model family aimed at different jobs. Flagship models are the largest and most capable, while tier models like the Flash variant on this page trade a little raw capability for speed, lower cost, and wider availability. This page compares DeepSeek's V4 Flash 0731 (a build dated 0731, meaning July 31 of its release year) against OpenAI's GPT-5.6 Luna.

• Flash-style models are designed for high-volume, latency-sensitive workloads such as chat apps, customer support, and agentic tools.
• A dated build number like 0731 tells developers exactly which version they are calling, which matters for reproducibility.
• Both models support reasoning, the ability to think through steps before answering harder questions.

Context windows: how much a model can hold at once

The context window is the total amount of text (or other tokens) a model can consider in a single conversation or request. Both models on this page offer a 1.05M-token window, which sits at the very large end of the spectrum.

• Tokens are chunks of text; a rough rule is that 1,000 tokens is close to 750 English words.
• A 1.05M-token window can hold tens of long documents, large codebases, or entire book manuscripts in one prompt.
• A larger context window means you can paste whole files instead of summarizing them yourself, but it also raises the cost of long prompts.

Latency and throughput: speed in practice

Latency measures how quickly the first token comes back, and throughput measures how many tokens per second the model produces once it starts. In the table, DeepSeek records a 1.45 second median latency versus 2.35 seconds for Luna, and 74.0 tokens per second versus 55.0.

• Lower latency matters most for interactive chat, where a long pause feels like waiting.
• Higher throughput matters most for bulk jobs such as summarizing thousands of documents or generating long reports.
• Real-world speed also depends on provider load, request size, and whether the model is in reasoning mode.

The economics of tokens

API pricing is charged per million tokens, separately for input and output, with discounts for cached input. The table shows DeepSeek at $0.14 input and $0.28 output, while Luna charges $0.10 input and $0.60 output. Cached input is $0.0028 for DeepSeek versus $0.01 for Luna, and Luna also charges cache-write fees starting at $0.125.

• Output tokens are priced several times higher than input because generating text is more compute-intensive.
• Cached input lets you re-use previously processed prompts at a fraction of the price, which compounds on big, repetitive workloads.
• For output-heavy applications like code generation and long-form writing, the output price often dominates the bill.

Multimodal and developer features

Both models accept text, image, audio, video, and file inputs, and can output text, image, audio, and video. Both support tool use, caching, and stream cancellation. They split on a few details: DeepSeek allows up to 384K output tokens and runs on 20 providers with FP8 quantization, while Luna allows 128K output tokens, runs on 3 providers, and is the only one of the pair that guarantees prompts are not used for training.

• Multimodal input lets a single model read documents, look at screenshots, and listen to audio in one conversation.
• Tool use lets models call functions, query databases, and drive APIs, which makes agents possible.
• More providers means more routing options and more redundancy for uptime-sensitive applications.

Fun Facts

• Both models share an identical 1.05M context window, which is unusual for rivals from different labs.
• The median latency figures are measured from the provider side, so shorter means a faster first response.
• A 384K max output lets a model write a small book in a single response; 128K still covers a long report or full script.
• DeepSeek's weighted average input price is lower, but Luna wins on the headline input rate when nothing is cached.
• FP8 quantization halves memory use versus full precision, which helps providers serve the model cheaply at scale.
• Privacy-conscious teams often pick a provider that commits to zero training on user prompts, a guarantee Luna makes and DeepSeek does not.
• No raw benchmark scores appear on this page, so judge the pair by price, speed, and feature fit for your workload.