// head_to_head
DeepSeek V4 Flash 0731 vs GPT-5.6 Luna
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Effectively the same quality — DeepSeek V4 Flash 0731 is the cheaper way to get it.
The two are within 0.6 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. DeepSeek V4 Flash 0731 lists 4.3× cheaper per blended million tokens. When quality ties, cost is the whole decision. DeepSeek V4 Flash 0731 also leads on measured cost per point of capability, at $0.0356 per point.
deepseek
DeepSeek V4 Flash 0731
- Blended / 1M
- $0.105
- Context
- 1.3M
- Released
- Jul 31, 2026
- Overall score
- 74.2
openai
GPT-5.6 Luna
- Blended / 1M
- $0.450
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 73.6
Specs and pricing
| Metric | DeepSeek V4 Flash 0731 | GPT-5.6 Luna |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 74.2 | 73.6 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0356win | $0.0911 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.105win | $0.450 |
| Input price / 1M | $0.080win | $0.200 |
| Output price / 1M | $0.180win | $1.20 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.016win | $0.020 |
| Context window | 1.3M | 1.1M |
| Max output tokens | 384Kwin | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — DeepSeek V4 Flash 0731 on top, GPT-5.6 Luna below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | DeepSeek V4 Flash 0731 | GPT-5.6 Luna |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $28.99/mo | $131.04/mo |
| RAG assistant 8K in / 600 out × 100K requests | $49.20/mo | $160.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $42.56/mo | $155.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $90.30/mo | $281.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $186.00/mo | $530.00/mo |
Which should you pick?
You are running this at volume
DeepSeek V4 Flash 0731
Lowest measured cost per point of capability at $0.0356 per point — the gap compounds with every request.
The workload is coding or agentic work
GPT-5.6 Luna
Leads on agentic coding — 48.4 against 46.8.
DeepSeek V4 Flash 0731 vs GPT-5.6 Luna FAQ
Which is better, DeepSeek V4 Flash 0731 or GPT-5.6 Luna?
Effectively the same quality — DeepSeek V4 Flash 0731 is the cheaper way to get it. The two are within 0.6 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. DeepSeek V4 Flash 0731 lists 4.3× cheaper per blended million tokens. When quality ties, cost is the whole decision. DeepSeek V4 Flash 0731 also leads on measured cost per point of capability, at $0.0356 per point.
Is DeepSeek V4 Flash 0731 cheaper than GPT-5.6 Luna?
DeepSeek V4 Flash 0731 is cheaper. On a 3:1 input:output blend, DeepSeek V4 Flash 0731 lists at $0.105 per million tokens and GPT-5.6 Luna at $0.450 — DeepSeek V4 Flash 0731 is 4.3× cheaper. Input and output are priced separately — DeepSeek V4 Flash 0731 charges $0.080 in and $0.180 out, GPT-5.6 Luna charges $0.200 and $1.20 — so the model that looks cheaper flips depending on how output-heavy your workload is.
DeepSeek V4 Flash 0731 vs GPT-5.6 Luna: which scores higher on benchmarks?
DeepSeek V4 Flash 0731 scores 74.2 and GPT-5.6 Luna scores 73.6 overall on LiveBench, the mean of its seven categories. That gap is inside the range that effort settings alone move a score, so treat them as equivalent on published quality. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, DeepSeek V4 Flash 0731 or GPT-5.6 Luna?
DeepSeek V4 Flash 0731. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. DeepSeek V4 Flash 0731 works out at $0.0356 per point and GPT-5.6 Luna at $0.0911.
Does DeepSeek V4 Flash 0731 or GPT-5.6 Luna have a bigger context window?
They are effectively the same — 1.3M for DeepSeek V4 Flash 0731 and 1.1M for GPT-5.6 Luna.
Do DeepSeek V4 Flash 0731 and GPT-5.6 Luna support prompt caching?
Both publish a cached-input rate: $0.016 per million for DeepSeek V4 Flash 0731 and $0.020 for GPT-5.6 Luna, against full input rates of $0.080 and $0.200. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.