// head_to_head
DeepSeek V4 Flash 0423 vs GPT-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.
DeepSeek V4 Flash 0423 is the cheaper of the two; neither can be ranked on quality here.
GPT-6 Luna does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
deepseek
DeepSeek V4 Flash 0423
- Blended / 1M
- $0.111
- Context
- 1.0M
- Released
- Apr 24, 2026
- Overall score
- 65.5
openai
GPT-6 Luna
- Blended / 1M
- $0.200
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | DeepSeek V4 Flash 0423 | GPT-6 Luna |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 65.5 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0083 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.111win | $0.200 |
| Input price / 1M | $0.089win | $0.100 |
| Output price / 1M | $0.177win | $0.500 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.018 | $0.010win |
| Context window | 1.0M | 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 0423 on top, GPT-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 0423 | GPT-6 Luna |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $30.34/mo | $57.52/mo |
| RAG assistant 8K in / 600 out × 100K requests | $53.16/mo | $74.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $45.37/mo | $69.60/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $98.35/mo | $133.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $203.79/mo | $255.00/mo |
Which should you pick?
You are cost-constrained
DeepSeek V4 Flash 0423
Cheaper on blended list price at $0.111 per million tokens.
DeepSeek V4 Flash 0423 vs GPT-6 Luna FAQ
Which is better, DeepSeek V4 Flash 0423 or GPT-6 Luna?
DeepSeek V4 Flash 0423 is the cheaper of the two; neither can be ranked on quality here. GPT-6 Luna does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is DeepSeek V4 Flash 0423 cheaper than GPT-6 Luna?
DeepSeek V4 Flash 0423 is cheaper. On a 3:1 input:output blend, DeepSeek V4 Flash 0423 lists at $0.111 per million tokens and GPT-6 Luna at $0.200 — DeepSeek V4 Flash 0423 is 1.8× cheaper. Input and output are priced separately — DeepSeek V4 Flash 0423 charges $0.089 in and $0.177 out, GPT-6 Luna charges $0.100 and $0.500 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does DeepSeek V4 Flash 0423 or GPT-6 Luna have a bigger context window?
They are effectively the same — 1.0M for DeepSeek V4 Flash 0423 and 1.1M for GPT-6 Luna.
Do DeepSeek V4 Flash 0423 and GPT-6 Luna support prompt caching?
Both publish a cached-input rate: $0.018 per million for DeepSeek V4 Flash 0423 and $0.010 for GPT-6 Luna, against full input rates of $0.089 and $0.100. 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.