// head_to_head
DeepSeek V4 Flash Vision Exp vs GPT-6 Luna Pro
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.
GPT-6 Luna Pro is the cheaper of the two; neither can be ranked on quality here.
GPT-6 Luna Pro 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 Vision Exp
- Blended / 1M
- $0.330
- Context
- 1.0M
- Released
- Aug 21, 2026
- Overall score
- 76.8
openai
GPT-6 Luna Pro
- Blended / 1M
- $0.200
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | DeepSeek V4 Flash Vision Exp | GPT-6 Luna Pro |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 76.8 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0277 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.330 | $0.200win |
| Input price / 1M | $0.220 | $0.100win |
| Output price / 1M | $0.660 | $0.500win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.0070win | $0.010 |
| Context window | 1.0M | 1.1M |
| Max output tokens | 944Kwin | 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 Vision Exp on top, GPT-6 Luna Pro 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 Vision Exp | GPT-6 Luna Pro |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $90.26/mo | $57.52/mo |
| RAG assistant 8K in / 600 out × 100K requests | $130.40/mo | $74.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $109.52/mo | $69.60/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $258.85/mo | $133.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $509.50/mo | $255.00/mo |
Which should you pick?
You are cost-constrained
GPT-6 Luna Pro
Cheaper on blended list price at $0.200 per million tokens.
DeepSeek V4 Flash Vision Exp vs GPT-6 Luna Pro FAQ
Which is better, DeepSeek V4 Flash Vision Exp or GPT-6 Luna Pro?
GPT-6 Luna Pro is the cheaper of the two; neither can be ranked on quality here. GPT-6 Luna Pro 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 Vision Exp cheaper than GPT-6 Luna Pro?
GPT-6 Luna Pro is cheaper. On a 3:1 input:output blend, DeepSeek V4 Flash Vision Exp lists at $0.330 per million tokens and GPT-6 Luna Pro at $0.200 — GPT-6 Luna Pro is 1.6× cheaper. Input and output are priced separately — DeepSeek V4 Flash Vision Exp charges $0.220 in and $0.660 out, GPT-6 Luna Pro 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 Vision Exp or GPT-6 Luna Pro have a bigger context window?
They are effectively the same — 1.0M for DeepSeek V4 Flash Vision Exp and 1.1M for GPT-6 Luna Pro.
Do DeepSeek V4 Flash Vision Exp and GPT-6 Luna Pro support prompt caching?
Both publish a cached-input rate: $0.0070 per million for DeepSeek V4 Flash Vision Exp and $0.010 for GPT-6 Luna Pro, against full input rates of $0.220 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.