// head_to_head
DeepSeek V4 Flash Vision Exp 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.
DeepSeek V4 Flash Vision Exp wins outright — it scores higher and costs less.
DeepSeek V4 Flash Vision Exp leads by 3.2 points overall while listing 36% cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. DeepSeek V4 Flash Vision Exp also leads on measured cost per point of capability, at $0.0277 per point.
deepseek
DeepSeek V4 Flash Vision Exp
- Blended / 1M
- $0.330
- Context
- 1.0M
- Released
- Aug 21, 2026
- Overall score
- 76.8
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 Vision Exp | GPT-5.6 Luna |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 76.8win | 73.6 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0277win | $0.0911 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.330win | $0.450 |
| Input price / 1M | $0.220 | $0.200win |
| Output price / 1M | $0.660win | $1.20 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.0070win | $0.020 |
| 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 Vision Exp 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 Vision Exp | GPT-5.6 Luna |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $90.26/mo | $131.04/mo |
| RAG assistant 8K in / 600 out × 100K requests | $130.40/mo | $160.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $109.52/mo | $155.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $258.85/mo | $281.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $509.50/mo | $530.00/mo |
Which should you pick?
You are running this at volume
DeepSeek V4 Flash Vision Exp
Lowest measured cost per point of capability at $0.0277 per point — the gap compounds with every request.
Quality matters more than the bill
DeepSeek V4 Flash Vision Exp
Highest overall LiveBench score of the two at 76.8.
The workload is coding or agentic work
DeepSeek V4 Flash Vision Exp
Leads on agentic coding — 65.1 against 48.4.
DeepSeek V4 Flash Vision Exp vs GPT-5.6 Luna FAQ
Which is better, DeepSeek V4 Flash Vision Exp or GPT-5.6 Luna?
DeepSeek V4 Flash Vision Exp wins outright — it scores higher and costs less. DeepSeek V4 Flash Vision Exp leads by 3.2 points overall while listing 36% cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. DeepSeek V4 Flash Vision Exp also leads on measured cost per point of capability, at $0.0277 per point.
Is DeepSeek V4 Flash Vision Exp cheaper than GPT-5.6 Luna?
DeepSeek V4 Flash Vision Exp is cheaper. On a 3:1 input:output blend, DeepSeek V4 Flash Vision Exp lists at $0.330 per million tokens and GPT-5.6 Luna at $0.450 — DeepSeek V4 Flash Vision Exp is 36% cheaper. Input and output are priced separately — DeepSeek V4 Flash Vision Exp charges $0.220 in and $0.660 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 Vision Exp vs GPT-5.6 Luna: which scores higher on benchmarks?
DeepSeek V4 Flash Vision Exp scores 76.8 and GPT-5.6 Luna scores 73.6 overall on LiveBench, the mean of its seven categories. That is a 3.2-point lead for DeepSeek V4 Flash Vision Exp. 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 Vision Exp or GPT-5.6 Luna?
DeepSeek V4 Flash Vision Exp. 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 Vision Exp works out at $0.0277 per point and GPT-5.6 Luna at $0.0911.
Does DeepSeek V4 Flash Vision Exp or GPT-5.6 Luna have a bigger context window?
They are effectively the same — 1.0M for DeepSeek V4 Flash Vision Exp and 1.1M for GPT-5.6 Luna.
Do DeepSeek V4 Flash Vision Exp and GPT-5.6 Luna support prompt caching?
Both publish a cached-input rate: $0.0070 per million for DeepSeek V4 Flash Vision Exp and $0.020 for GPT-5.6 Luna, against full input rates of $0.220 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.