// head_to_head
DeepSeek V4 Pro 0813 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 Pro 0813 scores higher, GPT-5.6 Luna costs less — it depends on your workload.
DeepSeek V4 Pro 0813 is ahead by 3.9 points overall, and GPT-5.6 Luna lists 4.0× cheaper per blended million tokens. Whether 3.9 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: GPT-5.6 Luna has the lower sticker price, but DeepSeek V4 Pro 0813 earns each point of capability for less — $0.0241 against $0.0911 — because per-token rates do not predict how many tokens a model actually spends on a task.
deepseek
DeepSeek V4 Pro 0813
- Blended / 1M
- $1.78
- Context
- 1.0M
- Released
- Aug 12, 2026
- Overall score
- 77.4
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 Pro 0813 | GPT-5.6 Luna |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 77.4win | 73.6 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0241win | $0.0911 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.78 | $0.450win |
| Input price / 1M | $1.19 | $0.200win |
| Output price / 1M | $3.56 | $1.20win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.040 | $0.020win |
| Context window | 1.0M | 1.1M |
| Max output tokens | — | 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 Pro 0813 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 Pro 0813 | GPT-5.6 Luna |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $487.56/mo | $131.04/mo |
| RAG assistant 8K in / 600 out × 100K requests | $704.88/mo | $160.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $592.42/mo | $155.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1397.88/mo | $281.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $2752.20/mo | $530.00/mo |
Which should you pick?
You are running this at volume
DeepSeek V4 Pro 0813
Lowest measured cost per point of capability at $0.0241 per point — the gap compounds with every request.
Quality matters more than the bill
DeepSeek V4 Pro 0813
Highest overall LiveBench score of the two at 77.4.
The workload is coding or agentic work
DeepSeek V4 Pro 0813
Leads on agentic coding — 54.9 against 48.4.
You are cost-constrained
GPT-5.6 Luna
Cheaper on blended list price at $0.450 per million tokens.
DeepSeek V4 Pro 0813 vs GPT-5.6 Luna FAQ
Which is better, DeepSeek V4 Pro 0813 or GPT-5.6 Luna?
DeepSeek V4 Pro 0813 scores higher, GPT-5.6 Luna costs less — it depends on your workload. DeepSeek V4 Pro 0813 is ahead by 3.9 points overall, and GPT-5.6 Luna lists 4.0× cheaper per blended million tokens. Whether 3.9 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: GPT-5.6 Luna has the lower sticker price, but DeepSeek V4 Pro 0813 earns each point of capability for less — $0.0241 against $0.0911 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is DeepSeek V4 Pro 0813 cheaper than GPT-5.6 Luna?
GPT-5.6 Luna is cheaper. On a 3:1 input:output blend, DeepSeek V4 Pro 0813 lists at $1.78 per million tokens and GPT-5.6 Luna at $0.450 — GPT-5.6 Luna is 4.0× cheaper. Input and output are priced separately — DeepSeek V4 Pro 0813 charges $1.19 in and $3.56 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 Pro 0813 vs GPT-5.6 Luna: which scores higher on benchmarks?
DeepSeek V4 Pro 0813 scores 77.4 and GPT-5.6 Luna scores 73.6 overall on LiveBench, the mean of its seven categories. That is a 3.9-point lead for DeepSeek V4 Pro 0813. 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 Pro 0813 or GPT-5.6 Luna?
DeepSeek V4 Pro 0813. 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 Pro 0813 works out at $0.0241 per point and GPT-5.6 Luna at $0.0911.
Does DeepSeek V4 Pro 0813 or GPT-5.6 Luna have a bigger context window?
They are effectively the same — 1.0M for DeepSeek V4 Pro 0813 and 1.1M for GPT-5.6 Luna.
Do DeepSeek V4 Pro 0813 and GPT-5.6 Luna support prompt caching?
Both publish a cached-input rate: $0.040 per million for DeepSeek V4 Pro 0813 and $0.020 for GPT-5.6 Luna, against full input rates of $1.19 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.