// head_to_head
DeepSeek V4 Pro 0813 vs GPT-6 Astra
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 is the cheaper of the two; neither can be ranked on quality here.
DeepSeek V4 Pro 0813 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 Pro 0813
- Blended / 1M
- $0.990
- Context
- 1.0M
- Released
- Aug 12, 2026
- Overall score
- Not evaluated
openai
GPT-6 Astra
- Blended / 1M
- $20.00
- Context
- 1.1M
- Released
- Sep 4, 2026
- Overall score
- 85.6
Specs and pricing
| Metric | DeepSeek V4 Pro 0813 | GPT-6 Astra |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 85.6 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0455 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.990win | $20.00 |
| Input price / 1M | $0.660win | $10.00 |
| Output price / 1M | $1.98win | $50.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.022win | $1.00 |
| Context window | 1.0M | 1.1M |
| Max output tokens | 393Kwin | 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-6 Astra 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-6 Astra |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $270.86/mo | $5,752/mo |
| RAG assistant 8K in / 600 out × 100K requests | $391.60/mo | $7,400/mo |
| Coding agent 40K in / 4K out × 20K requests | $329.12/mo | $6,960/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $776.60/mo | $13,300/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,529/mo | $25,500/mo |
Which should you pick?
You are cost-constrained
DeepSeek V4 Pro 0813
Cheaper on blended list price at $0.990 per million tokens.
DeepSeek V4 Pro 0813 vs GPT-6 Astra FAQ
Which is better, DeepSeek V4 Pro 0813 or GPT-6 Astra?
DeepSeek V4 Pro 0813 is the cheaper of the two; neither can be ranked on quality here. DeepSeek V4 Pro 0813 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 Pro 0813 cheaper than GPT-6 Astra?
DeepSeek V4 Pro 0813 is cheaper. On a 3:1 input:output blend, DeepSeek V4 Pro 0813 lists at $0.990 per million tokens and GPT-6 Astra at $20.00 — DeepSeek V4 Pro 0813 is 20× cheaper. Input and output are priced separately — DeepSeek V4 Pro 0813 charges $0.660 in and $1.98 out, GPT-6 Astra charges $10.00 and $50.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does DeepSeek V4 Pro 0813 or GPT-6 Astra have a bigger context window?
They are effectively the same — 1.0M for DeepSeek V4 Pro 0813 and 1.1M for GPT-6 Astra.
Do DeepSeek V4 Pro 0813 and GPT-6 Astra support prompt caching?
Both publish a cached-input rate: $0.022 per million for DeepSeek V4 Pro 0813 and $1.00 for GPT-6 Astra, against full input rates of $0.660 and $10.00. 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 2026-10-25, 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.