// head_to_head
DeepSeek V3.1 vs DeepSeek V4 Flash Vision Exp
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 is the cheaper of the two; neither can be ranked on quality here.
DeepSeek V3.1 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 V3.1
- Blended / 1M
- $0.425
- Context
- 164K
- Released
- Aug 21, 2025
- Overall score
- Not evaluated
deepseek
DeepSeek V4 Flash Vision Exp
- Blended / 1M
- $0.330
- Context
- 1.0M
- Released
- Aug 21, 2026
- Overall score
- 76.8
Specs and pricing
| Metric | DeepSeek V3.1 | DeepSeek V4 Flash Vision Exp |
|---|---|---|
| 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. | $0.425 | $0.330win |
| Input price / 1M | $0.250 | $0.220win |
| Output price / 1M | $0.950 | $0.660win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.130 | $0.0070win |
| Context window | 164K | 1.0Mwin |
| Max output tokens | 33K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — DeepSeek V3.1 on top, DeepSeek V4 Flash Vision Exp 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 V3.1 | DeepSeek V4 Flash Vision Exp |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $127.36/mo | $90.26/mo |
| RAG assistant 8K in / 600 out × 100K requests | $209.00/mo | $130.40/mo |
| Coding agent 40K in / 4K out × 20K requests | $208.80/mo | $109.52/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $315.25/mo | $258.85/mo |
| Bulk classification 500 in / 20 out × 5M requests | $660.00/mo | $509.50/mo |
Which should you pick?
You need to fit large documents in one call
DeepSeek V4 Flash Vision Exp
Wider context window — 1.0M against 164K.
DeepSeek V3.1 vs DeepSeek V4 Flash Vision Exp FAQ
Which is better, DeepSeek V3.1 or DeepSeek V4 Flash Vision Exp?
DeepSeek V4 Flash Vision Exp is the cheaper of the two; neither can be ranked on quality here. DeepSeek V3.1 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 V3.1 cheaper than DeepSeek V4 Flash Vision Exp?
DeepSeek V4 Flash Vision Exp is cheaper. On a 3:1 input:output blend, DeepSeek V3.1 lists at $0.425 per million tokens and DeepSeek V4 Flash Vision Exp at $0.330 — DeepSeek V4 Flash Vision Exp is 29% cheaper. Input and output are priced separately — DeepSeek V3.1 charges $0.250 in and $0.950 out, DeepSeek V4 Flash Vision Exp charges $0.220 and $0.660 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does DeepSeek V3.1 or DeepSeek V4 Flash Vision Exp have a bigger context window?
DeepSeek V4 Flash Vision Exp has the larger context window: 164K for DeepSeek V3.1 against 1.0M for DeepSeek V4 Flash Vision Exp. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do DeepSeek V3.1 and DeepSeek V4 Flash Vision Exp support prompt caching?
Both publish a cached-input rate: $0.130 per million for DeepSeek V3.1 and $0.0070 for DeepSeek V4 Flash Vision Exp, against full input rates of $0.250 and $0.220. 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.