// head_to_head
DeepSeek V4 Flash Vision Exp vs Llama 4 Maverick
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 and Llama 4 Maverick are priced within ~10% of each other.
Llama 4 Maverick 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
meta-llama
Llama 4 Maverick
- Blended / 1M
- $0.304
- Context
- 1.0M
- Released
- Apr 5, 2025
- Overall score
- Not evaluated
Specs and pricing
| Metric | DeepSeek V4 Flash Vision Exp | Llama 4 Maverick |
|---|---|---|
| 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 | $0.304 |
| Input price / 1M | $0.220 | $0.188win |
| Output price / 1M | $0.660 | $0.652 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.0070 | — |
| Context window | 1.0M | 1.0M |
| Max output tokens | 944Kwin | 16K |
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, Llama 4 Maverick 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 | Llama 4 Maverick |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $90.26/mo | $97.20/mo |
| RAG assistant 8K in / 600 out × 100K requests | $130.40/mo | $189.15/mo |
| Coding agent 40K in / 4K out × 20K requests | $109.52/mo | $202.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $258.85/mo | $236.44/mo |
| Bulk classification 500 in / 20 out × 5M requests | $509.50/mo | $534.00/mo |
DeepSeek V4 Flash Vision Exp vs Llama 4 Maverick FAQ
Which is better, DeepSeek V4 Flash Vision Exp or Llama 4 Maverick?
DeepSeek V4 Flash Vision Exp and Llama 4 Maverick are priced within ~10% of each other. Llama 4 Maverick 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 Llama 4 Maverick?
They cost about the same. Both land near $0.330 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does DeepSeek V4 Flash Vision Exp or Llama 4 Maverick have a bigger context window?
They are effectively the same — 1.0M for DeepSeek V4 Flash Vision Exp and 1.0M for Llama 4 Maverick.
Do DeepSeek V4 Flash Vision Exp and Llama 4 Maverick support prompt caching?
DeepSeek V4 Flash Vision Exp publishes a cached-input rate of $0.0070 per million tokens against a full input rate of $0.220. The catalogue lists no separate cached rate for Llama 4 Maverick, which means the provider does not price it separately here — not that caching is unavailable.
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.