// head_to_head
DeepSeek V4 Flash 0423 vs Qwen3.5-Flash
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 0423 and Qwen3.5-Flash are priced within ~10% of each other.
Qwen3.5-Flash 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 0423
- Blended / 1M
- $0.111
- Context
- 1.0M
- Released
- Apr 24, 2026
- Overall score
- 65.5
qwen
Qwen3.5-Flash
- Blended / 1M
- $0.114
- Context
- 1M
- Released
- Feb 25, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | DeepSeek V4 Flash 0423 | Qwen3.5-Flash |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 65.5 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0083 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.111 | $0.114 |
| Input price / 1M | $0.089 | $0.065win |
| Output price / 1M | $0.177win | $0.260 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.018 | — |
| Context window | 1.0M | 1M |
| Max output tokens | 384Kwin | 66K |
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 0423 on top, Qwen3.5-Flash 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 0423 | Qwen3.5-Flash |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $30.34/mo | $36.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $53.16/mo | $67.60/mo |
| Coding agent 40K in / 4K out × 20K requests | $45.37/mo | $72.80/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $98.35/mo | $84.50/mo |
| Bulk classification 500 in / 20 out × 5M requests | $203.79/mo | $188.50/mo |
DeepSeek V4 Flash 0423 vs Qwen3.5-Flash FAQ
Which is better, DeepSeek V4 Flash 0423 or Qwen3.5-Flash?
DeepSeek V4 Flash 0423 and Qwen3.5-Flash are priced within ~10% of each other. Qwen3.5-Flash 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 0423 cheaper than Qwen3.5-Flash?
They cost about the same. Both land near $0.111 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 0423 or Qwen3.5-Flash have a bigger context window?
They are effectively the same — 1.0M for DeepSeek V4 Flash 0423 and 1M for Qwen3.5-Flash.
Do DeepSeek V4 Flash 0423 and Qwen3.5-Flash support prompt caching?
DeepSeek V4 Flash 0423 publishes a cached-input rate of $0.018 per million tokens against a full input rate of $0.089. The catalogue lists no separate cached rate for Qwen3.5-Flash, 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.