// head_to_head
DeepSeek V4.1 Flash 1017 vs Qwen3.8 27B
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 V4.1 Flash 1017 scores higher, Qwen3.8 27B costs less — it depends on your workload.
DeepSeek V4.1 Flash 1017 is ahead by 15.6 points overall, and Qwen3.8 27B lists 1.5× cheaper per blended million tokens. Whether 15.6 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: Qwen3.8 27B has the lower sticker price, but DeepSeek V4.1 Flash 1017 earns each point of capability for less — $0.0018 against $0.0084 — because per-token rates do not predict how many tokens a model actually spends on a task.
deepseek
DeepSeek V4.1 Flash 1017
- Blended / 1M
- $0.525
- Context
- 1.0M
- Released
- Oct 17, 2026
- Overall score
- 74.0
qwen
Qwen3.8 27B
- Blended / 1M
- $0.342
- Context
- 1M
- Released
- Aug 14, 2026
- Overall score
- 58.4
Specs and pricing
| Metric | DeepSeek V4.1 Flash 1017 | Qwen3.8 27B |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 74.0win | 58.4 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0018win | $0.0084 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.525 | $0.342win |
| Input price / 1M | $0.300 | $0.039win |
| Output price / 1M | $1.20 | $1.25 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.0060 | $0.0039win |
| Context window | 1.0M | 1M |
| Max output tokens | 393Kwin | 66K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — DeepSeek V4.1 Flash 1017 on top, Qwen3.8 27B 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 V4.1 Flash 1017 | Qwen3.8 27B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $146.83/mo | $106.83/mo |
| RAG assistant 8K in / 600 out × 100K requests | $194.40/mo | $92.16/mo |
| Coding agent 40K in / 4K out × 20K requests | $171.36/mo | $111.54/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $375.30/mo | $131.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $723.00/mo | $204.95/mo |
Which should you pick?
You are running this at volume
DeepSeek V4.1 Flash 1017
Lowest measured cost per point of capability at $0.0018 per point — the gap compounds with every request.
Quality matters more than the bill
DeepSeek V4.1 Flash 1017
Highest overall LiveBench score of the two at 74.0.
The workload is coding or agentic work
DeepSeek V4.1 Flash 1017
Leads on long-horizon agentic — 40.9 against 20.6.
You are cost-constrained
Qwen3.8 27B
Cheaper on blended list price at $0.342 per million tokens.
DeepSeek V4.1 Flash 1017 vs Qwen3.8 27B FAQ
Which is better, DeepSeek V4.1 Flash 1017 or Qwen3.8 27B?
DeepSeek V4.1 Flash 1017 scores higher, Qwen3.8 27B costs less — it depends on your workload. DeepSeek V4.1 Flash 1017 is ahead by 15.6 points overall, and Qwen3.8 27B lists 1.5× cheaper per blended million tokens. Whether 15.6 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: Qwen3.8 27B has the lower sticker price, but DeepSeek V4.1 Flash 1017 earns each point of capability for less — $0.0018 against $0.0084 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is DeepSeek V4.1 Flash 1017 cheaper than Qwen3.8 27B?
Qwen3.8 27B is cheaper. On a 3:1 input:output blend, DeepSeek V4.1 Flash 1017 lists at $0.525 per million tokens and Qwen3.8 27B at $0.342 — Qwen3.8 27B is 1.5× cheaper. Input and output are priced separately — DeepSeek V4.1 Flash 1017 charges $0.300 in and $1.20 out, Qwen3.8 27B charges $0.039 and $1.25 — so the model that looks cheaper flips depending on how output-heavy your workload is.
DeepSeek V4.1 Flash 1017 vs Qwen3.8 27B: which scores higher on benchmarks?
DeepSeek V4.1 Flash 1017 scores 74.0 and Qwen3.8 27B scores 58.4 overall on LiveBench, the weighted mean of its seven categories. That is a 15.6-point lead for DeepSeek V4.1 Flash 1017. 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 V4.1 Flash 1017 or Qwen3.8 27B?
DeepSeek V4.1 Flash 1017. 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 V4.1 Flash 1017 works out at $0.0018 per point and Qwen3.8 27B at $0.0084.
Does DeepSeek V4.1 Flash 1017 or Qwen3.8 27B have a bigger context window?
They are effectively the same — 1.0M for DeepSeek V4.1 Flash 1017 and 1M for Qwen3.8 27B.
Do DeepSeek V4.1 Flash 1017 and Qwen3.8 27B support prompt caching?
Both publish a cached-input rate: $0.0060 per million for DeepSeek V4.1 Flash 1017 and $0.0039 for Qwen3.8 27B, against full input rates of $0.300 and $0.039. 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.