// head_to_head
Seed 1.6 vs Qwen3.6 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.
Seed 1.6 is the cheaper of the two; neither can be ranked on quality here.
Seed 1.6 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.
bytedance-seed
Seed 1.6
- Blended / 1M
- $0.688
- Context
- 262K
- Released
- Dec 23, 2025
- Overall score
- Not evaluated
qwen
Qwen3.6 27B
- Blended / 1M
- $0.915
- Context
- 262K
- Released
- Apr 27, 2026
- Overall score
- 64.0
Specs and pricing
| Metric | Seed 1.6 | Qwen3.6 27B |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 64.0 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.1074 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.688win | $0.915 |
| Input price / 1M | $0.250win | $0.320 |
| Output price / 1M | $2.00win | $2.70 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | $0.150 |
| Context window | 262K | 262K |
| Max output tokens | 33K | 262Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Seed 1.6 on top, Qwen3.6 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 | Seed 1.6 | Qwen3.6 27B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $220.00/mo | $280.56/mo |
| RAG assistant 8K in / 600 out × 100K requests | $320.00/mo | $350.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $360.00/mo | $376.80/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $400.00/mo | $514.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $825.00/mo | $985.00/mo |
Which should you pick?
You are cost-constrained
Seed 1.6
Cheaper on blended list price at $0.688 per million tokens.
Seed 1.6 vs Qwen3.6 27B FAQ
Which is better, Seed 1.6 or Qwen3.6 27B?
Seed 1.6 is the cheaper of the two; neither can be ranked on quality here. Seed 1.6 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 Seed 1.6 cheaper than Qwen3.6 27B?
Seed 1.6 is cheaper. On a 3:1 input:output blend, Seed 1.6 lists at $0.688 per million tokens and Qwen3.6 27B at $0.915 — Seed 1.6 is 33% cheaper. Input and output are priced separately — Seed 1.6 charges $0.250 in and $2.00 out, Qwen3.6 27B charges $0.320 and $2.70 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Seed 1.6 or Qwen3.6 27B have a bigger context window?
They are effectively the same — 262K for Seed 1.6 and 262K for Qwen3.6 27B.
Do Seed 1.6 and Qwen3.6 27B support prompt caching?
Qwen3.6 27B publishes a cached-input rate of $0.150 per million tokens against a full input rate of $0.320. The catalogue lists no separate cached rate for Seed 1.6, 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.