// head_to_head
Qwen Plus 0728 vs Qwen3.6 Plus
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.
Qwen Plus 0728 is the cheaper of the two; neither can be ranked on quality here.
Qwen Plus 0728 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.
qwen
Qwen Plus 0728
- Blended / 1M
- $0.390
- Context
- 1M
- Released
- Sep 8, 2025
- Overall score
- Not evaluated
qwen
Qwen3.6 Plus
- Blended / 1M
- $0.731
- Context
- 1M
- Released
- Apr 2, 2026
- Overall score
- 68.9
Specs and pricing
| Metric | Qwen Plus 0728 | Qwen3.6 Plus |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 68.9 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.1262 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.390win | $0.731 |
| Input price / 1M | $0.260win | $0.325 |
| Output price / 1M | $0.780win | $1.95 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | — |
| Context window | 1M | 1M |
| Max output tokens | 33K | 66Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Qwen Plus 0728 on top, Qwen3.6 Plus 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 | Qwen Plus 0728 | Qwen3.6 Plus |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $124.80/mo | $234.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $254.80/mo | $377.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $270.40/mo | $416.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $318.50/mo | $471.25/mo |
| Bulk classification 500 in / 20 out × 5M requests | $728.00/mo | $1,008/mo |
Which should you pick?
You are cost-constrained
Qwen Plus 0728
Cheaper on blended list price at $0.390 per million tokens.
Qwen Plus 0728 vs Qwen3.6 Plus FAQ
Which is better, Qwen Plus 0728 or Qwen3.6 Plus?
Qwen Plus 0728 is the cheaper of the two; neither can be ranked on quality here. Qwen Plus 0728 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 Qwen Plus 0728 cheaper than Qwen3.6 Plus?
Qwen Plus 0728 is cheaper. On a 3:1 input:output blend, Qwen Plus 0728 lists at $0.390 per million tokens and Qwen3.6 Plus at $0.731 — Qwen Plus 0728 is 1.9× cheaper. Input and output are priced separately — Qwen Plus 0728 charges $0.260 in and $0.780 out, Qwen3.6 Plus charges $0.325 and $1.95 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Qwen Plus 0728 or Qwen3.6 Plus have a bigger context window?
They are effectively the same — 1M for Qwen Plus 0728 and 1M for Qwen3.6 Plus.
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.