// head_to_head
WizardLM-2 8x22B 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.
WizardLM-2 8x22B is the cheaper of the two; neither can be ranked on quality here.
WizardLM-2 8x22B 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
Qwen3.6 Plus
- Blended / 1M
- $0.731
- Context
- 1M
- Released
- Apr 2, 2026
- Overall score
- 68.9
Specs and pricing
| Metric | WizardLM-2 8x22B | 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. | $0.620win | $0.731 |
| Input price / 1M | $0.620 | $0.325win |
| Output price / 1M | $0.620win | $1.95 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | — |
| Context window | 66K | 1Mwin |
| Max output tokens | 8K | 66Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — WizardLM-2 8x22B 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 | WizardLM-2 8x22B | Qwen3.6 Plus |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $198.40/mo | $234.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $533.20/mo | $377.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $545.60/mo | $416.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $666.50/mo | $471.25/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,612/mo | $1,008/mo |
Which should you pick?
You need to fit large documents in one call
Qwen3.6 Plus
Wider context window — 1M against 66K.
You are cost-constrained
WizardLM-2 8x22B
Cheaper on blended list price at $0.620 per million tokens.
WizardLM-2 8x22B vs Qwen3.6 Plus FAQ
Which is better, WizardLM-2 8x22B or Qwen3.6 Plus?
WizardLM-2 8x22B is the cheaper of the two; neither can be ranked on quality here. WizardLM-2 8x22B 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 WizardLM-2 8x22B cheaper than Qwen3.6 Plus?
WizardLM-2 8x22B is cheaper. On a 3:1 input:output blend, WizardLM-2 8x22B lists at $0.620 per million tokens and Qwen3.6 Plus at $0.731 — WizardLM-2 8x22B is 18% cheaper. Input and output are priced separately — WizardLM-2 8x22B charges $0.620 in and $0.620 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 WizardLM-2 8x22B or Qwen3.6 Plus have a bigger context window?
Qwen3.6 Plus has the larger context window: 66K for WizardLM-2 8x22B against 1M for Qwen3.6 Plus. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
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.