Qwen · Balanced
Qwen3.6 Max Preview
Qwen3.6 Max Preview is Alibaba's flagship Qwen 3.6 model for complex tasks. It supports both thinking and non-thinking modes in a single model id.
- Input
- Text
- Output
- Text
- Discovery tier
- Balanced
Pricing
USD per 1M tokens- Input
- $1.04
- Output
- $6.24
Reference prices as published by the model provider. API example
Specifications
- Context window
- 245.8K
- Max output
- 65.5K
Side by side
Qwen3.6 Max Preview compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Qwen3.6 Max PreviewQwenThis model | Abliterated ModelabliterationRemove | |
|---|---|---|
| Provider | Qwen | abliteration |
| Discovery tier | Balanced | Balanced |
| Added | Apr 20, 2026 | Aug 27, 2026 |
| Limits | ||
| Context window | 245.76K | 262.14KBest |
| Max output | 65.54K | 262.13KBest |
| Average output | — | 1.4K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $1.04 | $1.00Best |
| Output | $6.24 | $3.00Best |
| Cache read | — | $0.10 |
| Performance | ||
| Throughput | — | 171.0 tok/s |
| Time to first token | — | 2.2 s |
| Request success (24h) | — | 89.0% |
| Capabilities | ||
| Reasoning | No | Yes |
| Vision | No | Yes |
| Structured output | No | Yes |
| Reasoning effort | — | none, minimal, low, medium, high, xhigh, max |
Public evaluations
Benchmarks
There are no public benchmark results for these models yet.
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen/qwen3.6-max-preview",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="qwen/qwen3.6-max-preview",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Qwen
Related models
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