Qwen · Balanced
Qwen3.5 Plus Thinking
Qwen 3.5 Plus with extended reasoning. A commercial model with hybrid linear attention and sparse MoE architecture. Supports text, image, and video input with a 1M context window.
- Reasoning
- Vision
- Video input
- Input
- Text, Images, Video
- Output
- Text
- Discovery tier
- Balanced
Pricing
USD per 1M tokens- Input
- $0.40
- Output
- $2.40
- Cache read
- $0.04
Reference prices as published by the model provider. API example
Specifications
- Context window
- 983.6K
- Max output
- 65.5K
Side by side
Qwen3.5 Plus Thinking compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Qwen3.5 Plus ThinkingQwenThis model | Qwen 3.8 27B DarkIdolQwenRemove | |
|---|---|---|
| Provider | Qwen | Qwen |
| Discovery tier | Balanced | Open weights |
| Added | Feb 16, 2026 | Oct 5, 2026 |
| Limits | ||
| Context window | 983.62KBest | 262.14K |
| Max output | 65.54KBest | 32.77K |
| Average output | — | 554 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.40 | $0.10Best |
| Output | $2.40 | $0.60Best |
| Cache read | $0.04Best | $0.05 |
| Performance | ||
| Throughput | — | 40.9 tok/s |
| Time to first token | — | 2.2 s |
| Request success (24h) | — | 90.3% |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | No | Yes |
| Vision | Yes | Yes |
| Video input | Yes | No |
| Reasoning effort | — | — |
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/qwen3.5-plus:thinking",
"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/qwen3.5-plus:thinking",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Qwen
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