Qwen 3.6 Series
Qwen 3.6 is an API model from Alibaba. It’s positioned for hard reasoning and planning—work that benefits from iteration, not just one-shot answers.
Intelligence
Good
Speed
Slow
60 tok/s output
Cost
Moderate
$0.50 in / $3.00 out
Context
256K
Up to 256,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Reasoning
Solves complex math, logic, and planning tasks.
Context Window
256k tokens
≈ 197+ pages
4k Chat 聊天
32k Long docs 长文档
128k Books 整本书
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
256k
Availability
API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales
Pricing Model
Pay per token
Input and output billed separately.
Pay-per-token Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
7.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Best open-source reasoning model at its release — 235B-A22B (Thinking) beats DeepSeek-R1 on 17/23 benchmarks
- Toggle-able thinking mode: same weights serve both reasoning and fast-chat modes
- Strong 119-language coverage; the most genuinely multilingual frontier-tier model
Best use cases
- Multilingual production deployments (119 languages) where most models stay English-centric
- Self-hosted reasoning workflows that need both 'fast mode' and 'thinking mode' from one weight set
- Open-weights agentic coding (Qwen3-Coder) with very large context windows
Tools to try
Not ideal for
- Multimodal tasks (Qwen3 base is text — vision lives in Qwen3-VL, audio in Qwen3-Audio)
- Edge / single-consumer-GPU deployments at the 235B scale
Model Evolution
qwen is Alibaba's language model family.