Analysis Summary
Qwen3.6 35B A3B is a compact mixture-of-experts model with a 262K context window, text, image, and video input, plus tool use and function calling. Its coding capability is the clearest strength, while reasoning performance is more limited than its software implementation results. Long-context and instruction-following measurements support structured work, although autonomous agent performance is comparatively weaker.
For businesses, it suits code generation, technical documentation, multimodal document processing, and structured SEO production where price and context capacity matter. Function calling makes it practical for API workflows, but teams should keep human review around complex planning and high-stakes reasoning. The low input and output prices support high-volume deployment.
Adopt it as a cost-conscious coding and content workhorse, especially when multimodal inputs or large documents are part of the workflow.
Assessed September 7, 2026
Editorial notes
Qwen3.6 35B A3B combines affordable pricing with strong coding, vision, video understanding, a 262K context window, and reliable function calling. Its general reasoning and agentic performance are less advanced than its implementation skills.
Rankings consider pricing, capabilities, benchmarks, and real-world applicability and are refreshed as new models launch. Feedback?
DFO Verdict
Qwen3.6 35B A3B combines affordable pricing with strong coding, vision, video understanding, a 262K context window, and reliable function calling. Its general reasoning and agentic performance are less advanced than its implementation skills.
Benchmark scores
Magenta = intelligence · Ink = technical/agentic · Cyan = content & long-context · Grey = community benchmarks. Data: Artificial Analysis, Hugging Face.
26.2 Intelligence Index·41.9 Coding Index·15.1 Agentic Index
How Qwen: Qwen3.6 35B A3B compares
Qwen: Qwen3.6 35B A3B ranks #117 of 435 AI models we track for overall intelligence, #95 of 208 for coding, #104 of 190 for agentic tasks. Its 262K-token context window is larger than 71% of the models we list. At $0.10 per million input tokens it is cheaper than 70% of comparable models.
Dark bar = input · light bar = output, scaled to the priciest peer.
1M tokens ≈ 8 full-length novels or ~2,500 pages of business documents in a single request.
Strongest on value. The pulled-in intelligence corner is the trade-off, and if the shape matters more than the price, this is your model.
Compare shapes side-by-side →Pricing
| Token Type | Cost per 1M tokens | Cost per 1K tokens |
|---|---|---|
| Input | $0.10 | $0.000100 |
| Output | $0.90 | $0.000900 |
What would Qwen: Qwen3.6 35B A3B cost your business?
Pick the job that looks most like yours, then fine-tune with the sliders. Estimates update live.
A website chatbot handling around 100 customer conversations a day, a few short messages each.
Full calculator with 756 models → Price Calculator
These numbers get smaller with the right architecture.
We route routine calls to cheap models and save Qwen: Qwen3.6 35B A3B for the hard ones. Most clients cut their estimate by 60-80%.
Talk to our teamAbout Qwen: Qwen3.6 35B A3B
Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of-experts architecture combining Gated..
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Frequently asked questions about Qwen: Qwen3.6 35B A3B
How much does Qwen: Qwen3.6 35B A3B cost?
Qwen: Qwen3.6 35B A3B costs $0.10 per million input tokens and $0.90 per million output tokens.
What is the context window of Qwen: Qwen3.6 35B A3B?
Qwen: Qwen3.6 35B A3B has a context window of 262,144 tokens (262K).
Is Qwen: Qwen3.6 35B A3B good for coding?
On our coding benchmark index, Qwen: Qwen3.6 35B A3B ranks #95 of 208 models, placing it in the broader range of the field for code generation and debugging.
What can Qwen: Qwen3.6 35B A3B do?
Qwen: Qwen3.6 35B A3B supports image/vision input, tool use, and function calling.
Who created Qwen: Qwen3.6 35B A3B?
Qwen: Qwen3.6 35B A3B is developed by Qwen and was released on April 27, 2026.
Data sourced from the OpenRouter API, Artificial Analysis, the Hugging Face Open LLM Leaderboard and our own internal testing. Scores are editorially curated by our team.
Last updated: September 7, 2026 8:38 pm