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Qwen3.5-35B-A3BOPEN
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency.
Prices update automatically — checked daily against provider list prices.
- $0.5 per 1M tokens, assuming 3 input for 1 output the convention used by Artificial Analysis
- cheaper than 39% of the 191 open-weight models tracked here
- 27 of 51 Alibaba models tracked here, from cheapest
Benchmarks & performance
Independent benchmark scores for Qwen3.5-35B-A3B, measured by Artificial Analysis. Higher is better. Measurement mode: Reasoning.
For the same budget, Qwen3.5-35B-A3B delivers 6.9 times more capability. Open weights also mean you can self-host it and pay nothing per token.
Models at this level cost $1 per 1M tokens (median of 14) — this one costs $0.5.
Cheaper and better on this index: GLM 5.3 Flash · Gemini 3.7 Flash (batch) · GPT-5.6 Luna and 17 more
About this model
Qwen3.5-35B-A3B is an open-weight AI model by Alibaba. You can download and self-host it for free; the prices below are hosted-API list prices, tracked daily, for when you prefer convenience over self-hosting.
Frequently asked questions
What is Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B is an AI language model from Alibaba. It is open-weight: you can download it and run it on your own hardware, for free. It scores 29.9 on the Artificial Analysis intelligence index.
Is Qwen3.5-35B-A3B free?
The weights are free and open — you can self-host Qwen3.5-35B-A3B and pay nothing per token. If you prefer a hosted API, list prices are $0.25 per million input tokens and $1.25 per million output tokens.
What is Qwen3.5-35B-A3B good at?
Independent benchmarks from Artificial Analysis give it GPQA 84.5%, Humanity's Last Exam 21%, Long Context Reasoning 68.3%, SciCode 37.7%, IFBench 72.5%, τ²-Bench 89.2%, Terminal-Bench Hard 26.5%.
Can I self-host Qwen3.5-35B-A3B?
Yes. Qwen3.5-35B-A3B has open weights, so you can download it and run it on your own GPU or server with tools like Ollama, vLLM or llama.cpp — with no per-token cost.