Qwen: Qwen3.5 397B A17B
Analysis Summary
Qwen3.5 397B A17B is a large multimodal model with a 262K token context window, text, image, and video input, and support for tool use and function calling. Its coding capability is a clear strength, making it relevant to software assistance, code review, structured generation, and technical content workflows. The low listed API prices improve its economics for repeated usage.
The model is less convincing for autonomous agents. Its agentic measurement is substantially weaker than its coding result, so workflows involving extended planning, tool sequencing, or unattended actions should use guardrails and explicit validation. The context window is also smaller than some competing long-context models, although 262K tokens remains sufficient for many codebases and document sets.
Adopt it as a cost-efficient coding and technical-content specialist. Route routine engineering assistance and multimodal extraction through it, but reserve complex agent orchestration and high-risk client actions for a model with stronger agentic reliability.
Assessed September 7, 2026
Editorial notes
Qwen3.5 397B A17B combines strong coding capability with very low pricing, vision, video input, and tool calling, while its weak agentic measurement limits suitability for autonomous multi-step workflows.
Rankings consider pricing, capabilities, benchmarks, and real-world applicability and are refreshed as new models launch. Feedback?
DFO Verdict
Qwen3.5 397B A17B combines strong coding capability with very low pricing, vision, video input, and tool calling, while its weak agentic measurement limits suitability for autonomous multi-step workflows.
How Qwen: Qwen3.5 397B A17B compares
Qwen: Qwen3.5 397B A17B ranks #127 of 438 AI models we track for overall intelligence, #79 of 210 for coding, #115 of 192 for agentic tasks. Its 262K-token context window is larger than 71% of the models we list. At $0.55 per million input tokens it is cheaper than 37% 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 | $0.55 | $0.000550 |
| Output | $3.50 | $0.003500 |
What would Qwen: Qwen3.5 397B A17B cost your business?
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A website chatbot handling around 100 customer conversations a day, a few short messages each.
Full calculator with 779 models → Price Calculator
These numbers get smaller with the right architecture.
We route routine calls to cheap models and save Qwen: Qwen3.5 397B A17B for the hard ones. Most clients cut their estimate by 60-80%.
Talk to our teamAbout Qwen: Qwen3.5 397B A17B
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers..
Frequently asked questions about Qwen: Qwen3.5 397B A17B
How much does Qwen: Qwen3.5 397B A17B cost?
Qwen: Qwen3.5 397B A17B costs $0.55 per million input tokens and $3.50 per million output tokens.
What is the context window of Qwen: Qwen3.5 397B A17B?
Qwen: Qwen3.5 397B A17B has a context window of 262,144 tokens (262K).
Is Qwen: Qwen3.5 397B A17B good for coding?
On our coding benchmark index, Qwen: Qwen3.5 397B A17B ranks #79 of 210 models, placing it in the broader range of the field for code generation and debugging.
What can Qwen: Qwen3.5 397B A17B do?
Qwen: Qwen3.5 397B A17B supports image/vision input, tool use, and function calling.
Who created Qwen: Qwen3.5 397B A17B?
Qwen: Qwen3.5 397B A17B is developed by Qwen and was released on February 16, 2026.