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Qwen3.5 397B-A17B

Qwen3.5 397B-A17B via Vultr

Specifications

Context Window

262,144 tokens

Release Date

2026-02-15

Capabilities

AttachmentsReasoningTool callingStructured outputTemperatureImage inputVideo inputAudio input

Availability

Open Weights

Model Overview

Vultr is a cloud infrastructure provider offering AI model inference through their serverless GPU platform, providing simple and affordable access to popular models.

Qwen3.5 397B-A17B is a qwen-family model by Vultr with a 262k token context window and up to 66k output tokens. It is priced at $0.3000/1M input tokens and $2.00/1M output tokens.

Key capabilities include: attachments, reasoning, tool calling, structured output, temperature, image input, video input, audio input. It supports advanced reasoning for complex multi-step tasks. It can call external tools and functions for agentic workflows.

Details

ProviderVultr
Model IDQwen/Qwen3.5-397B-A17B
Familyqwen
Release Date2026-02-15
Last Updated2026-02-15
Knowledge CutoffN/A
Context Window262,144 tokens
Max Output65,536 tokens
Input Cost / 1M$0.3000
Output Cost / 1M$2.00

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Frequently Asked Questions

How much does Qwen3.5 397B-A17B cost to use?

Qwen3.5 397B-A17B is priced at $0.3000/1M input tokens and $2.00/1M output tokens. Use the cost estimator on this page to calculate your expected spend based on your usage pattern.

What is a token and how does it relate to pricing?

A token is a chunk of text — roughly ¾ of a word in English. For example, "chatbot" is two tokens. LLM API pricing is based on the number of tokens you send (input) and receive (output). Input tokens include your prompts, uploaded documents, and images, while output tokens are the model's generated responses.

Why are input and output tokens priced differently?

LLM providers charge separately for input and output tokens. Output tokens are typically more expensive because generating each token requires more compute — the model must run a full forward pass for every token it produces, while input tokens are processed in parallel.

What is the context window of Qwen3.5 397B-A17B?

Qwen3.5 397B-A17B supports a context window of 262,144 tokens. This is the maximum number of tokens (input + output combined) the model can process in a single request. Larger context windows let you send longer documents or maintain longer conversation histories.

How accurate is this cost estimation?

This tool provides a ballpark estimate based on per-token pricing. Actual costs may differ due to prompt caching, batched API calls, volume discounts, reasoning token overhead, and provider-specific billing rules. Use it for budgeting and comparison, not as an invoice prediction.

How does Qwen3.5 397B-A17B pricing compare to other models?

You can compare Qwen3.5 397B-A17B with other models on our LLM API pricing calculator. Use the cost estimator to see side-by-side cost breakdowns across different providers and models to find the best fit for your budget and requirements.

What factors affect my total API cost?

Your total cost depends on several factors: the number of API calls you make, the length of your prompts (input tokens), the length of generated responses (output tokens), whether you use features like image or document uploads (which add input tokens), and any provider-specific charges for caching or batch processing.

How can my team use Qwen3.5 397B-A17B via API?

You can connect your own Vultr API key and give your entire team access to Qwen3.5 397B-A17B through TypingMind Teams. It lets you build a unified AI workspace where team members can use Qwen3.5 397B-A17B and other models — without needing their own API keys. You stay in control of usage limits, costs, and permissions, all from a single dashboard.

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