Qwen3.5 397B A17B
Qwen3.5 397B A17B is a large mixture-of-experts language model from Alibaba's Qwen series, with roughly 397B total parameters and about 17B active per token.
API Pricing
Cheapest input on Deep Infra · cheapest output on Deep Infra| Provider | Input / 1M | Output / 1M | Cached / 1M |
|---|---|---|---|
| $0.450 | $3.00 | $0.220 | |
| $0.500 | $3.60 | $0.300 | |
| $0.600 | $3.60 | - | |
| $0.600 | $3.60 | - | |
| $0.600 | $3.60 | $0.120 | |
| $0.600 | $3.60 | - | |
| $0.600 | $3.60 | - | |
| $0.676 | $4.06 | - | |
| $0.710 | $4.25 | - | |
| $0.830 | $5.55 | - |
Prices updated daily. Last check: Nov 6, 2026
Compare API pricing for every Qwen model →Qwen3.5 397B A17B pricing by provider
Input, output, and batch rates, plus alternatives, for one provider at a time.
Performance & Benchmarks
Source: Artificial Analysis →Benchmarks measured Nov 2026. Scores are independent evaluations, not vendor-reported.
Model Details
General
- Creator
- Alibaba
- Modalities
- Text
Capabilities
- Open Source
- Yes
Strengths & Limitations
Strengths
- Mixture-of-experts design activates roughly 17B parameters per token out of ~397B total, reducing per-token compute relative to a dense model of comparable size
- Measured output throughput of about 95 tokens per second in Artificial Analysis testing, which is competitive for a model of this parameter class
- Part of Alibaba's Qwen series, which is widely served across multiple third-party inference providers, giving buyers alternatives on price and region
- Large total parameter count provides substantial capacity for knowledge-heavy and reasoning-heavy prompts
- Qwen models have historically had strong Chinese-English bilingual coverage, making the family a common choice for multilingual deployments
- Sparse activation makes self-hosting more tractable on compute terms, though total memory for all experts remains high
Limitations
- We do not have a confirmed context window figure for this variant, so effective input limits must be checked per provider
- Modality support (image or audio input) is not tracked in our data for this model
- Measured time to first token of roughly 1,375 ms is not especially fast; latency-sensitive interactive apps may prefer a smaller model
- No verified public benchmark scores in our database for this specific variant, making direct quality comparisons difficult
- Total parameter footprint of ~397B means all experts must be resident in memory for self-hosting, which is demanding even though only ~17B activate per token
- Throughput and pricing vary widely between the independent providers that host Qwen models, so performance is host-dependent
Key Features
About Qwen3.5 397B A17B
Common Use Cases
Qwen3.5 397B A17B suits workloads that benefit from a high-capacity model but where the compute cost of a dense model at similar scale would be prohibitive: general-purpose assistants, code generation and refactoring, document analysis, and multi-step reasoning tasks. Its sparse activation profile makes it a reasonable fit for batch and throughput-oriented pipelines, where the roughly 95 tokens per second measured output rate matters more than the ~1.4 second time to first token. For highly interactive, low-latency chat interfaces, a smaller Qwen variant may be a better trade. Teams already standardized on the Qwen ecosystem — or those needing strong Chinese-language handling alongside English — often evaluate this size class against comparable mixture-of-experts models from other labs on both cost per million tokens and measured throughput.
Frequently Asked Questions
How much does Qwen3.5 397B A17B cost to use?
Pricing depends on which inference provider you use and how the endpoint is billed — per-input-token, per-output-token, and any serverless versus dedicated arrangement all differ between hosts. Because Qwen models are typically served by several independent providers, rates can vary substantially for the same model. See the pricing table on this page for the current figures from the providers we track.
What does "397B A17B" mean?
It describes a mixture-of-experts model: roughly 397 billion total parameters across all experts, with approximately 17 billion parameters activated for any single token. The practical effect is that inference compute per token is closer to a 17B-class model, while total memory requirements scale with the full 397B parameter count.
What is Qwen3.5 397B A17B best used for?
It is generally aimed at general-purpose assistant work, coding, document analysis, and multi-step reasoning where a large-capacity model is useful. Its throughput profile — around 95 output tokens per second with a time to first token near 1,375 ms in Artificial Analysis testing — leans toward batch and generation-heavy workloads rather than very low-latency interactive use.
What context window does it support?
We do not have a confirmed context window for this specific variant in our database. Because context limits for Qwen models often differ between the model's native capability and what individual hosts expose, check the documentation of the provider you plan to use.
How does it compare to smaller Qwen models?
Within the Qwen3.5 generation, this variant sits at a much larger total parameter count than the compact models, which typically translates into more capacity for knowledge-heavy and reasoning tasks. Smaller siblings generally offer lower latency and lower per-token cost, so the trade-off is capacity versus responsiveness and price. Compare the specific endpoints in the pricing table to see how each option is served.