Qwen3.5 397B A17B
Qwen3.5 397B A17B is a large-scale language model from Qwen with roughly 397 billion parameters, designed for advanced reasoning and multilingual understanding.
What is Qwen3.5 397B A17B?
Qwen3.5 397B A17B is a 397-billion-parameter Qwen language model optimized for powerful, general-purpose AI assistance. It is used for complex text generation and understanding tasks such as drafting, analysis, and conversation. It is also applied in demanding reasoning, coding, and knowledge-intensive applications where very large models are preferred. It belongs to the Qwen (Qwen2/Qwen2.5/Qwen3.x) family of large language models developed by Qwen.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).
| Provider | Input | Output | Cache read /M | Latency | Throughput | Uptime |
|---|---|---|---|---|---|---|
| Together AI | $0.60 | $3.60 | — | — | — | — |
Try this model
Test Qwen3.5 397B A17B right here — free to start.
Suggestions for your first prompt
Code snippet
Call the model through the OpenAI-compatible API.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://inference.example.com/v1"
)
response = client.chat.completions.create(
model="qwen/qwen3-5-397b-a17b",
messages=[
{
"role": "user",
"content": "Describe this image in one sentence."
}
],
)
print(response.to_json())
{
"model": "qwen/qwen3-5-397b-a17b",
"messages": [
{
"role": "user",
"content": "Describe this image in one sentence."
}
]
}
5 Core Capabilities
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Advanced Chat
Engages in multi-turn conversations, follows complex instructions, and maintains context for reasoning, coding help, and detailed explanations.
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Image Understanding
Interprets uploaded images to identify objects, text, layouts, and visual relationships, supporting description, reasoning, and grounded question answering.
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Document OCR
Extracts and structures text from scanned documents, screenshots, and complex layouts, enabling downstream analysis, search, and transformation tasks.
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Code and Tools
Supports tool-using workflows, including calling external APIs, running code-like reasoning, and monitoring iterative steps for complex tasks.
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Multilingual Translation
Translates between many languages while preserving meaning, tone, and formatting, useful for cross-lingual communication and content localization.
6 Most Valuable Use Cases
- Customer Support Chatbots
- Financial Document Analysis
- Legal Contract Review
- Regulatory Compliance Monitoring
- E-commerce Product Recommendations
- Code Generation and Debugging
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent Model Routing
Automatically route each request to the optimal model across providers based on latency, price, and performance—no client changes required.
One endpoint, many models -
Cost-Aware Orchestration
Control spend with per-route pricing rules, automatic downgrades, and usage caps while keeping SLAs and quality intact.
Cut costs, keep quality -
Resilient Fallback Logic
Define multi-step failover chains so requests seamlessly retry on backup models or providers when outages or timeouts occur.
Never go dark -
Full-Stack Observability
Get end-to-end traces, latency histograms, provider error rates, and payload logs in one place to debug and optimize quickly.
See every token -
Task-Aware Abstractions
Use task-level APIs (chat, tools, embeddings, rerank, image) that stay stable even as underlying models and vendors change.
Code to tasks, not vendors -
High-Throughput Batch
Send massive batch jobs through a single endpoint with automatic sharding, rate limiting, and retries across providers.
Scale jobs, not scripts
When to Use — When NOT to Use
Use it if...
- You need a very large frontier model for complex, multi-step reasoning tasks.
- You need strong general-purpose performance across coding, math, writing, and analysis.
- Your use case involves difficult enterprise workloads where raw model capability dominates cost.
- Your use case involves evaluating frontier-scale models for research, benchmarking, or comparisons.
- You need robust performance on diverse multilingual inputs but will read outputs in English.
- You need a powerful assistant to explore and prototype advanced agentic or tool-use workflows.
Avoid if...
- You need ultra-low inference cost for millions of short, simple requests daily.
- Your workload requires strict real-time latency budgets on resource-constrained hardware.
- You need an extremely lightweight model deployable on edge or mobile devices.
- Your workload requires guaranteed on-device inference without large GPU or TPU resources.
- You need a fully open-weights, easily self-hostable small model for customization.
- Your workload requires predictable throughput on limited infrastructure rather than peak model power.
Frequently Asked Questions
-
What is Qwen3.5 397B A17B?
Qwen3.5 397B A17B is a large-scale Qwen language model accessible through LLM.API, optimized for complex reasoning, code, and high-quality text generation.
-
What is Qwen3.5 397B A17B best suited for?
It excels at multi-step reasoning, advanced coding assistance, data analysis, and generating long-form, instruction-following content with strong coherence.
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How is Qwen3.5 397B A17B priced on LLM.API?
Pricing is usage-based per input and output token; check your LLM.API dashboard or pricing docs for the latest specific rates.
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What context window does Qwen3.5 397B A17B support?
Qwen3.5 397B A17B supports a long context window suitable for extended conversations and documents; refer to LLM.API docs for the current token limit.
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How fast is Qwen3.5 397B A17B in terms of latency?
As a very large model it has higher latency than smaller Qwen variants, but LLM.API streams tokens progressively to improve perceived responsiveness.
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What modalities does Qwen3.5 397B A17B support via LLM.API?
Through LLM.API it supports text input and output; check the model capabilities section to confirm any additional modalities like images if enabled.
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How do I call Qwen3.5 397B A17B through LLM.API?
Use the standard chat or completion endpoint, specifying the model name "qwen3.5-397b-a17b" (or listed identifier) in your LLM.API request payload.
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How does Qwen3.5 397B A17B compare to smaller Qwen models?
It generally offers stronger reasoning and generation quality than smaller Qwen models, at higher cost and latency per request.
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What are the main limitations of Qwen3.5 397B A17B?
It may hallucinate incorrect facts, struggle with real-time or proprietary data, and be too slow or expensive for latency-critical, high-throughput workloads.
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Can I use Qwen3.5 397B A17B for batch or server-side workloads?
Yes, you can run batch and backend workloads via LLM.API, but should account for its higher token cost and compute latency in your design.
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-
Qwen3 VL 8B Instruct
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-
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