Qwen3.6 35B A3B
Qwen3.6 35B A3B is an open-weight, multimodal Mixture-of-Experts model with 35 billion parameters (about 3 billion active per token), designed for long-context reasoning, coding, and vision-language tasks.
What is Qwen3.6 35B A3B?
Qwen3.6 35B A3B is a sparse MoE vision-language model from Qwen/Alibaba with a 262K-token context window and hybrid attention architecture. It is mainly used for agentic coding, long-context reasoning, and tool-using assistants that need efficient inference with strong intelligence, and it also supports multimodal applications involving text, images, and video. The model is further applied in retrieval-augmented generation, software agents, and benchmarking research where an open-weight, high-capability model is required. It belongs to the Qwen3.6 family and succeeds earlier Qwen 3.x generations such as Qwen3.5 35B A3B.
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 |
|---|---|---|---|---|---|---|
| Qwen | ~$0.40 | ~$0.40 | — | ~180ms | ~120 tps | 99.9% |
| Aliyun | ~$0.45 | ~$0.45 | — | ~220ms | ~90 tps | 99.9% |
| Tencent Cloud | ~$0.50 | ~$0.50 | — | ~230ms | ~80 tps | 99.9% |
| Volcengine | ~$0.42 | ~$0.42 | — | ~210ms | ~100 tps | 99.9% |
Try this model
Test Qwen3.6 35B A3B 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/qwen3-6-35b-a3b",
messages=[
{
"role": "user",
"content": "Describe this image in one sentence."
}
],
)
print(response.to_json())
{
"model": "qwen/qwen3-6-35b-a3b",
"messages": [
{
"role": "user",
"content": "Describe this image in one sentence."
}
]
}
5 Core Capabilities
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Conversational AI
Engages in multi-turn dialogue, following instructions, maintaining context, and generating coherent, helpful responses for diverse conversational scenarios.
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Code Generation
Writes and edits source code, explains programming concepts, and assists with debugging across common languages and software development tasks.
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Image Understanding
Interprets uploaded images, identifying objects, text, and visual relationships, and answering questions grounded in the visual content.
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Text Translation
Translates between multiple languages while aiming to preserve meaning, tone, and domain-specific terminology in the target text.
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Visual Text Extraction
Reads and extracts textual information from images, such as documents, screenshots, and signs, enabling downstream analysis and processing.
6 Most Valuable Use Cases
- Customer Support Chatbots
- Financial Document Analysis
- Legal Contract Review
- Regulatory Change Monitoring
- E-commerce Product Assistance
- Code Generation and Debugging
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent AI Routing
Automatically route each request to the best-fit model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, any model -
Cost-Aware Orchestration
Define cost ceilings and smart tiering rules so LLM.API prefers cheaper models when quality is equivalent, keeping your AI bill predictable and under control.
Optimize spend by default -
Resilient Fallback Flows
Configure automatic fallbacks to alternate models or providers on errors, timeouts, or rate limits to harden your AI stack against provider outages.
No single point of failure -
End-to-End Observability
Get centralized tracing, metrics, and structured logs across every provider so you can debug prompts, compare models, and tune performance from a single dashboard.
See every token, everywhere -
Task-Level Abstractions
Describe what you want—chat, extraction, search, tools—and let LLM.API pick and configure the right model, prompts, and parameters for each task type.
Think tasks, not models -
High-Throughput Batch APIs
Send large batches of requests through a single call with built-in concurrency control, retries, and aggregation to maximize throughput and minimize coordination logic.
Scale jobs, shrink code
When to Use — When NOT to Use
Use it if...
- You need a strong general-purpose LLM for English and Chinese coding and chat.
- You need good reasoning performance without paying for the very largest frontier models.
- Your use case involves building multilingual chatbots or agents targeting Asian-language users.
- Your use case involves running mid-size models on-prem or in VPC for compliance.
- You need a capable 35B model for code completion, refactoring, and explanation tasks.
- Your use case involves offline or edge deployment where 70B+ models are impractical.
- You need balanced performance on reasoning, coding, and general knowledge without extreme hardware costs.
Avoid if...
- You need state-of-the-art performance on the hardest reasoning or competition-level math benchmarks.
- Your workload requires minimal latency at massive scale, favoring much smaller distilled models.
- You need a fully proprietary, Western-hosted model with strong enterprise support guarantees.
- Your workload requires the longest possible context window for book-length or multi-day transcripts.
- You need cutting-edge multimodal capabilities like advanced image, video, or audio understanding.
- Your workload requires strict alignment and safety tooling comparable to major US cloud providers.
- You need guaranteed compliance with highly regulated jurisdictions that restrict certain foreign AI providers.
Frequently Asked Questions
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What is Qwen3.6 35B A3B?
Qwen3.6 35B A3B is a 35-billion-parameter Qwen language model optimized for strong reasoning and coding performance via LLM.API.
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What is Qwen3.6 35B A3B best suited for?
Qwen3.6 35B A3B is best for complex reasoning, code generation, tool-using agents, and high-quality general-purpose chat applications.
-
What context window does Qwen3.6 35B A3B support on LLM.API?
Qwen3.6 35B A3B supports a context window of up to 32K tokens via LLM.API.
-
What modalities does Qwen3.6 35B A3B support?
Qwen3.6 35B A3B is a text-only model on LLM.API, accepting and producing natural language and code.
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How is Qwen3.6 35B A3B priced on LLM.API?
Qwen3.6 35B A3B pricing is usage-based per input and output tokens; check your LLM.API dashboard or pricing page for current rates.
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How fast is Qwen3.6 35B A3B in terms of latency and throughput?
As a 35B model, Qwen3.6 35B A3B has higher latency than smaller models but streams tokens fast enough for interactive applications.
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How do I call Qwen3.6 35B A3B through LLM.API?
Use the LLM.API chat or completions endpoint and set the model field to "qwen3.6-35b-a3b" in your request body.
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How does Qwen3.6 35B A3B compare to smaller Qwen models?
Compared to smaller Qwen models, Qwen3.6 35B A3B generally offers better reasoning and code quality at the cost of higher compute and latency.
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Does Qwen3.6 35B A3B support function calling or tool use via LLM.API?
Yes, Qwen3.6 35B A3B can be used with LLM.API's tool or function-calling interfaces for structured outputs and agents.
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What are the main limitations of Qwen3.6 35B A3B?
Qwen3.6 35B A3B can hallucinate, lacks real-time knowledge, and may struggle with inputs exceeding its context or requiring domain-expert validation.
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-
Qwen3 Coder Plus
Qwen3 Coder Plus is Qwen’s premium, API-accessible coding model with a 1M‑token context window, optimized for complex, agentic software engineering tasks. It offers higher capability and quality than the base Qwen3-Coder variants for large-scale code generation, refactoring, and debugging.
-
Qwen3 Max
Qwen3 Max is Qwen’s flagship trillion-parameter large language model, offered as a high-end proprietary API model. It is designed to deliver state-of-the-art performance across reasoning, coding, and multilingual tasks within the Qwen3 family.
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