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Qwen3.6 35B A3B

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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.

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Providers Comparison

Speed

Output tokens per second · Higher is better

246
97
92
91
90
80
60
27
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

End-to-End Response Time

Seconds · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

Price

USD per 1M tokens (blended) · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

About the model

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.

Model capabilities

5 Core Capabilities

  • Conversational AI

    Engages in multi-turn dialogue, following instructions, maintaining context, and generating coherent, helpful responses for diverse conversational scenarios.

  • Code Generation

    Writes and edits source code, explains programming concepts, and assists with debugging across common languages and software development tasks.

  • Image Understanding

    Interprets uploaded images, identifying objects, text, and visual relationships, and answering questions grounded in the visual content.

  • Text Translation

    Translates between multiple languages while aiming to preserve meaning, tone, and domain-specific terminology in the target text.

  • Visual Text Extraction

    Reads and extracts textual information from images, such as documents, screenshots, and signs, enabling downstream analysis and processing.

Use cases

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

Transparent pricing

Cost Comparison

LLM API offers the lowest cost and highest performance for Qwen3.6 35B–class models.

Provider Region Latency Throughput Uptime Input ($/1M) Output ($/1M) Context
LLM API BEST Global 110ms 180 tps 99.99% $0.20 $0.20 256K
Qwen Global ~180ms ~120 tps 99.9% ~$0.40 ~$0.40 ~128K
Aliyun APAC ~220ms ~90 tps 99.9% ~$0.45 ~$0.45 ~128K
Tencent Cloud APAC ~230ms ~80 tps 99.9% ~$0.50 ~$0.50 ~128K
Volcengine APAC ~210ms ~100 tps 99.9% ~$0.42 ~$0.42 ~128K

Performance benchmarks

Technical Specifications

Metric Qwen3.6 35B A3B Llama 3.1 70B Inference GPT-4.1 Mini
Avg Latency ~220ms ~280ms ~200ms
Context Window 128K 128K 128K
Input Price ($/1M) $0.30 $0.60 $0.15
Output Price ($/1M) $0.60 $0.90 $0.60
Max Output Tokens 8K 8K 8K
Throughput 120 tps 90 tps 150 tps
Uptime 99.9% 99.9% 99.9%

30-day usage via LLM API

420B
Prompt tokens processed (last 30 days)
75B
Completion tokens generated (last 30 days)
11.5M
API requests served (last 30 days)
310K
Unique users (last 30 days)
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Architecture & Integration

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

Decision guide

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.

FAQ

Frequently Asked Questions

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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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