Qwen3.5-Flash
Qwen3.5-Flash is a hosted, production-oriented large language model from Qwen, optimized for fast, efficient text and vision-language generation.
What is Qwen3.5-Flash?
Qwen3.5-Flash is a Qwen-provided hosted version of the Qwen3.5 series, based on the Qwen3.5-35B-A3B model with additional production features. It is mainly used for high-throughput text generation tasks such as chat applications, content creation, and assistants that benefit from fast inference. It also supports vision-language use cases like answering questions about images and multimodal workflows, enabled by its long context window and optimized architecture. It belongs to the Qwen3.5 family of large language models, which extends earlier Qwen3 and Qwen2.5 generations.
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 | ~$0.05 | ~$0.10 | — | ~220ms | ~80 tps | 99.9% |
| OpenAI | ~$0.10 | ~$0.20 | — | ~180ms | ~90 tps | 99.06% |
| Anthropic | ~$0.12 | ~$0.24 | — | ~190ms | ~70 tps | 99.28% |
| AWS Bedrock | ~$0.11 | ~$0.22 | — | ~210ms | ~60 tps | 100.00% |
Try this model
Test Qwen3.5-Flash 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-flash",
messages=[
{
"role": "user",
"content": "Describe this image in one sentence."
}
],
)
print(response.to_json())
{
"model": "qwen/qwen3-5-flash",
"messages": [
{
"role": "user",
"content": "Describe this image in one sentence."
}
]
}
5 Core Capabilities
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Conversational Chat
Engages in multi-turn, context-aware dialogues, answering questions, following instructions, and adapting tone for various assistant-style applications.
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Image Understanding
Interprets images to identify objects, scenes, text, and visual relationships, supporting tasks like description, Q&A, and basic analysis.
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Multilingual Translation
Translates between multiple languages while preserving meaning and context, supporting cross-lingual communication and content localization tasks.
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Code and Tools
Understands and generates code snippets, reasoning about APIs and tool usage to support software development and automation workflows.
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Text Extraction
Reads and extracts textual information from visually presented content, enabling downstream processing, summarization, and semantic understanding.
6 Most Valuable Use Cases
- High-speed Chatbot
- Code Assistance
- Content Drafting
- Text Summarization
- Language Translation
- Data Extraction
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Unified AI Routing
Automatically select the best model per request based on latency, cost, and quality. One stable API, limitless providers and versions behind it.
One endpoint, every model -
Cost-Aware Orchestration
Blend premium and budget models with policy-based routing and caps. Optimize spend automatically without rewriting application logic or juggling provider billing.
Ship faster, spend less -
Resilient Fallbacks
Define multi-provider fallback chains that trigger instantly on errors, rate limits, or timeouts. Keep production workloads up even when individual APIs fail.
Designed for zero downtime -
Full-Stack Observability
Trace every request across models and providers with metrics, logs, and structured events. Debug latency, errors, and quality issues from a single pane.
See every token and hop -
Task-Level Abstractions
Call high-level tasks like chat, generate, extract, or classify instead of wiring raw prompts per model. Swap providers without touching application code.
Code to intent, not models -
High-Throughput Batching
Submit thousands of operations in a single call with automatic chunking, retries, and concurrency control. Maximize throughput for analytics, evaluations, and backfills.
Scale jobs, not boilerplate
When to Use — When NOT to Use
Use it if...
- You need a very low-cost model for high-volume chat or API traffic.
- You need fast responses for lightweight question-answering, summaries, or simple classifications.
- You need a small assistant to power basic product support chats or FAQs.
- Your use case involves rapid prototyping where latency matters more than perfect reasoning.
- Your use case involves simple code snippets, boilerplate generation, or minor code edits.
- You need a fallback or cascading model before escalating to slower, more capable LLMs.
- Your use case involves short, transactional prompts rather than long multi-step conversations.
Avoid if...
- You need state-of-the-art reasoning quality for complex, multi-step or ambiguous problems.
- Your workload requires handling very long documents or deeply cross-referencing large contexts.
- You need highly reliable code generation for critical systems or complex software architectures.
- Your workload requires nuanced domain expertise in specialized legal, medical, or scientific tasks.
- You need high factual accuracy for research-grade analysis or important business decisions.
- Your workload requires advanced tool use, multi-agent orchestration, or complex planning chains.
- You need top-tier creative writing, narrative consistency, or stylistically rich long-form content.
Frequently Asked Questions
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What is Qwen3.5-Flash?
Qwen3.5-Flash is a lightweight, fast Qwen model optimized for low-latency text generation and tool-oriented applications via LLM.API.
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What is Qwen3.5-Flash best suited for?
Qwen3.5-Flash is best for high-throughput chatbots, rapid autocomplete, and inexpensive bulk processing where speed matters more than peak reasoning quality.
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What is the context window of Qwen3.5-Flash?
Qwen3.5-Flash supports a context window up to 32,768 tokens for prompts plus generated output combined.
-
How fast is Qwen3.5-Flash on LLM.API?
Qwen3.5-Flash is tuned for low latency, typically returning first tokens significantly faster than heavier reasoning-focused models of similar generation quality.
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What modalities does Qwen3.5-Flash support on LLM.API?
On LLM.API, Qwen3.5-Flash supports text input and text output; image or audio inputs are not supported for this model.
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How is Qwen3.5-Flash priced on LLM.API?
Qwen3.5-Flash uses LLM.API’s unified per-token pricing layer; its exact input and output rates are shown in the LLM.API pricing dashboard.
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How do I call Qwen3.5-Flash through the LLM.API?
Set the model field to "Qwen3.5-Flash" in your LLM.API completion or chat endpoint request, keeping the rest of the API usage unchanged.
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How does Qwen3.5-Flash compare to larger Qwen or reasoning models?
Compared to larger or reasoning-oriented models, Qwen3.5-Flash trades some depth and accuracy for much lower latency and cost.
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Are there any notable limitations of Qwen3.5-Flash?
Qwen3.5-Flash can be weaker on complex reasoning, long multi-step planning, and highly specialized domain tasks compared to larger Qwen variants.
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Can Qwen3.5-Flash handle long-running or streaming conversations?
Yes, but for very long conversations you should periodically summarize history to stay within the 32K token context window.
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-
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-
Qwen3 VL 30B A3B Instruct
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