Qwen3.5-9B
Qwen3.5-9B is a 9‑billion‑parameter multimodal language model from Qwen that supports long-context reasoning over text and images.
What is Qwen3.5-9B?
Qwen3.5-9B is a 9B-parameter multimodal foundation model from Qwen that accepts both text and visual inputs. It is mainly used for general-purpose chat and reasoning tasks where developers want a capable but lightweight model that can run with lower latency and cost than larger LLMs. It is also applied to coding assistance, document understanding, and vision-language applications such as describing or analyzing images. Qwen3.5-9B belongs to the Qwen3.5 model family, an evolution of earlier Qwen and Qwen3-generation models that improve multimodal performance and efficiency.
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 | $0.10 | $0.15 | — | — | — | — |
Try this model
Test Qwen3.5-9B 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/qwen3-5-9b",
messages=[
{
"role": "user",
"content": "Describe this image in one sentence."
}
],
)
print(response.to_json())
{
"model": "qwen/qwen3-5-9b",
"messages": [
{
"role": "user",
"content": "Describe this image in one sentence."
}
]
}
5 Core Capabilities
-
Conversational Chat
Engages in multi-turn dialogue, follows instructions, and maintains context to answer questions and assist with varied tasks.
-
Code Assistance
Generates and explains code snippets, debugs simple issues, and helps reason about programming concepts across common languages.
-
Text Translation
Translates text between multiple languages while aiming to preserve meaning, tone, and key domain-specific terminology.
-
Image Understanding
Interprets input images, identifying objects and basic visual context to support downstream reasoning or description tasks.
-
Visual Text Extraction
Extracts readable text from images or screenshots, enabling downstream search, analysis, or transformation of visual documents.
6 Most Valuable Use Cases
- Customer Support Chatbot
- Invoice Data Extraction
- Legal Document Search
- Regulation Change Monitoring
- E-commerce Product Assistant
- Code Generation Helper
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Unified AI Routing
Dynamically route each request to the optimal model across providers based on cost, speed, or quality—without changing your code or client integration.
One endpoint, any model -
Cost-Aware Orchestration
Control spend with per-request cost caps, smart model downgrades, and transparent pricing telemetry so you can optimize budgets without sacrificing performance.
Ship fast, spend less -
Automatic Smart Fallbacks
Avoid downtime and flaky providers with configurable failover policies that instantly retry on alternative models or regions when errors, timeouts, or rate limits occur.
Resilience by default -
Full-Stack Observability
Trace every token across models, providers, and teams with centralized logs, metrics, and structured events wired for debugging, analytics, and cost governance.
See every request -
Task-Level Abstractions
Define tasks like chat, tools, RAG, or workflows once and let LLM.API handle prompts, parameters, and providers so product teams can iterate safely and faster.
Model-agnostic tasks -
High-Throughput Batch APIs
Process millions of inferences with parallelized batching, automatic throttling, and retry semantics to maximize throughput while staying within provider quotas and budgets.
Scale without throttling
When to Use — When NOT to Use
Use it if...
- You need a small, general-purpose model for everyday chat and assistance tasks.
- You need cost-efficient inference for high-volume requests with moderate reasoning complexity.
- Your use case involves basic code generation, debugging, or small utility scripts.
- Your use case involves lightweight content creation like short emails, summaries, or descriptions.
- You need a compact model suitable for latency-sensitive applications on modest hardware.
- Your use case involves multilingual understanding without requiring top-tier translation quality.
- You need a model for prototyping AI features before scaling to larger systems.
Avoid if...
- You need state-of-the-art performance on complex reasoning, planning, or mathematical proofs.
- Your workload requires handling extremely long context windows with robust recall and reasoning.
- You need best-in-class coding assistance for large projects, refactors, or multi-file reasoning.
- Your workload requires highly reliable domain expertise in law, medicine, or finance.
- You need the strongest safety, alignment, and nuanced instruction-following available across models.
- Your workload requires rich multimodal capabilities like advanced image understanding or generation.
- You need cutting-edge performance in benchmark-driven research or competitive leaderboard scenarios.
Frequently Asked Questions
-
What is Qwen3.5-9B?
Qwen3.5-9B is a 9B-parameter Qwen language model optimized for fast, general-purpose text generation and reasoning through the LLM.API gateway.
-
What is the context window of Qwen3.5-9B?
Qwen3.5-9B supports up to a 32K token context window for combined input and output via LLM.API.
-
What is Qwen3.5-9B best suited for?
Qwen3.5-9B is best for lightweight assistants, code helpers, and analytical tasks where you need strong quality without the cost of very large models.
-
How is Qwen3.5-9B priced on LLM.API?
Qwen3.5-9B usage is metered per-token for input and output; check your LLM.API pricing page for the exact current rates.
-
How fast is Qwen3.5-9B in terms of latency?
Qwen3.5-9B generally returns first tokens quickly and is suitable for interactive applications, but actual latency depends on load and request size.
-
What modalities does Qwen3.5-9B support on LLM.API?
On LLM.API, Qwen3.5-9B is available as a text-only model, accepting and producing UTF-8 text tokens.
-
How do I call Qwen3.5-9B through LLM.API?
Specify the model name "Qwen3.5-9B" in your LLM.API chat or completion request, passing messages and parameters according to the unified API schema.
-
How does Qwen3.5-9B compare to larger Qwen models?
Compared to larger Qwen models, Qwen3.5-9B is cheaper and faster but may underperform on very complex reasoning or long-context tasks.
-
What are key limitations of Qwen3.5-9B?
Qwen3.5-9B can hallucinate facts, struggle with highly specialized domains, and may miss subtle long-range dependencies near its context length limit.
-
Can I fine-tune or customize Qwen3.5-9B via LLM.API?
Direct fine-tuning is not exposed; instead, use system prompts, exemplars, and tools to steer Qwen3.5-9B’s behavior through LLM.API.
COMPARE
Competitive Models
-
Qwen3 VL 30B A3B Thinking
Qwen3 VL 30B A3B Thinking is a large multimodal Qwen model with around 30 billion parameters, designed for vision-language reasoning with extended “thinking” capabilities. It is notable for combining image understanding with advanced step-by-step analytical generation.
-
Qwen3 VL 235B A22B Thinking
Qwen3 VL 235B A22B Thinking is a large Qwen multimodal model that can process both images and text with enhanced chain-of-thought style reasoning. It is configured for higher-quality, slower “thinking” outputs rather than fast responses.
-
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.
Get one key to every model
Swap your API key. Keep your code.