Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design with early fusion of multimodal tokens, allowing the model to process and reason across text and images within the same context.
Modalities
Input Price
$0.04per 1M
Output Price
$0.15per 1M
Context
262K
Weekly Tokens
48.7B
Released
Mar 10, 2026
Create an API key from your OpenRouter dashboard and set it as an environment variable:
Use qwen/qwen3.5-9b with the OpenRouter API:
OpenRouter supports reasoning-enabled models that can show their step-by-step thinking process. Use the reasoning parameter in your request to enable reasoning, and access the reasoning_details array in the response to see the model's internal reasoning before the final answer. When continuing a conversation, preserve the complete reasoning_details when passing messages back to the model so it can continue reasoning from where it left off. Learn more about reasoning tokens.
In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.
For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.
Add "stream": true to your request body to receive responses as server-sent events:
https://openrouter.ai/api/v1/chat/completionsBearer $OPENROUTER_API_KEYapplication/jsonoptional — your site URL, for rankingsoptional — your site name, for rankingsqwen/qwen3.5-9b| Name | Type | Default | Description |
|---|---|---|---|
reasoning | map | — | Controls reasoning behavior for models that support thinking tokens, including whether reasoning is enabled, the reasoning effort, maximum reasoning tokens, and whether reasoning is excluded from the response. |
max_tokens | integer | — | This sets the upper limit for the number of tokens the model can generate in response. |
temperature | float | 1 | This setting influences the variety in the model's responses. |
top_p | float | 1 | This setting limits the model's choices to a percentage of likely tokens: only the top tokens whose probabilities add up to P. |
stop | array | — | Stop generation immediately if the model encounter any token specified in the stop array. |
frequency_penalty | float | 0 | This setting aims to control the repetition of tokens based on how often they appear in the input. |
presence_penalty | float | 0 | Adjusts how often the model repeats specific tokens already used in the input. |
repetition_penalty | float | 1 | Helps to reduce the repetition of tokens from the input. |
top_k | integer | 0 | This limits the model's choice of tokens at each step, making it choose from a smaller set. |
seed | integer | — | If specified, the inferencing will sample deterministically, such that repeated requests with the same seed and parameters should return the same result. |
min_p | float | 0 | Represents the minimum probability for a token to be considered, relative to the probability of the most likely token. |
response_format | map | — | Forces the model to produce specific output format. |
logit_bias | map | — | Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. |
tools | array | — | Tool calling parameter, following OpenAI's tool calling request shape. |
tool_choice | string or object | — | Controls which (if any) tool is called by the model. |