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
In / Out Price
$0.10 / $0.15per 1M
Context
262K
Released
Mar 10, 2026
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).
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows where this model lands among all models on OpenRouter.
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.
Token volume and request traffic to this model over time.
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
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.
Qwen3.5-9B costs $0.10/M input tokens and $0.15/M output tokens.
Qwen3.5-9B has a 262,144 token context window. It supports up to 262,144 completion tokens.
Yes. Qwen3.5-9B accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.
Qwen3.5-9B accepts text, images and video as input and returns text.
Qwen3.5-9B is served by 5 providers on OpenRouter: SiliconFlow, DeepInfra, Venice, Parasail and Together. Requests are routed to the best available provider, with automatic failover to the others, and you can pin or exclude providers with provider routing.
Qwen3.5-9B was released on March 10, 2026.
| $0.10 | $0.15 | 1.88s | 10 tps | ||
| $0.10 | $0.15 | 0.73s | 87 tps | ||
| $0.10 | $0.25 | 1.01s | 23 tps | ||
| $0.17 | $0.25 | 0.48s | 46 tps | ||
| $0.10 | $0.15 | 0.46s | 49 tps |
Throughput
87tok/s
P50, best across providers
Latency
0.46s
P50, best provider
100.00%
99.91%
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and customization options.