> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kymaapi.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Qwen 3.7 Flash

> Qwen 3.7 Flash is Alibaba’s vision-language reasoning model built for high-volume multimodal tasks. Reach for it when you need fast, cheap-tier processing of text, images, and video with tool use and reasoning capabilities.

## Overview

This is a vision-language reasoning model from Alibaba that accepts text, image, and video inputs and returns text. It supports a 1M token context window and outputs up to 8,192 tokens. The model operates in the cheap cost tier and is optimized for high-throughput workloads.

On Kyma, it runs through an OpenAI-compatible endpoint with automatic request failover if a serving path degrades. Prompt caching is enabled, billing repeated prefixes at this model's cached input rate. Responses include the exact request cost in usage.cost and the executed model ID in the X-Kyma-Model header.

It does not support structured outputs, so you will need to parse JSON or enforce formatting manually. The 1M context window is large, but the 8,192 token output limit restricts long-form generation.

## Specs

| Field              | Value                                                            |
| ------------------ | ---------------------------------------------------------------- |
| Model ID           | `qwen3.7-flash`                                                  |
| Best for           | Multimodal agents, visual coding and computer use at high volume |
| Context window     | 1M                                                               |
| Max output tokens  | 8K                                                               |
| Input modalities   | Text, Image, Video                                               |
| Output modalities  | Text                                                             |
| Tool calling       | Yes                                                              |
| Structured outputs | Yes                                                              |
| Prompt caching     | Yes                                                              |
| Speed              | Fast                                                             |
| Cost band          | Cheap                                                            |
| Release stage      | Stable                                                           |

## Pricing

|        | Per 1M tokens |
| ------ | :-----------: |
| Input  |    \$0.0498   |
| Output |    \$0.2164   |

Above a long prompt this model costs more per token, because the infrastructure
serving it charges more there. The threshold is on the **prompt**, so you know which
rate applies before you send.

| Prompt length        |   Input  |  Output  |
| -------------------- | :------: | :------: |
| Standard             | \$0.0498 | \$0.2164 |
| Above 32,000 tokens  |  \$0.135 |  \$0.54  |
| Above 256,000 tokens |  \$0.27  |  \$1.08  |

## Use this when

* **Multimodal agent orchestration** — Route text, image, and video inputs to tool-calling agents that run at high volume.
* **Visual code review** — Analyze screenshots, UI mockups, and video walkthroughs to generate code suggestions.
* **Document analysis at scale** — Process large batches of mixed-media files within a 1M token context window.
* **High-throughput chat routing** — Handle conversational workloads with fast response times and low compute overhead.

## Not ideal for

Avoid this model when you require guaranteed structured JSON outputs or need to generate responses longer than 8,192 tokens.

## Pick something else when

* You need guaranteed structured JSON outputs: use [`gpt-5.6-terra`](/models/gpt-5.6-terra).
* You require longer output sequences: use [`qwen-3.7-max`](/models/qwen-3.7-max).

## Example

```python theme={null}
from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="kyma-...")

response = client.chat.completions.create(
    model="qwen3.7-flash",
    messages=[{"role": "user", "content": "..."}],
    tools=[...],  # function calling supported
)
```

## Agent query example

Ask the API which models fit, instead of hardcoding an id:

```bash theme={null}
curl "https://kymaapi.com/v1/models?recommended_for=chat&tools=true&quality_tier=efficient"
```

## FAQ

**Does this model support function calling?**

Yes, it supports tool use and reasoning, allowing you to pass tool definitions and parse the model's calls.

**How does prompt caching affect billing?**

Kyma bills cached prompt prefixes at this model's cached input rate, reducing costs for repeated system prompts or conversation history.

**What happens if the serving path fails?**

Kyma automatically reroutes your request to an alternate serving path, ensuring high availability without manual retries.