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QwenText Generation/v1/ai/chat

Qwen 3.5 Flash

Access Qwen 3.5 Flash through one API key. Ultra-fast with 1M context.

Alibaba's fast inference model. 1M context window at an ultra-low price for high-throughput tasks.

No credit card required. Takes 30 seconds.

2,400+

Developers

1.2M+

API calls served

100+

Endpoints

$0.01

Per call

Yep, that's it.

Try it live

Send a message and see Qwen 3.5 Flash respond in real time.

POST/v1/ai/chat

Maximum tokens in the response.

Real-time tokens

Hit "Send Request" to see the response

Context Window

1M tokens

Max Output

66K tokens

Input Price

$0.09 / 1M tokens

Output Price

$0.36 / 1M tokens

Strengths

1M context

A 1,000,000-token context window lets Qwen 3.5 Flash ingest entire books, large codebases, or long transcript archives in a single request, with up to 65,536 output tokens.

Ultra fast

Optimized for fast inference, it returns responses quickly even on long inputs, suiting latency-sensitive and high-concurrency workloads.

Ultra low cost

At $0.09 per 1M input and $0.36 per 1M output tokens, it pairs a million-token window with pricing low enough for continuous, large-scale use.

High throughput

Its speed-tuned design sustains many simultaneous requests, making it well suited to batch summarization and bulk document processing.

Quick start

Copy this snippet and start making calls with Qwen 3.5 Flash.

const res = await fetch('https://api.yepapi.com/v1/ai/chat', {
  method: 'POST',
  headers: {
    'x-api-key': 'YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    "model": "qwen/qwen3.5-flash-02-23",
    "messages": [
      {
        "role": "user",
        "content": "Explain API gateways in 2 sentences."
      }
    ],
    "maxTokens": 256
  }),
});
const { data } = await res.json();
console.log(data.message.content);

Why use Qwen 3.5 Flash through YepAPI?

One API key for all models — no separate accounts
OpenAI SDK compatible — just change the base URL
No monthly minimums — pay per token
Switch models with one line of code
Full provider passthrough — citations, search results, and all extras included
Streaming and non-streaming support on every model
Works with Cursor, Claude, LangChain, and any LLM tool
Unified billing across all providers

Qwen 3.5 Flash API: 1M-token context at ultra-low cost

Qwen 3.5 Flash is Alibaba's fast-inference Qwen model, combining a one-million-token context window with pricing low enough to run continuously at scale.

On YepAPI you call Qwen 3.5 Flash through a single OpenAI-compatible endpoint at $0.09 per 1M input and $0.36 per 1M output tokens, with a 1,000,000-token context window.

What is Qwen 3.5 Flash?

Qwen 3.5 Flash is a speed-optimized model in Alibaba's Qwen 3.5 family, built to deliver fast responses at very low cost while carrying an unusually large 1,000,000-token context window. The combination is rare: most models force a trade-off between context size and price, but Flash offers both, making it suited to workloads that must read enormous inputs cheaply and quickly. It supports up to 65,536 output tokens per response. The model targets high-throughput jobs such as long-document summarization, large-scale retrieval-augmented generation, and bulk processing where a million tokens of context removes the need for heavy chunking.

Build with Qwen 3.5 Flash via YepAPI

Access Qwen 3.5 Flash through YepAPI's OpenAI-compatible /v1/ai/chat endpoint. Point your OpenAI SDK at YepAPI and set the model string to qwen-3.5-flash; switching models later is a single-string edit. One YepAPI key also covers every other model plus SEO, SERP, and web-scraping tools, so you can crawl large sets of pages and feed them straight into Flash's million-token window for summarization or analysis without juggling multiple providers.

Qwen 3.5 Flash API pricing — $0.09 / $0.36 per 1M tokens

Qwen 3.5 Flash costs $0.09 per 1M input tokens and $0.36 per 1M output tokens on YepAPI. That pricing is what makes its 1M context practical: feeding very long inputs stays affordable even at scale. For RAG and summarization pipelines that push large prompt sizes, the low input rate keeps the dominant cost component in check, so you can lean on the big context window rather than aggressive chunking.

Qwen 3.5 Flash for long-document and RAG workloads

The million-token window makes Qwen 3.5 Flash ideal for jobs that choke smaller-context models: summarizing entire books or report archives, answering questions across large document sets, and running RAG without splitting content into many fragments. Its fast inference and high throughput let you process large batches quickly, and the low per-token price keeps continuous operation viable. When the bottleneck is fitting and affording huge inputs, Flash is the obvious choice.

Try Qwen 3.5 Flash free

Every new YepAPI account includes $5 of free credit with no card required. With Flash's low rates, that covers testing the full million-token window on your longest real documents, so you can confirm quality and cost before committing.

Start generating in 30 seconds

$5 free credit on signup. No credit card required. Pay per call.

What developers say

Switched from SerpAPI and cut our SERP costs by 80%. Same data quality, way simpler billing.

Marcus T.

SEO Platform Founder

One API key for AI models, SERP data, and web scraping. Saved us from managing 4 separate providers.

Priya S.

Full-Stack Developer

The $5 free credit let us prototype our entire rank tracking feature before committing. No other API does that.

Jake R.

Indie Hacker

Frequently asked questions

Alibaba's fast inference model. 1M context window at an ultra-low price for high-throughput tasks.

Input tokens cost $0.09 per 1M tokens and output tokens cost $0.36 per 1M tokens through YepAPI. No monthly minimums — you only pay for what you use.

Sign up for a free API key, then send requests to the /v1/ai/chat endpoint.

Qwen 3.5 Flash supports a 1M token context window with up to 66K output tokens per request.

Ready to use Qwen 3.5 Flash?

$5 free credit on signup. No credit card required. Pay per call.

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