Name: Qwen3 Thinking 2507 API
Brand: Alibaba Cloud

Overview

What is Qwen3 Thinking 2507 API?

Alibaba's Qwen 3 thinking 2507 is a multilingual reasoning model with knowledge augmentation, and creative capabilities.

Technical Specifications
- Supported inputs: Text
- Output: Text
- Context window: 32,000 tokens / Max output: 16,000 tokens
- Endpoint: /chat/completions

Performance Benchmarks
[embed]

Qwen3 Thinking 2507 API Pricing
- Input: $0.299 / 1M tokens
- Output: $2.99 / 1M tokens

Where to Use Qwen3 Thinking 2507

Coding and code review
Generate, debug, and review code across languages, reading whole files or repos in a single prompt.

Agentic and multi-step workflows
Reliable tool-calling and instruction-following suit agent loops that plan, call tools, and iterate.

Long-document analysis
Summarize and extract from contracts, papers, and reports without aggressive chunking.

Code Sample
[embed]

Uptime and performance

Uptime (24h): 100%
Uptime (7d): 100%
Availability (24h): 100%
Error rate (24h): 0%
End-to-end latency (24h median): 7.5s
Throughput (24h median): 3.7 tok/s

Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.

Use cases

Complex mathematical problem solving and scientific reasoning are documented use cases for Qwen3-235B-A22B-Thinking-2507, with the model described as improved for mathematical, science, and coding tasks and for “high-difficulty, strong-thinking scenarios.”

Coding and advanced software-development work are also stated use cases, including advanced coding challenges, algorithm development, and code generation across complex software engineering tasks.

Long-document analysis and deep research are listed as suitable applications, with the model described as supporting deep, long-form understanding and long-document analysis.

Tool-enabled agentic workflows are another documented fit, including tool use, function calling, structured JSON output, and integration with agent frameworks.

How to use Qwen3 Thinking 2507 API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to alibaba/qwen3-235b-a22b-thinking-2507.

import requests

r = requests.post(
    "https://api.aimlapi.com/v1/chat/completions",
    headers={"Authorization": "Bearer " + AIMLAPI_KEY},
    json={
      "model": "alibaba/qwen3-235b-a22b-thinking-2507",
      "messages": [
        {
          "role": "user",
          "content": "Hello!"
        }
      ]
    },
)
print(r.json())

const r = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "alibaba/qwen3-235b-a22b-thinking-2507",
    "messages": [
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }),
});
console.log(await r.json());

curl -X POST https://api.aimlapi.com/v1/chat/completions \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"alibaba/qwen3-235b-a22b-thinking-2507","messages":[{"role":"user","content":"Hello!"}]}'

OpenAI-compatible — swap the base URL and it works with your existing SDK.