Qwen3.5-Flash

• GPQA DiamondVery hard multiple-choice questions in biology, physics and chemistry, written by PhD experts. Random guessing scores 25%.82.3%#61 of 116
• AIME mathCompetition-level math problems in the style of the American Invitational Mathematics Examination. Share solved.84.4%#60 of 112
• FrontierMathOriginal, unpublished math problems written by professional mathematicians, from advanced to research level. Share solved.18.2%#65 of 73
• SimpleQA VerifiedShort factual questions. Measures how often the model gets facts right instead of making something up.20.3%#58 of 69

Benchmark data: Epoch AI, licensed CC BY 4.0

Companies that serve this model through an API: 1. Prices per million tokens, cheapest first.

Availability and prices come from OpenRouter’s public API and are refreshed daily. Uptime is the share of successful requests over the last 24 hours. Some providers run a compressed version of open models (fp8, int4), which is cheaper but can be slightly less accurate.

Call Qwen3.5-Flash through OpenRouter’s OpenAI-compatible API: create a key on openrouter.ai, then run one of these examples.

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "qwen/qwen3.5-flash-02-23", "messages": [{"role": "user", "content": "Hello! What can you do?"}]}'

import os
from openai import OpenAI

client = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=os.environ["OPENROUTER_API_KEY"])
reply = client.chat.completions.create(
    model="qwen/qwen3.5-flash-02-23",
    messages=[{"role": "user", "content": "Hello! What can you do?"}],
)
print(reply.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://openrouter.ai/api/v1", apiKey: process.env.OPENROUTER_API_KEY });
const reply = await client.chat.completions.create({
  model: "qwen/qwen3.5-flash-02-23",
  messages: [{ role: "user", content: "Hello! What can you do?" }],
});
console.log(reply.choices[0].message.content);

The same code works with every model on this site: only the model ID changes. Many labs also offer their own API and SDK.

• 1,000 chat replies

  ≈ 500 tokens in, 300 out each
  $0.111
• Summarise a 100-page document

  ≈ 50,000 tokens in, 1,000 out
  $0.0035
• An app with 10,000 requests a day for a month

  ≈ 1,000 tokens in, 400 out each
  $50.70

Cost calculator →

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How much does Qwen3.5-Flash cost?

Qwen3.5-Flash costs $0.065 per million input tokens and $0.26 per million output tokens through the API. For example, 1,000 chat replies of about 500 tokens in and 300 out cost around $0.111.

What is the context window of Qwen3.5-Flash?

Qwen3.5-Flash can read up to 1,000,000 tokens in one request, about 1,875 pages of text. It can write up to 65,536 tokens in a single answer.

Is Qwen3.5-Flash open source?

No. Qwen3.5-Flash is a closed model: its weights are not published, so you use it through Alibaba Qwen’s own apps and API, or through cloud providers.

Who makes Qwen3.5-Flash?

Qwen3.5-Flash is made by Alibaba Qwen. It became available through the API on February 25, 2026.

How good is Qwen3.5-Flash?

Qwen3.5-Flash has an overall index of 144.0 in Epoch AI’s independent tests, ranking #78 of 136 models we track.

Can Qwen3.5-Flash read images?

Yes. Besides text, it accepts images and video as input.

What are the alternatives to Qwen3.5-Flash?

Models with a similar price from other companies include Granite 4.2 8B, Ling 3.0 Flash Fin and Nemotron 3.5 Lightning. You can compare them side by side on this page.