Qwen3.5-27B

Text

Dense open-weight Qwen3.5 vision model; 256K context, hybrid thinking; OpenAI-compatible API

Input $0.30 / 1M tokens | Output $2.40 / 1M tokens Qwen3.5-27B API reference →

Model health Insufficient data

runapi.ai

# Base URL
https://runapi.ai

# Endpoints
POST /v1/chat/completions
POST /v1/responses
POST /v1/messages
POST /v1beta/models/qwen3.5-27b:generateContent
POST /v1beta/models/qwen3.5-27b:streamGenerateContent

curl https://runapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $RUNAPI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "qwen3.5-27b",
  "messages": [
    {
      "role": "user",
      "content": "Read this screenshot of a failing dashboard, explain what the error message means, and suggest the code change that would fix it."
    }
  ]
}'

from openai import OpenAI

client = OpenAI(
    base_url="https://runapi.ai/v1",
    api_key="your-runapi-key"
)

response = client.chat.completions.create(
    model="qwen3.5-27b",
    messages=[{"role": "user", "content": "Read this screenshot of a failing dashboard, explain what the error message means, and suggest the code change that would fix it."}]
)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://runapi.ai/v1",
  apiKey: "your-runapi-key"
});

const response = await client.chat.completions.create({
  model: "qwen3.5-27b",
  messages: [{ role: "user", content: "Read this screenshot of a failing dashboard, explain what the error message means, and suggest the code change that would fix it." }]
});

https://runapi.ai 5 endpoints

9 versions available

OVERVIEW

What Qwen3.5-27B is built for

Qwen3.5-27B is a dense native vision-language model from Alibaba's Qwen3.5 Medium series, released with open weights under Apache 2.0 on February 24, 2026. It uses linear attention for fast responses, and Alibaba rates its overall performance as comparable to Qwen3.5-122B-A10B.

It is a hybrid model with thinking on by default, a 262,144-token context window, and up to 65,536 output tokens. Alibaba lists function calling and structured outputs as supported.

On RunAPI, call qwen3.5-27b through the OpenAI-compatible Chat Completions endpoint (/v1/chat/completions) with an OpenAI SDK pointed at https://runapi.ai/v1. Images go in image_url parts inside messages[].content[], and tools use the standard OpenAI tools format.

Model ID

qwen3.5-27b

Released

February 24, 2026

Architecture

Dense, 27B parameters, Apache 2.0 weights

Context window

262,144 tokens

Max output

65,536 tokens

Thinking

Hybrid, on by default

Endpoint on RunAPI

OpenAI Chat Completions, text and image input

Playground

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Specifications

Provider	Alibaba
Model ID	qwen3.5-27b
Modality	Text
Task types	Synchronous
API endpoint	/v1/chat/completions
	/v1/responses

	/v1/messages
	/v1beta/models/qwen3.5-27b:generateContent
	/v1beta/models/qwen3.5-27b:streamGenerateContent
Billing unit	1K tokens
Catalog status	Operational

HOW IT WORKS

Use This Model in 3 Steps

Get Your Key

Sign up for free and create an API key for qwen3.5-27b from the dashboard.
Send a Request

POST to /v1/chat/completions with the qwen3.5-27b model slug and your parameters.
Get Results

Read the completed qwen3.5-27b response directly from the endpoint.

What this model offers

Endpoint coverage

Exposes 1 public endpoint(s): chat_completion.

Usage-based billing

Metered by 1K tokens with no subscription requirement.

Qwen3.5-27B Pricing

Endpoint

Price

chat_completion

Input: $0.30 / 1M tokens Output: $2.40 / 1M tokens

API REFERENCE

Endpoint Specification

Endpoint: POST /v1/chat/completions
Model ID: qwen3.5-27b
Task Types: chat_completion

Request parameters

No request parameters are published for this endpoint.

USE CASES

Customer support

Answer customer questions from a private knowledge base, reducing ticket volume.

Document analysis

Draft contract summaries and flag key clauses for attorney review.

Code generation

Auto-generate unit tests, code reviews, and refactoring suggestions in CI.

Trusted by developers

API Calls / Day

100.0%

Success Rate

10.4s

Avg Generation

Verified customer feedback is not available for this model yet.

FAQ

Frequently asked questions about Qwen3.5-27B

Is Qwen3.5-27B a dense or a Mixture-of-Experts model?

Dense. All 27B parameters run for every token, unlike the Qwen3.5 MoE models such as Qwen3.5-35B-A3B, which activates 3B parameters per token.

How does Qwen3.5-27B compare with Qwen3.5-122B-A10B?

Alibaba rates the two as comparable overall. Both have a 262,144-token context window, image input, hybrid thinking, and function calling.

Should I use Qwen3.5-27B or Qwen3.6-27B?

Qwen3.6-27B is built on Qwen3.5-27B. Qwen reports stronger agentic coding, STEM reasoning, OCR, and video understanding for the 3.6 model. Keep Qwen3.5-27B for prompts and evaluations already tuned on it.

Does Qwen3.5-27B accept images on RunAPI?

Yes. Send image_url parts with the text in a Chat Completions request. Alibaba also lists video input; video requests are not verified on RunAPI.

How do I select qwen3.5-27b?

Pass the model ID shown in the quickstart.

What's the rate limit on this variant?

Per-key rate limits scale with your usage tier. The pricing page shows current limits. If you need higher throughput, contact support to discuss tier upgrades.

Can I switch variants later?

Yes. Variant is a parameter in the request. Switch by changing the model ID — no code changes, no re-authentication, no separate billing setup. All variants share the same API key and request shape.

Does it stream?

Where streaming is available, RunAPI streams end-to-end. LLM models support token-level streaming. Media models use async task polling or webhook callbacks for result delivery.

Where do I report quality issues?

Open an issue on the public GitHub repo or email support at [email protected]. Include the task ID and model ID so the team can investigate the specific generation.

Do I need a separate provider account?

No. Your RunAPI API key is enough to access this variant and every other model in the catalog. You do not need accounts with the underlying provider.

Building with a team?

We're here to help with enterprise setup, integrations, and technical questions.