Qwen 3.6 27B API

Qwen3.6-27B is the first fully dense open-weight model in the Qwen3.6 series and it just beat a 397-billion-parameter model on agentic coding benchmarks.

Modalities: Text, ImageText
In / Out price: $0.6/$3.6per 1M
Context window: 250K tokens
Released: Apr 23, 2026

Qwen 3.6 27B overview

Qwen 3.6 27B by Alibaba Cloud

Whether you are building complex agentic workflows, high-volume customer support bots, or sophisticated code assistants, Qwen 3.6-27B provides the optimal infrastructure for next-generation AI products.

Qwen 3.6 27B specs

Model ID: alibaba/qwen3.6-27b
Developer: Alibaba Cloud
Context window: 256,000 tokens
Max output: 252,000 tokens
Input: Text, Image
Output: Text
API: OpenAI-compatible, base URL https://api.aimlapi.com/v1

Engineered for Speed. Validated for Accuracy

‍Benchmark-tested across coding (HumanEval), math (GSM8K), and reasoning (MMLU) tasks, this 27B-parameter model from Alibaba Cloud's Tongyi Lab delivers performance rivaling 70B-class systems, with 3–5× faster token throughput. Integrated via a low-latency API endpoint, it's the go-to choice for applications where every millisecond and every token budget matters, from real-time translation engines to autonomous decision agents.

Numbers That Actually Matter

Benchmark results are only useful when they correlate with real tasks. Qwen's team shaped this release around direct community feedback, targeting agentic coding workflows rather than chasing leaderboard novelty. Here's how it stacks up.

Coding: Outperforms legacy 70B-class models in Python and JavaScript generation tasks.
Mathematics: Achieves state-of-the-art scores in GSM8K and MATH benchmarks for its size category.
Instruction Following: Demonstrates superior adherence to complex, multi-constraint prompts compared to previous generations.

A Hybrid Architecture Built for Long Contexts

Most models at this size either sacrifice depth for speed or pay the quadratic cost of full self-attention at every layer. Qwen3.6-27B takes a different approach, a layered hybrid that keeps computation linear for most of the stack, reserving full attention for where it counts.

Gated DeltaNet Linear Attention

Three out of every four sublayers use Gated DeltaNet, a linear attention mechanism with O(n) complexity instead of the O(n²) scaling of standard self-attention. This is what makes 262K-token contexts tractable without ballooning memory requirements.

Repeating 16-Block Hybrid Pattern

The model's hidden layout repeats a specific pattern across 64 layers: three Gated DeltaNet → FFN blocks followed by one Gated Attention → FFN block. This 3:1 ratio is deliberate, it balances efficiency with expressiveness throughout the depth of the network.

Asymmetric Q/KV Head Counts

The attention layers use asymmetric query and key/value head configurations, reducing memory bandwidth at inference time without losing the representational capacity that matters for complex reasoning tasks.

Use cases

Qwen3.6 27B is documented as suited to agentic coding and repository-level reasoning, including building coding assistants and multi-step software workflows.

It is also suited to multimodal reasoning over text, images, and video, including document understanding and visual question answering.

The model is documented for frontend generation and UI-focused workflows, where it can inspect screenshots or other visual inputs and help diagnose or fix interface issues.

It is additionally suited to long-context understanding, including work over large codebases and other extended multimodal inputs.

Capabilities

Function calling
Streaming
Structured outputs
Reasoning
Web search

Qwen 3.6 27B benchmarks by Artificial Analysis

See how Qwen 3.6 27B compares in generation speed and latency. Data retrieved Nov 3, 2026.

Speed#164 / 21957.7Output tokens / second Latency#185 / 2163.64Seconds to first token Humanity's Last Exam#126 / 45823.1%Score GPQA Diamond#104 / 43984.2%Score

Speed

Output tokens / second

Benchmark results

Compare 9 models available on AI/ML API across 5 independent benchmarks.

Humanity's Last Exam

Expert-written questions across academic subjects.

Qwen 3.6 27B API pricing

Qwen 3.6 27B pricing in US dollars
Usage	Price	Unit
Input	$0.6	1M tokens
Output	$3.6	1M tokens

Prices from the AI/ML API price list, checked on Nov 4, 2026.

Qwen 3.6 27B vs other models: token prices

AI/ML API input and output rates for 5 models, per million tokens.

AI/ML API model prices in USD per million tokens
Model	Input	Output
Qwen 3.6 27B	$0.6	$3.6
Qwen3.8 Omni Flash	$0.15	$0.47
Qwen3.8-Flash	$0.16	$0.47
Qwen 3.8 27B	$0.4761	$3.39
Qwen3.8 Max Prime	$4.23	$12.7

How to use the Qwen 3.6 27B API

View docs

Build with your agent

Give your agent the model details, code and documentation.

1Get your API key

Create API key

export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"

2Make your first request

curl 'https://api.aimlapi.com/v1/chat/completions' \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "alibaba/qwen3.6-27b",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about the ocean."
    }
  ]
}'

import os
import requests

headers = {
    "Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
    "Content-Type": "application/json",
}
payload = {
    "model": "alibaba/qwen3.6-27b",
    "messages": [
        {
            "role": "user",
            "content": "Write a haiku about the ocean."
        }
    ]
}

response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())

const response = 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/qwen3.6-27b",
    "messages": [
      {
        "role": "user",
        "content": "Write a haiku about the ocean."
      }
    ]
  }),
});

if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());

All posts

Qwen 3.6 27B FAQ

What is the context window of Qwen 3.6 27B?

Qwen 3.6 27B has a 256,000 tokens context window and can return up to 252,000 tokens.

What inputs and outputs does Qwen 3.6 27B support?

Qwen 3.6 27B takes image, text as input and returns text.

What is the model ID for Qwen 3.6 27B?

Use alibaba/qwen3.6-27b as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.

When was Qwen 3.6 27B released?

Qwen 3.6 27B became available on April 23, 2026.

How much does Qwen 3.6 27B cost?

Qwen 3.6 27B costs $0.6 input and $3.6 output per 1M tokens on AI/ML API, billed as you go with no subscription.

Does Qwen 3.6 27B support streaming?

Yes, Qwen 3.6 27B can stream responses as they are generated.

Can Qwen 3.6 27B read images?

Yes, Qwen 3.6 27B accepts image input alongside text.

Who created Qwen 3.6 27B?

Qwen 3.6 27B was built by Alibaba Cloud.

Start building with Qwen 3.6 27B

Get API key

Qwen 3.6 27B API

alibaba/qwen3.6-27b

Build with your agent

Give your agent the model details, code and documentation.

1Get your API key

Create API key

export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"

2Make your first request

curl 'https://api.aimlapi.com/v1/chat/completions' \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "alibaba/qwen3.6-27b",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about the ocean."
    }
  ]
}'

import os
import requests

headers = {
    "Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
    "Content-Type": "application/json",
}
payload = {
    "model": "alibaba/qwen3.6-27b",
    "messages": [
        {
            "role": "user",
            "content": "Write a haiku about the ocean."
        }
    ]
}

response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())

const response = 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/qwen3.6-27b",
    "messages": [
      {
        "role": "user",
        "content": "Write a haiku about the ocean."
      }
    ]
  }),
});

if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());

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Qwen 3.6 27B API

Qwen 3.6 27B overview

Qwen 3.6 27B by Alibaba Cloud

Qwen 3.6 27B specs

Engineered for Speed. Validated for Accuracy

Numbers That Actually Matter

A Hybrid Architecture Built for Long Contexts

Gated DeltaNet Linear Attention

Repeating 16-Block Hybrid Pattern

Asymmetric Q/KV Head Counts

Use cases

Capabilities

Qwen 3.6 27B benchmarks by Artificial Analysis

Speed

Benchmark results

Humanity's Last Exam

GPQA Diamond

Terminal-Bench 4.0

AA-LCR

SciCode

Qwen 3.6 27B API pricing

Qwen 3.6 27B vs other models: token prices

How to use the Qwen 3.6 27B API

Build with your agent

1Get your API key

2Make your first request

Related articles

Qwen 3.6 27B FAQ

Start building with Qwen 3.6 27B

Qwen 3.6 27B API

Build with your agent

1Get your API key

2Make your first request

Qwen 3.6 27B API

Qwen 3.6 27B overview

Qwen 3.6 27B by Alibaba Cloud

Qwen 3.6 27B specs

Engineered for Speed. Validated for Accuracy

Numbers That Actually Matter

A Hybrid Architecture Built for Long Contexts

Gated DeltaNet Linear Attention

Repeating 16-Block Hybrid Pattern

Asymmetric Q/KV Head Counts

Use cases

Capabilities

Speed

Benchmark results

Humanity's Last Exam

GPQA Diamond

Terminal-Bench 4.0

AA-LCR

SciCode

Qwen 3.6 27B API pricing

Qwen 3.6 27B vs other models: token prices

How to use the Qwen 3.6 27B API

Build with your agent

1Get your API key

2Make your first request

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