Groq

Qwen/Qwen3.6-27B

Preview
qwen/qwen3.6-27b
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TOKEN SPEED
~500 tps
Powered bygroq
INPUT
Text, images
OUTPUT
Text
Alibaba Cloud logoAlibaba Cloud
Model card

Qwen 3.6 27B is a 27-billion-parameter multimodal model from Alibaba's Qwen series, delivering flagship-level agentic coding and reasoning that rivals models many times its size. It accepts both image and text inputs for visual understanding tasks such as image analysis, OCR, and visual question answering, and supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within a single model, with a long context window and strong multilingual support.


PRICING

Input
$0.60
1.7M / $1
Output
$3.00
333,333 / $1

LIMITS

CONTEXT WINDOW
131,072

MAX OUTPUT TOKENS
32,768

MAX FILE SIZE
20 MB

MAX INPUT IMAGES
3

QUANTIZATION

This uses Groq's TruePoint Numerics, which reduces precision only in areas that don't affect accuracy, preserving quality while delivering significant speedup over traditional approaches. Learn more here.

Key Technical Specifications

Model Architecture

A dense model with 27 billion parameters across 64 layers, using a hybrid Gated DeltaNet and Gated Attention design with a 5120 hidden dimension. Features a dual-mode system supporting both thinking mode for complex reasoning and non-thinking mode for efficient dialogue, with a 131K-token context window on Groq.

Performance Metrics

Qwen 3.6 27B demonstrates flagship-level performance across reasoning and agentic coding benchmarks:
  • GPQA Diamond (Reasoning): 87.8%
  • AIME 2026 (Math): 94.1%
  • LiveCodeBench v6 (Coding): 83.9%
  • SWE-bench Verified (Agentic Coding): 77.2%
  • SWE-bench Pro (Agentic Coding): 53.5%

Use Cases

Agentic Coding and Software Engineering
Delivers flagship-level coding performance in a compact dense model, ideal for autonomous coding agents and full-stack development.
  • Repository-level code generation and refactoring
  • Bug fixing and multi-file edits
  • Integration with coding assistants and agent scaffolds
  • Tool calling for software engineering workflows
Complex Problem Solving and Dialogue
Switches between deep reasoning and efficient conversation within a single model.
  • Multi-step reasoning and mathematical problem solving
  • Creative writing and multi-turn dialogue
  • Multilingual content generation
  • Strategic planning and decision support
Multimodal Visual Understanding
Accepts image and text inputs for vision tasks alongside its text capabilities.
  • Image analysis and captioning
  • Optical Character Recognition (OCR)
  • Visual question answering
  • Chart, diagram, and document understanding

Best Practices

  • Mode Selection: use thinking mode (reasoning_effort="default") for complex reasoning, math, and coding, and non-thinking mode (reasoning_effort="none") for efficient, general-purpose dialogue.
  • Thinking Mode (general): temperature=1.0, top_p=0.95, top_k=20, min_p=0. For precise coding tasks, lower the temperature to 0.6.
  • Non-thinking Mode: temperature=0.7, top_p=0.80, top_k=20, min_p=0, presence_penalty=1.5.
  • Math Problems: include 'Please reason step by step, and put your final answer within \boxed{}' in the prompt.
  • History Management: in multi-turn conversations, only include final outputs without thinking content.
  • Reasoning Format: set reasoning_format to hidden to return only the final answer, or parsed to include the reasoning in a separate field.
  • Use the full 131K context window for repository-scale code and multi-document workflows.

Get Started with Qwen 3.6 27B

Experience state-of-the-art reasoning and agentic coding with Qwen 3.6 27B at Groq speed:

shell
pip install groq
Python
from groq import Groq
client = Groq()
completion = client.chat.completions.create(
    model="qwen/qwen3.6-27b",
    messages=[
        {
            "role": "user",
            "content": "Explain why fast inference is critical for reasoning models"
        }
    ]
)
print(completion.choices[0].message.content)

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