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Qwen3-Coder-Flash

Coding modelQwen3

Qwen3-Coder-30B-A3B-Instruct

Release date: 2025-07-31Updated: 2025-08-01 07:53:261,571
Parameters
30.5B
Context length
1000K
Chinese support
Supported
Reasoning ability

Qwen3-Coder-30B-A3B-Instruct is an AI model published by Alibaba, released on 2025-07-31, for Coding model, with 30.5B parameters, and 1000K context length, requiring about 61.09 GB storage, with a 51.60 score on SWE-bench Verified.

Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology

Qwen3-Coder-Flash

Model basics

Reasoning traces
Not supported
Thinking modes
Thinking modes not supported
Context length
1000K tokens
Max output length
16K tokens
Model type
Coding model
Modality (in / out)
Text → Text
Release date
2025-07-31
Model file size
61.09 GB
MoE architecture
Yes
Total params / Active params
30.5B / 3.3B
Knowledge cutoff
No data
Qwen3-Coder-Flash

Open source & experience

Qwen3-Coder-Flash

Official resources

Paper
No paper available
DataLearnerAI blog
No blog post yet
Qwen3-Coder-Flash

API details

API speed
3/5
💡Default unit: $/1M tokens. If vendors use other units, follow their published pricing.
Standard
TypeConditionInputOutput
Text-¥0.0015/ 1K¥0.0060/ 1K
TextContext > 32768¥0.0023/ 1K¥0.0090/ 1K
Qwen3-Coder-Flash

Benchmark Results

Qwen3-Coder-Flash currently shows benchmark results led by Terminal-Bench (19 / 35, score 31.30), SWE-bench Verified (93 / 112, score 51.60). This page also consolidates core specs, context limits, and API pricing so you can evaluate the model from benchmark results and deployment constraints together.

Thinking

Coding and Software Engineer

1 evaluations
Benchmark / mode
Score
Rank/total
51.60
93 / 112

AI Agent - Tool Usage

1 evaluations
Benchmark / mode
Score
Rank/total
31.30
19 / 35

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Qwen3-Coder-Flash

Publisher

Qwen3-Coder-30B-A3B-Instruct

Model Overview

Qwen3-Coder-30B-A3B-Instruct is an AI model published by Alibaba, released on 2025-07-31, for Coding model, with 30.5B parameters, and 1000K context length, requiring about 61.09 GB storage, with a 51.60 score on SWE-bench Verified.

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