Qwen2.5 Coder 32B Instruct
Alibaba • LLM
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...
Specifications
Context Window
33K tokens
Input Price/1M
—
Output Price/1M
—
Parameters
—
Benchmarks
Qwen2.5 Coder 32B Instruct results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
Coding
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 30.0 | 100.0 | — |
| SciCode | 27.0 | 100.0 | — |
Knowledge
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 64.0 | 100.0 | — |
overall
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 12.9 | 100.0 | — |
Reasoning
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| GPQA Diamond | 42.0 | 100.0 | — |
| HLE | 4.0 | 100.0 | — |
Information
- Release date
- November 11, 2024
- Tool Calling
- ❌ Not supported
- Vision
- ❌ Not supported
- Audio
- ❌ Not supported
Full Analysis: Qwen2.5 Coder 32B Instruct
What is Qwen2.5 Coder 32B Instruct?
Qwen2.5 Coder 32B Instruct is an AI model developed by Alibaba, classified as a large language model (LLM). It focuses on text processing and natural language generation. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 33K tokens, it is suitable for processing medium-sized documents like articles, reports, and code sections.
Pricing & Costs in 2026
Qwen2.5 Coder 32B Instruct does not have public per-token pricing available at this time. Some models offer access via enterprise plans or research programs. Check Alibaba's official website for up-to-date availability and pricing.
Benchmarks & Performance
Qwen2.5 Coder 32B Instruct was evaluated on 6 different benchmarks, covering categories like Coding, Knowledge, overall, Reasoning. Results show moderate performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Recommended Use Cases
Qwen2.5 Coder 32B Instruct is suitable for a wide range of AI applications: text generation, summarization, translation, and general assistance.
Comparison with Alternatives
In the 2026 AI model ecosystem, Qwen2.5 Coder 32B Instruct competes directly with similarly capable models. Alibaba competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Frequently Asked Questions
What is Qwen2.5 Coder 32B Instruct?
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...
How much does Qwen2.5 Coder 32B Instruct cost?
Qwen2.5 Coder 32B Instruct does not have public per-token pricing available at this time. Check Alibaba's official website for up-to-date information.
How does Qwen2.5 Coder 32B Instruct compare with other models?
In available benchmarks, Qwen2.5 Coder 32B Instruct scored: LiveCodeBench: 30/100, SciCode: 27/100, MMLU-Pro: 64/100. See the full table above for a detailed comparison.
Is Qwen2.5 Coder 32B Instruct open source?
Yes, Qwen2.5 Coder 32B Instruct is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.
What is Qwen2.5 Coder 32B Instruct best for?
Qwen2.5 Coder 32B Instruct excels at general-purpose language tasks.
Last updated: June 01, 2026 • View methodology →