Open-weight 32B code model matching GPT-4o coding ability, ideal for self-hostable code generation, repair, and completion.
Modality:
Text
model ID
qwen/qwen-2.5-coder-32b-instruct
Output Speed *
N/A
tok/s
Intelligence Index *
6.7
/ 100
Context Window *
32768
tokens
Input price
0
Anytoken
Output price
0
Anytoken
A GPT-4o-Class Open Code Model You Can Actually Self-Host
Qwen2.5 Coder 32B Instruct is the largest instruction-tuned model in Alibaba's code-specific Qwen2.5-Coder series, built on Qwen2.5 and trained on 5.5 trillion tokens of source code and text-code data. It is an open-weight, Apache 2.0-licensed model positioned as the coding-specialist flagship of the family. Its differentiator is state-of-the-art open-source coding performance: on EvalPlus and multiple code benchmarks it matches GPT-4o-level results. Code generation, code repair, fill-in-the-middle completion, and code-agent workloads benefit most from this model.
Start building with Qwen2.5 Coder 32B Instruct via the AnyAPI.ai API.
Performance
Coding Accuracy That Closes the Gap With Proprietary Models
Qwen2.5 Coder 32B Instruct excels at code generation, reasoning, and repair across dozens of programming languages. Qwen reports it achieved the highest EvalPlus score among tested models and describes it as the strongest open-source code model at release, with coding ability comparable to GPT-4o. Independent benchmark aggregates place it near 92.7% on HumanEval and 90.2% on MBPP. For teams building code tooling, this means an open-weight, Apache 2.0 model can plausibly replace proprietary APIs for generation and repair tasks, removing per-token vendor dependence while keeping accuracy competitive with closed models.
Benchmarks
How Qwen2.5 Coder 32B Performs in Independent Coding Tests
Independent evaluations reinforce Qwen's coding claims. Aggregated benchmark data reports HumanEval around 92.7% and MBPP around 90.2%, ahead of the general-purpose Qwen2.5 32B Instruct on core code tasks. On Aider's code-editing benchmark, third-party testing placed it near 73.7%, competitive with top-tier proprietary models on real edit workflows. Independent repository-level tests (RepoEval, CrossCodeLongEval) also show strong exact-match performance, in some cases exceeding leading closed models. Treat these as independent measurements rather than official specifications; scores vary by benchmark version and decoding settings.
Output Speed
*
N/A
tok/s
Intelligence Index
*
6.7
/ 100
MMLU *
Broad world knowledge and problem-solving
64
%
GPQA *
PhD-level scientific reasoning across physics, biology, chemistry.
42
%
HLE *
Adherence to multi-step structured instructions.
4
%
LiveCodeBench *
Tool-calling reliability in long agentic loops.
30
%
Technical Specifications
What the model supports
Qwen2.5 Coder 32B Instruct is a dense 32.5B-parameter, text-in/text-out model with no image, audio, or video support. Its architecture natively supports a 131,072-token context, but the two production caveats matter most: many hosted endpoints restrict the effective context to 32,768 tokens, and developers report output degradation when input approaches provider-imposed limits. Enabling the full 128K context requires YaRN rope-scaling configuration. Plan input management carefully for large repositories, since the deployed context may be far smaller than the model's theoretical maximum.
Verified Specifications —
Qwen2.5 Coder Instruct 32B
*
Input modalities
Text
output modalities
Text
Context window
32768
tokens
Maximum output tokens
29491
Reasoning
No
Knowledge cutoff
November 2024
Pricing (standard)
0
AnyTokens in
/
0
AnyTokens out
Limitations & Trade-offs
1
Provider-capped context. Although the model natively supports 131,072 tokens, many hosted endpoints restrict effective context to 32,768 tokens, and unlocking full 128K requires YaRN rope-scaling. Developers report output degrading into nonsense when input nears these limits. For whole-repository analysis or very long-context RAG, verify the deployed context of your specific endpoint or prefer a model with a reliably larger served window.
2
No multimodal input. This is a text-only model with no image, audio, video, or document-vision support. Workflows that require reading screenshots, diagrams, PDFs as images, or UI mockups cannot use it directly. If your coding pipeline depends on visual inputs, pair it with a separate vision-capable model or choose a multimodal alternative.
3
No dedicated reasoning mode. Qwen2.5 Coder 32B Instruct is not a reasoning/extended-thinking model; it produces direct responses without a configurable chain-of-thought budget. For hard algorithmic problems, multi-step planning, or agent tasks that benefit from explicit deliberation, a reasoning-oriented model may produce more reliable results, at the cost of higher latency and token usage.
4
Weaker on non-code tasks than the general variant. Because training was weighted toward code, independent aggregates show it trailing Qwen2.5 32B Instruct on several general benchmarks including GSM8K, MMLU, and MATH. For applications mixing heavy general reasoning with coding, the specialized advantage narrows and a general-purpose model may serve better.
Best-Fit Workloads
Where this model earns its place
01
Code generation and repair services
The model's core strength is producing and fixing code across many languages, backed by top open-source EvalPlus, HumanEval, and MBPP results. It suits backend services that generate functions, refactor code, or auto-repair failing snippets. As an Apache 2.0 open-weight model, it lets teams run this in production without proprietary API dependence.
02
IDE fill-in-the-middle completion
Qwen reports state-of-the-art code completion on infilling benchmarks including Humaneval-Infilling, CrossCodeEval, RepoEval, and SAFIM, using fill-in-the-middle mode. This makes it well-suited to editor autocomplete and inline suggestion features. Watch the served context limit, since completion quality depends on supplying sufficient surrounding file context within the endpoint's window.
03
Code agents and automated editing
Qwen positions the model as a foundation for real-world code agents, and independent Aider code-editing testing placed it near 73.7%, competitive with top proprietary models on realistic edit tasks. It fits agentic pipelines that plan, edit, and re-run code. Confirm tool-calling support on your chosen endpoint, since availability varies.
04
Self-hosted coding infrastructure
At 32.5B dense parameters the model runs on a single high-memory GPU or capable Apple Silicon machine, and its Apache 2.0 license permits commercial self-hosting. This suits teams needing on-premise code intelligence for data-sensitive environments. Expect modest local throughput on consumer hardware, so plan for GPU acceleration in production.
Integration
01
One API integration
02
Easy model switching
03
Flexible for production
04