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Qwen2.5 Coder 32B InstructCode SOTA Open Model

Try Qwen2.5 Coder 32B InstructAPI Documentation

Master Code Tasks

Code Generation

SOTA Benchmarks

Matches GPT-4o on EvalPlus, LiveCodeBench, BigCodeBench for multi-language synthesis.

Code Repair

Fix Bugs Fast

Scores 73.7 on Aider benchmark, outperforms open models in error correction.

Multi-Language

40+ Languages

Handles Haskell, Racket via balanced pre-training; 75.2 on MdEval repair.

Examples

See what Qwen2.5 Coder 32B Instruct can create

Copy any prompt below and try it yourself in the playground.

SQL Query

“Write a Python function using pandas to analyze sales data from a CSV: group by region, compute total revenue and average units sold, output top 3 regions by revenue.”

API Endpoint

“Generate a FastAPI endpoint that accepts JSON input for user registration, validates email with regex, hashes password with bcrypt, stores in SQLite database.”

Algorithm Fix

“Debug this binary search implementation in Rust that fails on even-length sorted arrays: fn binary_search(arr: &[i32], target: i32) -> Option<usize> { ... } and fix it.”

Data Pipeline

“Create a Python script with asyncio to fetch JSON from multiple APIs concurrently, aggregate results, save to parquet file with pyarrow.”

For Developers

A few lines of code.
Code gen. One call.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Qwen2.5 Coder 32B Instruct

Read the docs

32B parameter LLM for code generation, reasoning, repair via API. Supports 131k token context. Open-weight from Qwen series.

Native 32k tokens, extends to 131k with YaRN. Processes long codebases and histories in one request.

Leads open models on EvalPlus, LiveCodeBench; matches GPT-4o on code gen, 73.7 Aider repair. Strong in 40+ languages.

SOTA open-source coding LLM, outperforms Claude 3.5 Sonnet on LiveCodeBench. Cost-effective for agentic workflows.

32.5B total, 31B non-embedding in 64-layer GQA Transformer. Uses RoPE and optimized inference.

Code synthesis, debugging, explanation in scientific workflows. Balances coding with math, general reasoning.

Ready to create?

Start generating with Qwen2.5 Coder 32B Instruct on ModelsLab.

Try Qwen2.5 Coder 32B InstructAPI Documentation