Qwen 2.5 Coder 32B Instruct
Alibaba's open-weights code generation specialist matching GPT-4o on coding benchmarks while fitting on a single GPU.
128k
Tokens
8.192k
Output limit
Dense Transformer
Model family
32.5B
Total / Active
$0.00
Per 1M tokens
$0.00
Per 1M tokens
Model Overview
Qwen 2.5 Coder 32B Instruct is widely recognized as the premier open-weights model for software development and repository-level reasoning. It matches or exceeds larger closed models on HumanEval, LiveCodeBench, and SWE-bench while running smoothly on a single RTX 3090/4090 (24GB VRAM) in 4-bit quantization.
Developer Implementation Notes
Permissive Apache 2.0 license. In Q4_K_M GGUF format, it consumes only 19.8GB VRAM and runs at 35+ tokens/sec on consumer hardware.
Key Strengths
- Open-source under permissive Apache 2.0 license
- Matches closed frontier models on LiveCodeBench (60.1%)
- Runs on a single 24GB consumer GPU (RTX 3090/4090)
- 128k context window support with full repository understanding
Limitations & Boundaries
- Text/code only (no visual diagram comprehension)
- General creative writing is less polished than non-coder models
Capabilities & Modalities
Best Production Use Cases
- Local AI code editor backend (Cursor, Continue.dev, Claude Code)
- Private enterprise codebase automated refactoring
- Autonomous code review & test generation
Verified Benchmark Results
Standardized evaluations with methodology notes and authoritative citation links.
Pricing & Inference Cost Calculator
Token Cost Estimator – Qwen 2.5 Coder 32B Instruct
Calculate projected inference spend with prompt caching
Quick Workload Presets
$0.0000
2.00M input tokens
$0.0000
0.50M output tokens
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Avg: $0.00000 / req
Deployment & Integration Options
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Data Accuracy & Verification Notice
AI model specifications, pricing records, and benchmark metrics published on this platform are compiled directly from authoritative sources (official provider documentation, research papers, and verified evaluation harnesses). Benchmark results reflect specific test harnesses and prompting methodologies; scores are not directly comparable across differing evaluation setups.