Qwen 2.5 Coder 32BvsQwen 2.5 Coder 14B Instruct
Complete comparison between Qwen 2.5 Coder 32B (32B parameters, Alibaba) and Qwen 2.5 Coder 14B Instruct (14B, Alibaba). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Qwen 2.5 Coder 32B | Qwen 2.5 Coder 14B Instruct |
|---|---|---|
| Settings | 32B | 14B |
| Family | Qwen | Qwen |
| Author | Alibaba | Alibaba |
| Origin | CN | CN |
| License | Apache 2.0 | Apache 2.0 |
| Context | 131,072 tokens | 131,072 tokens |
| Output | November 2024 | November 2024 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Qwen 2.5 Coder 32B | Qwen 2.5 Coder 14B Instruct |
|---|---|---|
| Q4_K_M (lightweight) | 19 GB | 9 GB |
| Q5_K_M (balanced) | 23 GB | 11 GB |
| Q8 (near-lossless) | 35 GB | 16 GB |
| FP16 (max quality) | 64 GB | 28 GB |
| CPU-only RAM | 32 GB | 16 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Qwen 2.5 Coder 32B | Qwen 2.5 Coder 14B Instruct |
|---|---|---|
| RTX 4090 (24 GB) | 30 tok/s · Q5_K_M | 55 tok/s · Q8 |
| RTX 4080 (16 GB) | ✗ too heavy | 20 tok/s · Q8 |
| RTX 3060 12GB (12 GB) | ✗ too heavy | 6 tok/s · Q5_K_M |
| Apple M4 Pro (48 GB) (36 GB) | 12 tok/s · Q8 | 20 tok/s · FP16 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Qwen 2.5 Coder 32B
The open-weight code reference from late 2024, now surpassed by the Qwen3-Coder / Devstral generation.
- Best open-weight coding LLM
- Very strong on HumanEval (2024 benchmark)
- 128k context
- Requires 20+ GB of VRAM
- Worse at general chat
Installation
Qwen 2.5 Coder 14B Instruct
Coding 14B. HumanEval 89.6, LiveCodeBench 37.1. VRAM sweet spot for self-hosted coding.
- HumanEval 89.6
- LiveCodeBench 37.1
- Apache 2.0
- Worse at general chat
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen 2.5 Coder 32B or Qwen 2.5 Coder 14B Instruct?
On an RTX 4090, Qwen 2.5 Coder 32B runs at Q5_K_M (~30 tok/s), Qwen 2.5 Coder 14B Instruct in Q8 (~55 tok/s). In raw throughput, Qwen 2.5 Coder 14B Instruct wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Qwen 2.5 Coder 14B Instruct fits in 9 GB versus 19 GB for its rival — a 10 GB gap, significant if you are targeting an RTX 3060 or an 8 GB 4060 Ti.
Qwen 2.5 Coder 32B or Qwen 2.5 Coder 14B Instruct for coding?
Qwen 2.5 Coder 32B is the best fit (code-specialized, top HumanEval scores). For an IDE copilot, pair it with Continue or Aider.
Can these models be used in commercial production?
Qwen 2.5 Coder 32B is under Apache 2.0 — free for commercial use. Qwen 2.5 Coder 14B Instruct is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 2.5 Coder 14B Instruct (14B). More capable: Qwen 2.5 Coder 32B (32B). If you're unsure, run it configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.