Qwen 2.5 Coder 32BvsDeepSeek Coder V2 Lite 16B
Complete comparison between Qwen 2.5 Coder 32B (32B parameters, Alibaba) and DeepSeek Coder V2 Lite 16B (16B, DeepSeek). 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 | DeepSeek Coder V2 Lite 16B |
|---|---|---|
| Settings | 32B | 16B |
| Family | Qwen | DeepSeek |
| Author | Alibaba | DeepSeek |
| Origin | CN | CN |
| License | Apache 2.0 | MIT |
| Context | 131,072 tokens | 131,072 tokens |
| Output | November 2024 | June 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 | DeepSeek Coder V2 Lite 16B |
|---|---|---|
| Q4_K_M (lightweight) | 19 GB | 10 GB |
| Q5_K_M (balanced) | 23 GB | 12 GB |
| Q8 (near-lossless) | 35 GB | 18 GB |
| FP16 (max quality) | 64 GB | 32 GB |
| CPU-only RAM | 32 GB | 18 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 | DeepSeek Coder V2 Lite 16B |
|---|---|---|
| RTX 4090 (24 GB) | 30 tok/s · Q5_K_M | 45 tok/s · Q8 |
| RTX 4080 (16 GB) | ✗ too heavy | 18 tok/s · Q5_K_M |
| RTX 3060 12GB (12 GB) | ✗ too heavy | 5 tok/s · Q5_K_M |
| Apple M4 Pro (48 GB) (36 GB) | 12 tok/s · Q8 | 18 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
DeepSeek Coder V2 Lite 16B
Code-specialized MoE. Fast despite its size.
- 128k code context
- Efficient MoE
- Very good at code generation and correction
- DeepSeek license
- Lite version less powerful than 236B
- Worse than Qwen 2.5 Coder 32B
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen 2.5 Coder 32B or DeepSeek Coder V2 Lite 16B?
On an RTX 4090, Qwen 2.5 Coder 32B runs at Q5_K_M (~30 tok/s), DeepSeek Coder V2 Lite 16B in Q8 (~45 tok/s). In pure throughput terms, DeepSeek Coder V2 Lite 16B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, DeepSeek Coder V2 Lite 16B fits in 10 GB versus 19 GB over its rival—a 9 GB difference, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.
Qwen 2.5 Coder 32B or DeepSeek Coder V2 Lite 16B 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. DeepSeek Coder V2 Lite 16B is also free under the MIT license. For SaaS, favor Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: DeepSeek Coder V2 Lite 16B (16B). 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.