Qwen3-Coder-Next 80B-A3BvsQwen 2.5 Coder 32B
Complete comparison between Qwen3-Coder-Next 80B-A3B (80B parameters, Alibaba) and Qwen 2.5 Coder 32B (32B, 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 | Qwen3-Coder-Next 80B-A3B | Qwen 2.5 Coder 32B |
|---|---|---|
| Settings | 80B | 32B |
| Family | Qwen | Qwen |
| Author | Alibaba | Alibaba |
| Origin | CN | CN |
| License | Apache 2.0 | Apache 2.0 |
| Context | 262,000 tokens | 131,072 tokens |
| Output | February 2026 | 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 | Qwen3-Coder-Next 80B-A3B | Qwen 2.5 Coder 32B |
|---|---|---|
| Q4_K_M (lightweight) | 48 GB | 19 GB |
| Q5_K_M (balanced) | 58 GB | 23 GB |
| Q8 (near-lossless) | 86 GB | 35 GB |
| FP16 (max quality) | 160 GB | 64 GB |
| CPU-only RAM | 72 GB | 32 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 | Qwen3-Coder-Next 80B-A3B | Qwen 2.5 Coder 32B |
|---|---|---|
| RTX 4090 (24 GB) | ✗ too heavy | 30 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | ✗ too heavy | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | ✗ too heavy | 12 tok/s · Q8 |
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
Qwen3-Coder-Next 80B-A3B
Gated DeltaNet + Attention hybrid MoE. Local coding copilot on a 24 GB GPU.
- Local Copilot on a 24 GB GPU
- 262k ctx
- Apache 2.0
- Hybrid architecture → partial llama.cpp support
Installation
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
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen3-Coder-Next 80B-A3B or Qwen 2.5 Coder 32B?
On a RTX 4090, Qwen3-Coder-Next 80B-A3B won't fit in 24 GB—you need to offload part of it to the CPU, Qwen 2.5 Coder 32B in Q5_K_M (~30 tok/s). See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Qwen 2.5 Coder 32B fits in 19 GB versus 48 GB for its rival—a 29 GB gap, significant if you're targeting an RTX 3060 or a 4060 Ti 8 GB.
Qwen3-Coder-Next 80B-A3B or Qwen 2.5 Coder 32B for coding?
Both are specialized code models—the choice comes down to context (262,000 vs. 131,072 tokens), license (Apache 2.0 vs. Apache 2.0), and available VRAM. Both integrate with Continue or Aider.
Can these models be used in commercial production?
Qwen3-Coder-Next 80B-A3B is under Apache 2.0 — free for commercial use. Qwen 2.5 Coder 32B 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 32B (32B). More capable: Qwen3-Coder-Next 80B-A3B (80B). If you're unsure, run the 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.