Devstral Small 2 24BvsDeepSeek Coder V2 Lite 16B
Complete comparison between Devstral Small 2 24B (24B parameters, Mistral AI) 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 | Devstral Small 2 24B | DeepSeek Coder V2 Lite 16B |
|---|---|---|
| Settings | 24B | 16B |
| Family | Mistral | DeepSeek |
| Author | Mistral AI | DeepSeek |
| Origin | FR | CN |
| License | Apache 2.0 | MIT |
| Context | 256,000 tokens | 131,072 tokens |
| Output | December 2025 | 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 | Devstral Small 2 24B | DeepSeek Coder V2 Lite 16B |
|---|---|---|
| Q4_K_M (lightweight) | 14 GB | 10 GB |
| Q5_K_M (balanced) | 17 GB | 12 GB |
| Q8 (near-lossless) | 26 GB | 18 GB |
| FP16 (max quality) | 48 GB | 32 GB |
| CPU-only RAM | 24 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 | Devstral Small 2 24B | DeepSeek Coder V2 Lite 16B |
|---|---|---|
| RTX 4090 (24 GB) | 40 tok/s · Q5_K_M | 45 tok/s · Q8 |
| RTX 4080 (16 GB) | 15 tok/s · Q4_K_M | 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) | 15 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
Devstral Small 2 24B
24B coding specialist, Apache 2.0. 72.2% SWE-Bench. 256k ctx, FR lab.
- 68.0% SWE-bench Verified
- Fits on RTX 4090
- Apache 2.0
- French lab
- No vision
- Dense: slower than code MoEs (Qwen3-Coder 30B-A3B) at the same memory capacity
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): Devstral Small 2 24B or DeepSeek Coder V2 Lite 16B?
On a RTX 4090, Devstral Small 2 24B runs at Q5_K_M (~40 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 14 GB for its rival—a 4 GB difference, significant if you are targeting an RTX 3060 or an 8 GB 4060 Ti.
Devstral Small 2 24B or DeepSeek Coder V2 Lite 16B for coding?
Both are specialized code models — the choice comes down to context (256,000 vs. 131,072 tokens), license (Apache 2.0 vs. MIT), and available VRAM. Both integrate with Continue or Aider.
Can these models be used in commercial production?
Devstral Small 2 24B 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: Devstral Small 2 24B (24B). 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.