Devstral Small 2 24B vs DeepSeek Coder V2 Lite 16B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Devstral Small 2 24B | DeepSeek Coder V2 Lite 16B |
|---|---|---|
| Parameters | 24B | 16B |
| Author | Mistral AI | DeepSeek |
| License | Apache 2.0 | MIT |
| Context window | 0k | 0k |
| VRAM at Q4 | 14 GB | 10 GB |
| VRAM at Q5 | 17 GB | 12 GB |
| VRAM at Q8 | 26 GB | 18 GB |
| VRAM at FP16 | 48 GB | 32 GB |
| Use cases | code, fr | code |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
The two models at a glance
About Devstral Small 2 24B
Mistral AI's 24B coding specialist co-developed with All Hands AI, scoring 72.2% on SWE-Bench under Apache 2.0. Fits on a single RTX 4090. Strengths: 72.2% SWE-Bench in a 24B dense model, Runs comfortably on a single RTX 4090, 256K context for whole-repo work, Apache 2.0 license.
About DeepSeek Coder V2 Lite 16B
A 16B MoE code specialist from DeepSeek covering 338 programming languages with a 128k context. Fast inference for its quality tier. Strengths: 128k context for code, MoE architecture keeps inference fast, Coverage of 338 programming languages, Strong code generation and repair.
How they compare
Devstral Small 2 24B comes from Mistral AI and DeepSeek Coder V2 Lite 16B from DeepSeek, they belong to the Mistral and DeepSeek families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.
At 24B vs 16B parameters, Devstral Small 2 24B is the larger of the two. At Q4, DeepSeek Coder V2 Lite 16B fits in about 10 GB of VRAM versus 14 GB for the other — a 4 GB difference that matters on consumer GPUs.
The two models target different sweet spots: Devstral Small 2 24B is tuned for code, fr, while DeepSeek Coder V2 Lite 16B leans toward code. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, DeepSeek Coder V2 Lite 16B pushes roughly 18 tokens/sec versus 15, so it is the more responsive choice for interactive or high-volume use. For long-context work, Devstral Small 2 24B offers the bigger window (250k vs 128k tokens).
Memory, quantization & throughput
Across quantization levels, Devstral Small 2 24B requires Q4 ≈ 14 GB, Q5 ≈ 17 GB, Q8 ≈ 26 GB, FP16 ≈ 48 GB, while DeepSeek Coder V2 Lite 16B requires Q4 ≈ 10 GB, Q5 ≈ 12 GB, Q8 ≈ 18 GB, FP16 ≈ 32 GB. In practice Devstral Small 2 24B needs a 16 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.
Without a GPU, Devstral Small 2 24B needs roughly 24 GB of system RAM to run on CPU and DeepSeek Coder V2 Lite 16B about 18 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 15 tokens/sec from Devstral Small 2 24B and 18 from DeepSeek Coder V2 Lite 16B, scaling up to 40 and 45 tokens/sec on high-end hardware.
Which fits your GPU
Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Devstral Small 2 24B or DeepSeek Coder V2 Lite 16B to the card you actually own:
- On a 12 GB GPU: Devstral Small 2 24B does not fit; DeepSeek Coder V2 Lite 16B runs at Q5 (12 GB).
- On a 16 GB GPU: Devstral Small 2 24B runs at Q4 (14 GB); DeepSeek Coder V2 Lite 16B runs at Q5 (12 GB).
- On a 24 GB GPU: Devstral Small 2 24B runs at Q5 (17 GB); DeepSeek Coder V2 Lite 16B runs at Q8 (18 GB).
Benchmark scores
Reported benchmarks for Devstral Small 2 24B: SWE-Bench 72.2.
Reported benchmarks for DeepSeek Coder V2 Lite 16B: HumanEval 81.1, LiveCodeBench 28.8.
Bottom line: which should you pick?
- Pick Devstral Small 2 24B for long-context work (up to 250k tokens).
- Pick DeepSeek Coder V2 Lite 16B for lower VRAM and faster inference; pick Devstral Small 2 24B for maximum headline quality.
- Pick Devstral Small 2 24B if your workload is fr.
Which GPU should you buy to run Devstral Small 2 24B?
To run Devstral Small 2 24B locally at Q4, you need ~14 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).
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Frequently asked questions
What is the difference between Devstral Small 2 24B and DeepSeek Coder V2 Lite 16B?
The headline differences: Devstral Small 2 24B is a 24B model and DeepSeek Coder V2 Lite 16B is 16B; their context windows differ (250k vs 128k tokens); they ship under different licenses (Apache 2.0 vs MIT). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Devstral Small 2 24B and DeepSeek Coder V2 Lite 16B run on a 24 GB GPU?
At a Q4 quantization, Devstral Small 2 24B needs about 14 GB of VRAM and fits comfortably on a 24 GB GPU; DeepSeek Coder V2 Lite 16B needs about 10 GB and fits comfortably on a 24 GB GPU. DeepSeek Coder V2 Lite 16B is the lighter option for tight VRAM budgets.
Which is faster, Devstral Small 2 24B or DeepSeek Coder V2 Lite 16B?
DeepSeek Coder V2 Lite 16B is the smaller model (16B vs 24B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
What licenses do Devstral Small 2 24B and DeepSeek Coder V2 Lite 16B use?
Devstral Small 2 24B is licensed under Apache 2.0 and DeepSeek Coder V2 Lite 16B under MIT.
Which has the longer context window, Devstral Small 2 24B or DeepSeek Coder V2 Lite 16B?
Devstral Small 2 24B has the larger context window (250k vs 128k tokens), so it handles longer documents and codebases in a single prompt.