Lucie 7B vs DeepSeek R1 Distill 7B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Lucie 7B | DeepSeek R1 Distill 7B |
|---|---|---|
| Parameters | 7B | 7B |
| Author | OpenLLM-France | DeepSeek |
| License | Apache 2.0 | MIT |
| Context window | 0k | 0k |
| VRAM at Q4 | 5 GB | 5 GB |
| VRAM at Q5 | 6 GB | 6 GB |
| VRAM at Q8 | 9 GB | 9 GB |
| VRAM at FP16 | 16 GB | 16 GB |
| Use cases | chat, fr | reasoning |
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 Lucie 7B
A French-sovereign 7B model from OpenLLM-France, backed by CNRS and LINAGORA, with a fully transparent and auditable training corpus. Strengths: Full European data sovereignty story, Publicly available training corpus, Strong formal French output, Backed by CNRS and LINAGORA.
About DeepSeek R1 Distill 7B
A 7B DeepSeek model distilled from R1 671B with explicit chain-of-thought reasoning. Surprisingly strong on AIME and MATH for its size. Strengths: Explicit chain-of-thought reasoning at 7B scale, Strong AIME and MATH scores for its size, 32k context, MIT license.
How they compare
Lucie 7B comes from OpenLLM-France and DeepSeek R1 Distill 7B from DeepSeek, they belong to the Lucie 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.
Lucie 7B and DeepSeek R1 Distill 7B share the same 7B parameter class. Both need about 5 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.
The two models target different sweet spots: Lucie 7B is tuned for chat, fr, while DeepSeek R1 Distill 7B leans toward reasoning. Match the model to your dominant workload rather than to raw size.
For long-context work, DeepSeek R1 Distill 7B offers the bigger window (32k vs 4k tokens).
Memory, quantization & throughput
Across quantization levels, Lucie 7B requires Q4 ≈ 5 GB, Q5 ≈ 6 GB, Q8 ≈ 9 GB, FP16 ≈ 16 GB, while DeepSeek R1 Distill 7B requires Q4 ≈ 5 GB, Q5 ≈ 6 GB, Q8 ≈ 9 GB, FP16 ≈ 16 GB. In practice Lucie 7B fits an 8 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, Lucie 7B needs roughly 8 GB of system RAM to run on CPU and DeepSeek R1 Distill 7B about 8 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 35 tokens/sec from Lucie 7B and 35 from DeepSeek R1 Distill 7B, scaling up to 90 and 90 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 Lucie 7B or DeepSeek R1 Distill 7B to the card you actually own:
- On a 8 GB GPU: Lucie 7B runs at Q5 (6 GB); DeepSeek R1 Distill 7B runs at Q5 (6 GB).
- On a 12 GB GPU: Lucie 7B runs at Q8 (9 GB); DeepSeek R1 Distill 7B runs at Q8 (9 GB).
- On a 16 GB GPU: Lucie 7B runs at FP16 (16 GB); DeepSeek R1 Distill 7B runs at FP16 (16 GB).
- On a 24 GB GPU: Lucie 7B runs at FP16 (16 GB); DeepSeek R1 Distill 7B runs at FP16 (16 GB).
Benchmark scores
Reported benchmarks for Lucie 7B: MMLU (fr) 54.2, FrenchBench 68.
Reported benchmarks for DeepSeek R1 Distill 7B: AIME 2024 55.5, MATH-500 92.8.
Bottom line: which should you pick?
- Pick DeepSeek R1 Distill 7B for long-context work (up to 32k tokens).
- Pick Lucie 7B if your workload is chat, fr.
- Pick DeepSeek R1 Distill 7B if your workload is reasoning.
Which GPU should you buy to run Lucie 7B?
To run Lucie 7B locally at Q4, you need ~5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Frequently asked questions
What is the difference between Lucie 7B and DeepSeek R1 Distill 7B?
The headline differences: both are 7B models; their context windows differ (4k vs 32k 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 Lucie 7B and DeepSeek R1 Distill 7B run on a 24 GB GPU?
At a Q4 quantization, Lucie 7B needs about 5 GB of VRAM and fits comfortably on a 24 GB GPU; DeepSeek R1 Distill 7B needs about 5 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.
What licenses do Lucie 7B and DeepSeek R1 Distill 7B use?
Lucie 7B is licensed under Apache 2.0 and DeepSeek R1 Distill 7B under MIT.
Which has the longer context window, Lucie 7B or DeepSeek R1 Distill 7B?
DeepSeek R1 Distill 7B has the larger context window (32k vs 4k tokens), so it handles longer documents and codebases in a single prompt.