DeepSeek R1 Distill 7B vs Qwen 3 8B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | DeepSeek R1 Distill 7B | Qwen 3 8B |
|---|---|---|
| Parameters | 7B | 8B |
| Author | DeepSeek | Alibaba |
| License | MIT | Apache 2.0 |
| 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 | reasoning | chat, general, reasoning, multilingual |
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 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.
About Qwen 3 8B
Alibaba's 8B dense model with a toggleable thinking mode and broad multilingual coverage. Punches well above its weight for an 8B and runs comfortably on a single consumer GPU. Strengths: Hybrid thinking/fast modes switchable per request, Strong multilingual performance across 119 languages, Up to 131K context via YaRN (32K native), Apache 2.0 — clean commercial use.
How they compare
DeepSeek R1 Distill 7B comes from DeepSeek and Qwen 3 8B from Alibaba, they belong to the DeepSeek and Qwen 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 7B vs 8B parameters, Qwen 3 8B is the larger of the two. 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: DeepSeek R1 Distill 7B is tuned for reasoning, while Qwen 3 8B leans toward chat, general, reasoning, multilingual. Match the model to your dominant workload rather than to raw size.
The smaller model, DeepSeek R1 Distill 7B, will generally generate tokens faster on the same hardware. For long-context work, Qwen 3 8B offers the bigger window (128k vs 32k tokens).
Memory, quantization & throughput
Across quantization levels, DeepSeek R1 Distill 7B requires Q4 ≈ 5 GB, Q5 ≈ 6 GB, Q8 ≈ 9 GB, FP16 ≈ 16 GB, while Qwen 3 8B requires Q4 ≈ 5 GB, Q5 ≈ 6 GB, Q8 ≈ 9 GB, FP16 ≈ 16 GB. In practice DeepSeek R1 Distill 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, DeepSeek R1 Distill 7B needs roughly 8 GB of system RAM to run on CPU and Qwen 3 8B about 10 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 DeepSeek R1 Distill 7B and 35 from Qwen 3 8B, 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 DeepSeek R1 Distill 7B or Qwen 3 8B to the card you actually own:
- On a 8 GB GPU: DeepSeek R1 Distill 7B runs at Q5 (6 GB); Qwen 3 8B runs at Q5 (6 GB).
- On a 12 GB GPU: DeepSeek R1 Distill 7B runs at Q8 (9 GB); Qwen 3 8B runs at Q8 (9 GB).
- On a 16 GB GPU: DeepSeek R1 Distill 7B runs at FP16 (16 GB); Qwen 3 8B runs at FP16 (16 GB).
- On a 24 GB GPU: DeepSeek R1 Distill 7B runs at FP16 (16 GB); Qwen 3 8B runs at FP16 (16 GB).
Benchmark scores
Reported benchmarks for DeepSeek R1 Distill 7B: AIME 2024 55.5, MATH-500 92.8.
Reported benchmarks for Qwen 3 8B: MMLU-Pro 68.7, GPQA 60, LiveCodeBench 54.4.
Bottom line: which should you pick?
- Pick Qwen 3 8B for long-context work (up to 128k tokens).
- Pick DeepSeek R1 Distill 7B for lower VRAM and faster inference; pick Qwen 3 8B for maximum headline quality.
- Pick Qwen 3 8B if your workload is chat, general, multilingual.
Which GPU should you buy to run DeepSeek R1 Distill 7B?
To run DeepSeek R1 Distill 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 DeepSeek R1 Distill 7B and Qwen 3 8B?
The headline differences: DeepSeek R1 Distill 7B is a 7B model and Qwen 3 8B is 8B; their context windows differ (32k vs 128k tokens); they ship under different licenses (MIT vs Apache 2.0). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can DeepSeek R1 Distill 7B and Qwen 3 8B run on a 24 GB GPU?
At a Q4 quantization, DeepSeek R1 Distill 7B needs about 5 GB of VRAM and fits comfortably on a 24 GB GPU; Qwen 3 8B needs about 5 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.
Which is faster, DeepSeek R1 Distill 7B or Qwen 3 8B?
DeepSeek R1 Distill 7B is the smaller model (7B vs 8B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
What licenses do DeepSeek R1 Distill 7B and Qwen 3 8B use?
DeepSeek R1 Distill 7B is licensed under MIT and Qwen 3 8B under Apache 2.0.
Which has the longer context window, DeepSeek R1 Distill 7B or Qwen 3 8B?
Qwen 3 8B has the larger context window (128k vs 32k tokens), so it handles longer documents and codebases in a single prompt.