DeepSeek R1 Distill 32B vs QwQ 32B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | DeepSeek R1 Distill 32B | QwQ 32B |
|---|---|---|
| Parameters | 32B | 32B |
| Author | DeepSeek | Alibaba |
| License | MIT | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 19 GB | 19 GB |
| VRAM at Q5 | 23 GB | 23 GB |
| VRAM at Q8 | 35 GB | 35 GB |
| VRAM at FP16 | 64 GB | 64 GB |
| Use cases | reasoning | 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 DeepSeek R1 Distill 32B
The 32B DeepSeek R1 distill — the best accessible open-weight reasoner we've tested. Explicit chain-of-thought, MIT-licensed, runs on a single 24GB GPU. Strengths: Best open-weight reasoner that fits on one consumer GPU, Excellent math and science performance, Explicit step-by-step thinking, MIT license.
About QwQ 32B
Alibaba's dedicated 32B reasoner, trained with reinforcement learning rather than distillation. Hits 79.5 on AIME24 and 90.6 on MATH-500 — a direct Apache-licensed alternative to DeepSeek R1. Strengths: Direct competitor to DeepSeek R1 at a fraction of the size, 131K context for long thinking traces, Trained with RL, not just distilled, Apache 2.0.
How they compare
DeepSeek R1 Distill 32B comes from DeepSeek and QwQ 32B 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.
DeepSeek R1 Distill 32B and QwQ 32B share the same 32B parameter class. Both need about 19 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.
Where they overlap on benchmarks, QwQ 32B takes AIME 2024 with 79.5 against 72.6 — a clear 6.9-point margin. On MATH-500 the edge goes to DeepSeek R1 Distill 32B (94.3 vs 90.6). For workloads weighted toward that benchmark, QwQ 32B is the stronger default.
For long-context work, QwQ 32B offers the bigger window (128k vs 32k tokens).
Memory, quantization & throughput
Across quantization levels, DeepSeek R1 Distill 32B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 64 GB, while QwQ 32B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 64 GB. In practice DeepSeek R1 Distill 32B wants a 24 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 32B needs roughly 32 GB of system RAM to run on CPU and QwQ 32B about 32 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 12 tokens/sec from DeepSeek R1 Distill 32B and 12 from QwQ 32B, scaling up to 30 and 30 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 32B or QwQ 32B to the card you actually own:
- On a 24 GB GPU: DeepSeek R1 Distill 32B runs at Q5 (23 GB); QwQ 32B runs at Q5 (23 GB).
Benchmark scores
Reported benchmarks for DeepSeek R1 Distill 32B: AIME 2024 72.6, MATH-500 94.3, GPQA 62.1.
Reported benchmarks for QwQ 32B: AIME 2024 79.5, MATH-500 90.6.
Bottom line: which should you pick?
- Pick QwQ 32B for long-context work (up to 128k tokens).
- Pick QwQ 32B if AIME 2024 performance is your priority (79.5 vs 72.6).
Which GPU should you buy to run DeepSeek R1 Distill 32B?
To run DeepSeek R1 Distill 32B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Frequently asked questions
What is the difference between DeepSeek R1 Distill 32B and QwQ 32B?
The headline differences: both are 32B models; 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 32B and QwQ 32B run on a 24 GB GPU?
At a Q4 quantization, DeepSeek R1 Distill 32B needs about 19 GB of VRAM and fits comfortably on a 24 GB GPU; QwQ 32B needs about 19 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.
Is DeepSeek R1 Distill 32B or QwQ 32B more capable?
On AIME 2024, QwQ 32B scores higher (79.5 vs 72.6), a 6.9-point advantage on this benchmark.
What licenses do DeepSeek R1 Distill 32B and QwQ 32B use?
DeepSeek R1 Distill 32B is licensed under MIT and QwQ 32B under Apache 2.0.
Which has the longer context window, DeepSeek R1 Distill 32B or QwQ 32B?
QwQ 32B has the larger context window (128k vs 32k tokens), so it handles longer documents and codebases in a single prompt.