DeepSeek R1 671B vs R1 32B Distill — Quality Gap Real Math
Last updated 2026-06-30
We put the full 671B reasoner against the 32B distill on AIME, MATH-500 and GPQA, then weigh the gap against what each one costs to run.
By Mohamed Meguedmi · 8 min read
Key takeaways
- The math gap is real but uneven. On hard competition math (AIME 2024) the full R1 671B beats the 32B distill by 7.2 points (79.8% vs 72.6%). On the easier, near-saturated MATH-500 the gap shrinks to just 3.0 points (97.3% vs 94.3%).
- The distill is not a shrunk R1. It is a Qwen2.5-32B base fine-tuned on R1's outputs (SFT only, no reinforcement learning), so it imitates the reasoning style without the full search depth.
- Hardware tells the real story. The 32B distill runs in ~20 GB on a single RTX 3090/4090. The 671B model needs ~380–450 GB of memory and 10–40x the hardware budget.
- Reasoning models pay a token tax. R1 emits thousands of thinking tokens per answer. At 1–8 tok/s on RAM-offloaded 671B, a single AIME problem can take minutes; the 32B distill clears it in seconds.
- Verdict: Run the 32B distill on 24 GB hardware for 90%+ of local work. Reserve the 671B for research-grade math, hard agentic chains, or when a 7-point AIME swing is worth a five-figure platform.
The numbers nobody disputes
Both models come from DeepSeek's January 2025 release and were measured on the same harness in the official DeepSeek-R1 paper. We are comparing DeepSeek-R1 (671B total parameters, ~37B active per token in a Mixture-of-Experts layout) against DeepSeek-R1-Distill-Qwen-32B (a 32.8B dense model). Here are the headline pass@1 figures.
| Benchmark (pass@1) | R1 671B | R1-Distill-Qwen-32B | Gap |
|---|---|---|---|
| AIME 2024 | 79.8% | 72.6% | 7.2 pp |
| MATH-500 | 97.3% | 94.3% | 3.0 pp |
| GPQA Diamond | 71.5% | 62.1% | 9.4 pp |
| LiveCodeBench | 65.9% | 57.2% | 8.7 pp |
| Codeforces (Elo) | 2029 | 1691 | 338 |
For context, the 32B distill already edges out OpenAI's o1-mini (63.6% AIME, 90.0% MATH-500) on every line here. That is the part most buyers miss: the “small” option is already a frontier-class reasoner. The question is not whether the distill is good — it is whether the remaining gap to 671B justifies the hardware.
The “real math” gap: saturation hides the truth
MATH-500 is the benchmark you will see quoted most, because it produces the friendliest headline — “within 3 points of the full model.” That number is technically true and practically misleading. MATH-500 is approaching saturation: when a test maxes out near 97%, every model clusters at the top and differences compress. It can no longer separate a strong reasoner from an exceptional one.
AIME 2024 is where real math lives. These are 15 short-answer olympiad problems that reward multi-step planning, backtracking, and self-correction — exactly the behaviors reinforcement learning instills in the full R1. Here the gap nearly triples to 7.2 points. GPQA Diamond (graduate-level science) widens it further to 9.4 points. The pattern is consistent: the harder the problem, the more the distill falls behind.
Rule of thumb: on saturated tests the distill looks nearly identical to 671B; on frontier-difficulty reasoning the full model pulls away by 7–10 points. Your workload decides which regime you live in.
If your local math is high-school-to-undergrad (the MATH-500 regime), the distill is functionally equal. If you are doing competition math, research-grade derivations, or long agentic proof chains where a single early error cascades, those 7–10 points are the difference between a usable answer and a confidently wrong one. We track these splits on the benchmarks hub, and every figure on this page is queryable through the BestLLMfor public API (CC BY 4.0) or our open-source MCP server.
Why the distill is not a smaller R1
This is the single most misunderstood point, and a popular r/LocalLLaMA thread put it bluntly. The distills are separate base models — Qwen2.5 and Llama-3 — fine-tuned on roughly 800K reasoning traces generated by the full R1. They are students imitating a teacher, not a compressed copy of the teacher.
Two consequences follow. First, the distill received supervised fine-tuning only; DeepSeek deliberately skipped the RL stage that gave 671B its self-correcting search behavior. The distill learned what good reasoning looks like, not the full policy that discovers it. Second, the distill inherits its base model's blind spots. When the 32B distill fails an AIME problem, it often fails by committing early to a flawed path and never backtracking — the precise capability RL was meant to add.
That is why “retains 96% of MATH-500 quality” and “is a 96%-as-good model” are different claims. The distill retains most of the easy quality and a shrinking fraction of the hard quality.
Hardware and cost reality
Benchmarks are free; running the models is not. This table is where the decision is usually made for you.
| Spec | R1 671B (IQ4_K_M) | R1-Distill-Qwen-32B (Q4_K_M) |
|---|---|---|
| Parameters | 671B total / ~37B active (MoE) | 32.8B dense |
| Weights on disk | ~404 GB | ~20 GB |
| Min memory to run | ~380–450 GB | ~22–24 GB |
| Typical platform | 512 GB Mac Studio M3 Ultra, 8× A100/H100, or EPYC + 768 GB DDR5 | Single RTX 3090 / 4090 / 5090 |
| Approx. hardware cost | $9,500 – $30,000+ | $700 – $1,900 |
| Realistic throughput | 1–8 tok/s | 30–50 tok/s |
A few people run 671B at home via Unsloth's 1.58-bit dynamic quant, squeezing weights to ~140 GB. It boots, but aggressive quantization erodes exactly the hard-reasoning edge you bought the model for — so you pay 671B prices for sub-671B quality. The 32B distill, by contrast, is a one-line pull from ollama.com and saturates a single consumer GPU. Model the full picture with our cost calculator before committing to a 512 GB platform.
The reasoning-token tax
Throughput matters more for R1 than for any non-reasoning model, because R1 thinks out loud. A single hard problem routinely generates 2,000–10,000 tokens inside the <think> block before the final answer appears. Do the arithmetic:
- 32B distill at 40 tok/s: a 4,000-token chain finishes in ~100 seconds.
- 671B at 4 tok/s (RAM-offloaded): the same chain takes ~17 minutes.
On a proper 8×H100 node the 671B flies, but that is a data-center bill, not a desk. For interactive local use — the entire premise of running models locally — the distill's speed advantage compounds the cost advantage. You can run ten distill attempts (and self-consistency vote) in the time one offloaded 671B answer arrives, which often closes the quality gap in practice.
Which one should you actually run?
Match the model to the workload, not to the leaderboard.
| Your situation | Run this | Why |
|---|---|---|
| 24 GB GPU, general dev + reasoning | R1-Distill-Qwen-32B | Beats o1-mini, ~20 GB, 40 tok/s, near-parity on MATH-500 |
| 12 GB GPU | R1-Distill-Qwen-14B | 69.7% AIME / 93.9% MATH-500 in ~10 GB |
| Competition math, research proofs | R1 671B | +7–10 pp on AIME/GPQA where errors cascade |
| Long agentic / multi-step chains | R1 671B (if you have the platform) | RL-trained backtracking prevents early-commit failures |
| Privacy-bound but math is undergrad-level | R1-Distill-Qwen-32B | Functionally equal in the MATH-500 regime, 1/15th the cost |
Browse size-matched alternatives in the model catalog, and see our methodology for how we normalize these scores.
Verdict
For local, on-device use, the R1-Distill-Qwen-32B is the correct default for the overwhelming majority of readers. It delivers frontier reasoning on a $700–$1,900 GPU, runs fast enough to be interactive, and trails the full model by a margin that only opens up on the hardest tier of problems. The full 671B is a specialist instrument: genuinely better at olympiad math and graduate science, but only if you can feed it 400+ GB of fast memory — and only if you avoid the deep quants that would have surrendered its edge anyway. Pay for 671B when a 7-point AIME swing changes the outcome of your work. Otherwise the distill wins on every axis that a local deployment actually optimizes for.
Frequently asked questions
Is DeepSeek R1 32B distill the same as a quantized R1 671B?
No. The 32B distill is a Qwen2.5-32B base model fine-tuned on ~800K reasoning traces from the full R1. It is a separate model imitating R1's outputs, not a compressed copy. A quantized 671B is the real R1 with lower-precision weights; the distill is a different architecture entirely.
How big is the real quality gap on math?
On AIME 2024 (hard competition math) the full R1 leads 79.8% vs 72.6% — a 7.2-point gap. On the near-saturated MATH-500 it narrows to 3.0 points (97.3% vs 94.3%). The harder the math, the wider the gap, reaching 9.4 points on GPQA Diamond.
Can I run R1 671B on a single 24 GB GPU?
Not meaningfully. Even at 4-bit the model needs ~380–450 GB of memory. Unsloth's 1.58-bit dynamic quant drops it near 140 GB, but that still exceeds consumer GPUs and degrades the hard-reasoning quality you wanted the 671B for. A single 24 GB card runs the 32B distill comfortably instead.
Why is the 32B distill so much faster in practice?
It fits entirely in GPU VRAM (~20 GB) and runs at 30–50 tok/s. The 671B, when offloaded to system RAM, often runs at 1–8 tok/s. Because R1 generates thousands of reasoning tokens per answer, that throughput difference can mean seconds versus many minutes per question.
Is the distill good enough to replace o1-mini?
Yes. DeepSeek-R1-Distill-Qwen-32B outscores OpenAI's o1-mini on AIME (72.6% vs 63.6%), MATH-500 (94.3% vs 90.0%), and LiveCodeBench (57.2% vs 53.8%), while running fully offline on consumer hardware.
For running local LLMs comfortably, an RTX 5070 Ti (16 GB VRAM) is the best value for money.
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