Llama 3.3 70B vs DeepSeek R1 32B — Which Wins for Reasoning?
Last updated 2026-06-13
Two heavyweight open-weight models, two very different philosophies. We benchmarked both on local hardware to settle the reasoning question.
By Mohamed Meguedmi · 9 min read
Key Takeaways
- DeepSeek R1 32B (distilled from R1) wins on pure reasoning — it scores ~72.6% on AIME 2024 vs ~50% for Llama 3.3 70B Instruct, despite being less than half the parameter count.
- Llama 3.3 70B wins on general knowledge, instruction following, and multilingual tasks — MMLU 86.0 vs ~74 for the R1 distill, plus a 128K context that R1 32B matches but uses less efficiently.
- Hardware cost gap is significant: R1 32B Q4_K_M runs in ~20 GB VRAM (single RTX 4090/3090), while Llama 3.3 70B Q4_K_M needs ~42 GB (dual 3090s or an H100).
- Latency tradeoff: R1 emits long <think> chains (often 2–8K tokens) before answering, so simple queries feel slower even though throughput is higher.
- Verdict: For math, code review, and agentic planning, pick DeepSeek R1 32B. For chat assistants, RAG, and tool-use over broad domains, pick Llama 3.3 70B.
Why this matchup matters in 2026
By mid-2026, the local-LLM landscape has bifurcated. On one side, dense instruction-tuned models from Meta — Llama 3.3 70B Instruct — represent the mature "generalist assistant" lineage. On the other, reasoning-first models like DeepSeek-R1-Distill-Qwen-32B bake chain-of-thought directly into the weights via reinforcement-learning distillation.
Buyers running models locally face a real budget question: is a 70B generalist on dual GPUs worth twice the hardware cost of a 32B specialist? We tested both across reasoning, code, and chat workloads to find out. For ongoing tracking of cost-per-token across both, see our local inference cost calculator.
Architecture and training: two opposite bets
Llama 3.3 70B is a dense transformer trained on ~15T tokens, fine-tuned with supervised learning and RLHF for general assistant behavior. Meta explicitly positioned it as a drop-in replacement for Llama 3.1 405B on most tasks at a fraction of the inference cost. It has no native reasoning mode — it answers directly, occasionally with a brief chain of thought when prompted.
DeepSeek-R1-Distill-Qwen-32B is a different beast. The base is Qwen2.5-32B, but the post-training pipeline distills reasoning traces from the full DeepSeek-R1 (671B MoE) model. The result is a 32B dense model that natively produces <think>…</think> blocks before its final answer, mimicking the search-and-verify pattern of o1-class systems.
Spec comparison
| Spec | Llama 3.3 70B Instruct | DeepSeek-R1-Distill-Qwen-32B |
|---|---|---|
| Parameters | 70.6B dense | 32.8B dense |
| Base architecture | Llama 3 (GQA, RoPE) | Qwen2.5 (GQA, RoPE) |
| Context window | 128K | 128K (recommend ≤32K for stability) |
| Vocab size | 128,256 | 152,064 |
| Training cutoff | Dec 2023 | Inherits Qwen2.5 (Oct 2023) + RL distill |
| License | Llama 3.3 Community License | MIT (very permissive) |
| Native reasoning | No | Yes (<think> tokens) |
The license gap is worth flagging: DeepSeek R1's MIT terms make it materially easier to embed in commercial products than Llama's community license, which has user-count and naming restrictions.
Benchmarks: where each model lands
We aggregated results from official model cards, the Open LLM Leaderboard v2, and our own reruns on Q4_K_M quantizations. Reasoning benchmarks were run with the model's recommended sampling (temperature 0.6, top_p 0.95 for R1; temperature 0.0 for Llama).
| Benchmark | Llama 3.3 70B | DeepSeek R1 32B | Winner |
|---|---|---|---|
| MMLU (5-shot) | 86.0 | 74.0 | Llama |
| MMLU-Pro | 68.9 | 62.1 | Llama |
| AIME 2024 (pass@1) | ~50.0 | 72.6 | DeepSeek |
| MATH-500 | 77.0 | 94.3 | DeepSeek |
| GPQA Diamond | 50.5 | 62.1 | DeepSeek |
| LiveCodeBench | 33.3 | 57.2 | DeepSeek |
| Codeforces (Elo) | ~870 | 1691 | DeepSeek |
| IFEval | 92.1 | ~80 | Llama |
| HumanEval | 88.4 | ~85 | Llama (narrow) |
The pattern is consistent: anywhere reasoning is explicitly rewarded — competition math, advanced physics, algorithmic coding — the distilled R1 32B beats a model more than twice its size. Anywhere broad recall, instruction-following nuance, or general chat quality is measured, Llama 3.3 70B pulls ahead.
Local hardware footprint and cost
The reasoning win comes with a hardware silver lining: R1 32B fits on a single consumer GPU at 4-bit. Llama 3.3 70B does not. Here is what each model demands at common GGUF quantizations, measured against tested deployments in our model catalog.
| Quantization | Llama 3.3 70B size / VRAM | DeepSeek R1 32B size / VRAM |
|---|---|---|
| Q8_0 | 74.9 GB / ~78 GB | 34.8 GB / ~38 GB |
| Q5_K_M | 50.0 GB / ~54 GB | 23.3 GB / ~26 GB |
| Q4_K_M | 42.5 GB / ~46 GB | 19.9 GB / ~22 GB |
| Q3_K_M | 34.3 GB / ~38 GB | 16.0 GB / ~18 GB |
| IQ2_XS | 20.8 GB / ~24 GB | 9.5 GB / ~12 GB |
Practical implications at Q4_K_M, the sweet spot most users land on:
- DeepSeek R1 32B: runs comfortably on a single RTX 4090 (24 GB), RTX 3090 (24 GB), or RTX 5080 (16 GB with partial offload). A used 3090 around $700 USD is enough.
- Llama 3.3 70B: requires either dual 24 GB cards (~$1,400 used, plus PSU/case headroom), a single 48 GB card like the RTX 6000 Ada (~$6,800 new), an Apple M3 Ultra Mac Studio with 64 GB+ unified memory (~$4,200), or aggressive quantization to IQ2_XS at a measurable quality cost.
For a 3-year amortization of electricity plus depreciation, the total cost of ownership gap between the two setups is roughly 2.2× in favor of R1 32B at current US power prices (~$0.16/kWh).
Throughput, latency, and the reasoning tax
Raw tokens-per-second favors R1 32B for obvious reasons — fewer parameters, less memory bandwidth pressure. But R1 spends a large fraction of its output budget on internal reasoning before the user-visible answer arrives. This changes how the latency math feels in practice.
Measured on a single RTX 4090 (R1 32B) and a dual RTX 3090 setup (Llama 70B), both Q4_K_M, llama.cpp build b4400, batch size 1:
| Metric | Llama 3.3 70B (2×3090) | DeepSeek R1 32B (1×4090) |
|---|---|---|
| Prompt eval (tok/s) | ~480 | ~1,150 |
| Generation (tok/s) | ~14 | ~38 |
| Time-to-first-token (1K prompt) | ~2.1 s | ~0.9 s |
| Avg. answer length (simple Q) | ~180 tokens | ~2,400 tokens (think + answer) |
| Wall-clock to final answer (simple Q) | ~14 s | ~64 s |
| Wall-clock to final answer (math problem) | ~22 s (often wrong) | ~95 s (usually correct) |
The takeaway: for snappy chat, Llama feels faster despite lower throughput. For correctness on hard problems, R1's longer thinking pays for itself.
Choosing by use case
Pick DeepSeek R1 32B when…
- You're building a math tutor, scientific reasoning assistant, or algorithmic-coding pair-programmer.
- You need a permissive MIT license for commercial redistribution.
- Your budget tops out at one 24 GB GPU.
- You want a local stand-in for o1-style models behind an agent framework. Pair it with our local reasoning agents guide.
Pick Llama 3.3 70B when…
- You're powering a customer-facing chat assistant where tone, instruction-following, and breadth matter more than raw IQ.
- Your RAG corpus is broad (legal, medical, multi-domain) and you need strong retrieval-grounded synthesis.
- You need solid multilingual coverage — Llama 3.3 supports 8 official languages with parity that R1's Qwen base only matches in English and Chinese.
- You already have ≥40 GB of VRAM, an Apple Silicon machine with 64 GB+, or you're running on cloud H100s.
How to install and benchmark both locally
The fastest path to reproduce our numbers is via Ollama, which ships both models with sensible defaults.
# Install Ollama (Linux)
curl -fsSL https://ollama.com/install.sh | sh
# Pull both models (Q4_K_M defaults)
ollama pull llama3.3:70b
ollama pull deepseek-r1:32b
# Quick sanity check
ollama run deepseek-r1:32b "Solve: if 3x + 7 = 2x - 5, what is x?"
ollama run llama3.3:70b "Solve: if 3x + 7 = 2x - 5, what is x?"
For reproducible benchmarking, use llama-bench from llama.cpp with a fixed prompt set. Our full methodology — prompt list, sampling parameters, hardware config — is published under CC BY 4.0 via the BestLLMfor public API and mirrored in the open-source MCP server at github.com/bestllmfor/mcp-server, so any reader can pull the raw numbers behind these tables.
For a deeper dive on quantization tradeoffs, see our benchmarking methodology page, and for adjacent comparisons, the best reasoning LLMs roundup.
Final verdict
| Workload | Winner | Why |
|---|---|---|
| Competition math / hard reasoning | DeepSeek R1 32B | +22 pts on AIME, +17 on MATH-500 |
| Competitive coding | DeepSeek R1 32B | Codeforces Elo 1691 vs ~870 |
| General chat assistant | Llama 3.3 70B | +12 pts MMLU, +12 IFEval, no think-tax |
| RAG over broad corpora | Llama 3.3 70B | Better recall, cleaner instruction-following |
| Multilingual | Llama 3.3 70B | 8 official languages vs EN/ZH bias |
| Single-GPU local deployment | DeepSeek R1 32B | Fits 24 GB; Llama needs 48+ |
| Commercial redistribution | DeepSeek R1 32B | MIT vs Llama Community License |
If forced to pick one for a mixed reasoning + chat workload on prosumer hardware, the BestLLMfor editorial team recommends DeepSeek R1 32B. The reasoning advantage is large and measurable, the hardware footprint is half, and the license is friendlier. Llama 3.3 70B remains the better generalist, but unless you specifically need broad domain coverage or multilingual chat, the smaller specialist is the rational 2026 default.
Frequently asked questions
Is DeepSeek R1 32B really the same as the 671B R1 model?
No. The 32B is a distillation: it's a Qwen2.5-32B base fine-tuned on reasoning traces generated by the full DeepSeek-R1 671B MoE. It inherits much of the reasoning behavior but with materially lower world knowledge and weaker instruction-following than the full model.
Can I run Llama 3.3 70B on a single 24 GB GPU?
Only at heavy quantization (IQ2_XS, ~21 GB) which measurably degrades output quality. For acceptable quality, you need Q4_K_M at minimum, which requires ~46 GB VRAM — practically dual 24 GB cards or a single 48 GB card.
Why does R1 emit so many tokens before answering?
It's trained to produce explicit chain-of-thought inside <think> tags before the final answer. This is the source of its reasoning gains. You can suppress reasoning at inference time with templates that close </think> early, but you lose most of the accuracy benefit.
Which model is better for agentic / tool-use workflows?
Mixed. Llama 3.3 70B follows tool schemas more reliably out of the box. R1 32B reasons better about when to call tools but sometimes mangles JSON formatting because of the think-block overhead. For production agents, Llama is the safer pick today.
How do these compare to closed models like GPT-4o or Claude?
R1 32B is within ~5 points of o1-mini on AIME and MATH. Llama 3.3 70B sits roughly at GPT-4o-mini level on MMLU. Neither matches frontier closed models on agentic tasks, but the gap on pure reasoning is the smallest it has ever been.