The three model families that split every “best open-weight 2026” argument are Meta’s Llama 3.3, Alibaba’s Qwen3, and Mistral’s portfolio. Each pulls from a different philosophy: Meta builds for English-first reasoning at flagship scale, Alibaba optimizes for coding and multilingual density, Mistral trades total parameter count for speed-per-VRAM-dollar. For home lab users, the question is which one actually makes sense for the GPU sitting under your desk.

Start with the reality check nobody puts in the headline: Mistral Large is not a practical local AI model for most home labs. Mistral Large 2 weighs in at 123B parameters and needs roughly 73 GB of VRAM at Q4_K_M quantization — four RTX 4090s, or a pair of 48 GB professional cards. If you’re searching “Mistral Large vs Llama locally,” the real answer is that Mistral’s home-lab champion is Mistral Small 3.2 at 24B, and that’s the model this comparison runs against the other two families.

The three models you’re actually comparing

Model	Architecture	Parameters	Context	License
Llama 3.3 70B Instruct	Dense	70B	128K	Llama 3.3 Community
Qwen3.6-27B	Dense	27B	262K native	Apache 2.0
Qwen3.6-35B-A3B	MoE (3B active)	35B total / 3B active	262K native	Apache 2.0
Mistral Small 3.2 24B	Dense	24B	128K	Apache 2.0
(for reference) Mistral Large 2	Dense	123B	128K	Mistral Research

Qwen3.6, released April 2026, is the current iteration of Alibaba’s Qwen3 family — it supersedes the original Qwen3-32B and Qwen3-30B-A3B from May 2025 with stronger coding performance and a 262K native context window. The 27B dense model emphasizes quality; the 35B-A3B MoE model activates only 3B parameters per token, making inference cost equivalent to a 3B dense model.

Mistral Small 3.2 (released June 2025) is a direct weight-upgrade of Small 3.1 using the same 24B base — Mistral retrained the instruct layer to fix instruction drift, reduce infinite-generation bugs, and improve function calling. The underlying architecture is identical; the benchmark gains are purely from the instruction-tuning refresh.

VRAM requirements and which GPU tier handles each model

This is where the comparison shapes itself by hardware.

Model	Q4_K_M VRAM	Q6_K VRAM	Min consumer GPU	Notes
Llama 3.3 70B	~40 GB	~53 GB	Dual RTX 3090/4090 or Mac M4 Max 64GB	Single 24GB GPU requires CPU offloading
Qwen3.6-27B	~17 GB	~22 GB	RTX 3090 / RTX 4090	22 GB at Q4 — tight on 24GB, comfortable with short context
Qwen3.6-35B-A3B	~16–22 GB	~27 GB	RTX 3090 / RTX 4090	Activates only 3B parameters per token; fast on 24GB
Mistral Small 3.2 24B	~13.4 GB	~18 GB	RTX 4060 Ti 16GB or better	Only 24B model in this list that fits a 16GB GPU

Llama 3.3 70B on a single RTX 4090 (24GB) is a compromised configuration. At Q4_K_M, roughly 40 GB of weights must split between GPU VRAM and system RAM. With layers offloaded to CPU, effective throughput drops to 8–15 tokens/second — a miserable experience for interactive chat. Running Q2_K instead (about 20 GB, fits entirely in 24GB) recovers some speed at ~18 tok/s, but Q2 quantization introduces quality degradation that narrows Llama 3.3’s benchmark advantages.

If your GPU is a single RTX 3090 or RTX 4090 (24GB), the Qwen3.6 models are the practical choice, not Llama 3.3. Llama 3.3 earns its place on dual 24GB setups, Mac M4 Max 64GB (where 40GB model weights fit comfortably in unified memory), or any 48GB+ card.

Quality benchmarks: what the numbers actually say

Benchmark	Llama 3.3 70B	Qwen3.6-35B-A3B	Mistral Small 3.2 24B
MMLU	86.0%	—	81%
MMLU-Pro	—	85.2	—
HumanEval (coding)	88.4%	—	92.9%
MATH	77.0%	—	—
SWE-bench Verified	—	73.4%	—
IFEval (instruction follow)	92.1%	—	—
Arena Hard v2	—	—	43.1%

A few things worth unpacking here.

Llama 3.3 70B leads on MMLU (86%) and instruction following (IFEval 92.1%). Meta’s training pipeline is optimized for English academic benchmarks and precise instruction adherence. That matters for general-purpose chat, multi-step reasoning, and use cases where following a complex system prompt correctly is non-negotiable.

Qwen3.6-35B-A3B’s 73.4% on SWE-bench Verified is a tier-1 software engineering result. SWE-bench Verified measures real pull-request resolution on GitHub codebases — the hardest practical coding benchmark currently available. 73.4% puts the model alongside frontier API models. At 35B total / 3B active, it achieves this running on a single RTX 3090. The 27B dense sibling (Qwen3.6-27B) scores even higher at 77.2% on SWE-bench — a dense model with deeper per-token compute outperforming the MoE variant on quality while trading away speed.

Mistral Small 3.2 24B’s 92.9% HumanEval outperforms Llama 3.3 70B’s 88.4% on the same benchmark, despite 46B fewer parameters. Mistral’s instruction-tuning refresh specifically targeted coding tasks and function calling. The model also supports 128K context with stronger multilingual performance for European languages — French, Spanish, German, Italian, Portuguese — than Llama 3.3, which has 8 supported languages but weaker quality on the non-English ones.

One important caveat: Qwen3 models support two inference modes — “thinking mode” (internal chain-of-thought reasoning enabled) and non-thinking mode. Thinking mode substantially improves scores on reasoning and coding benchmarks at the cost of higher latency and more tokens generated. The benchmarks above reflect non-thinking mode; with thinking mode, Qwen3 scores improve significantly on math and code.

Real-world inference speed by GPU tier

These are generation speeds (tokens per second output), not prompt processing speeds, measured at standard Q4_K_M quantization with the default context length.

GPU	Llama 3.3 70B	Qwen3.6-27B	Qwen3.6-35B-A3B	Mistral Small 3.2 24B
RTX 4060 Ti 16GB	❌ Won’t fit Q4	❌ Tight / partial offload	~30–40 tok/s (MoE fits)	~35–45 tok/s
RTX 3090 / 4090 (24GB)	8–15 tok/s (CPU offload)	~43 tok/s	~107–135 tok/s	~30–50 tok/s
Dual RTX 3090 / 4090 (48GB)	~35–50 tok/s	~60 tok/s	~120–140 tok/s	~50 tok/s
Mac M4 Max 128GB	~30–40 tok/s	~45 tok/s	~90 tok/s	~50 tok/s

The Qwen3.6-35B-A3B speed numbers deserve a second look: 107–135 tokens per second on a single RTX 3090. Because the MoE model activates only 3B parameters per token, the memory bandwidth bottleneck that cripples 70B inference barely applies. The 35B total weights sit in VRAM (16–22 GB at Q4), and each forward pass touches only the activated 3B slice. The throughput is closer to a 3B model than a 35B one. Community benchmarks on the RTX 3090 using Ollama 0.20.x with Q4_K_M confirmed approximately 107 tok/s; llama.cpp with UD-Q4_K_XL quantization reached ~135 tok/s on the same card.

For comparison, Llama 3.3 70B on the same single RTX 3090 at Q4_K_M requires CPU offloading and produces 8–15 tok/s. That’s a 10× speed gap on identical hardware, with the MoE model delivering better SWE-bench scores.

If speed matters for interactive use — autocomplete, fast iteration, shared family server, running multiple sessions — the 35B-A3B is the obvious pick for 24GB GPU owners.

Use-case decision matrix

Use case	Best pick	Runner-up	Reasoning
General English chat	Llama 3.3 70B	Mistral Small 3.2	Highest MMLU + IFEval; best instruction adherence
Coding assistant / autocomplete	Qwen3.6-27B	Mistral Small 3.2 24B	SWE-bench 77.2%; thinking mode optional
Agentic coding / repo-level tasks	Qwen3.6-35B-A3B	Qwen3.6-27B	SWE-bench 73.4%, fast output for tool-use loops
Very long documents (>64K tokens)	Qwen3.6 (either)	—	262K native context; Llama + Mistral top out at 128K
Multilingual (EU languages)	Mistral Small 3.2	Qwen3.6	Mistral explicitly optimized for French, Spanish, German, Italian, Portuguese
Speed-sensitive or high concurrency	Qwen3.6-35B-A3B	Mistral Small 3.2	MoE activates 3B/pass; Mistral runs 3× faster than Llama on same hardware
16GB GPU (RTX 4060 Ti / 5060 Ti)	Mistral Small 3.2 24B	—	Only viable choice; others don’t fit at Q4
24GB GPU (RTX 3090 / 4090)	Qwen3.6-35B-A3B	Qwen3.6-27B	Speed + quality at full VRAM utilization
48GB+ or dual 24GB	Llama 3.3 70B	Qwen3.6-27B	Finally has the VRAM to run without CPU offload
Math and scientific reasoning	Llama 3.3 70B	Qwen3.6 (thinking mode)	MATH 77.0% for Llama; Qwen3 thinking mode competitive
Fine-tuning (QLoRA)	Mistral Small 3.2 24B	Qwen3.6-27B	Lower VRAM floor = cheaper RTX 3090 / 4090 setup

Honest take

If your GPU is an RTX 3090 or RTX 4090 (24GB), Llama 3.3 70B is the wrong answer unless you’re willing to live with CPU offloading. At 8–15 tok/s, it’s functional but slow for interactive use. The Qwen3.6-35B-A3B delivers better software engineering benchmarks and 10× faster generation on the same card. For general chat where you prefer Llama’s English quality and can tolerate lower speed, Mistral Small 3.2 runs at 30–50 tok/s and sits only 5 MMLU points behind Llama 3.3 despite being half the parameter count.

Llama 3.3 70B earns its reputation once you have 48GB+ of VRAM. On a Mac M4 Max 64GB, dual RTX 3090s, or a single RTX 6000 Ada 48GB, the model fits fully in GPU memory and delivers its intended performance. Meta’s training still leads on English instruction following and IFEval — if you’re building a home assistant that needs to follow complex system-prompt logic reliably, Llama 3.3 is the better base than either Qwen or Mistral Small.

Mistral Small 3.2 24B is the underdog worth tracking. Its 92.9% HumanEval outscores Llama 3.3 70B despite 46B fewer parameters, it fits on a 16GB GPU, and it’s the best choice for users serving multilingual European-language content. The instruction-tuning refresh that landed in the 3.2 release fixed the repetition bug that made 3.1 frustrating in long conversations. At 13.4 GB Q4_K_M, you can run it comfortably on an RTX 4060 Ti 16GB or RTX 5060 Ti 16GB — see our RTX 5060 Ti 16GB benchmark piece for realistic Ollama throughput on that card.

On the Mistral Large question: if your use case genuinely needs Mistral Large’s frontier-class performance (French legal documents, complex European multi-language workflows), and you have a multi-GPU workstation with 96–192 GB of VRAM, it runs. For everyone else, Mistral Small 3.2 covers 90% of the same territory. Mistral’s own release notes describe Mistral Small 3.1 as rivaling models “three times larger” — the gap between the 24B and 123B in practical home-lab tasks is smaller than the parameter count suggests.

The emerging option nobody expected: Qwen3.6-35B-A3B for coding workflows on a 24GB card. 73.4% SWE-bench from a model that fits on an RTX 3090 and runs at 100+ tok/s was not on the roadmap six months ago. If you’re using Ollama as a local coding backend for Continue.dev or a similar editor plugin, this is currently the best single-GPU answer.

Choosing your setup

• 16GB GPU (RTX 4060 Ti, RTX 5060 Ti 16GB): Mistral Small 3.2 24B at Q4_K_M (~13.4 GB). The only viable option in this list that fits.
• 24GB GPU (RTX 3090, RTX 4090): Qwen3.6-35B-A3B for coding/speed-sensitive work; Qwen3.6-27B for highest-quality output; Mistral Small 3.2 as a fast general-purpose alternative.
• 48GB+ / dual 24GB: Llama 3.3 70B finally makes sense here. Pair it with Ollama in a multi-user setup and let the household benefit from top-tier English reasoning.
• Don’t have a GPU or need cloud backup: RunPod offers RTX 4090 instances where you can test all three at no upfront cost before committing hardware money — useful if you want to benchmark your specific workload before buying.

For hardware context on what 24GB GPUs cost in May 2026, see our GPU buying guide and the RTX 3090 value analysis. For electricity cost considerations on a 24/7 inference server, the power bill math article covers full TCO.

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Sources

• What Is Meta’s Llama 3.3 70B? — DataCamp
• Llama 3.3 70B VRAM and CPU-offload performance — LocalLLM.in
• Qwen3.6-35B-A3B: 73.4% SWE-Bench, Runs Locally — BuildFastWithAI
• Qwen3 Technical Report — arXiv 2505.09388
• Qwen3.6-27B vs 35B-A3B Benchmark Data — LLM Stats
• Mistral just updated Small 3.1 to 3.2 — VentureBeat
• Mistral Small 3 official announcement — Mistral AI
• Qwen3.6-27B RTX 4090 benchmark recipes — GitHub outsourc-e
• Qwen3.6-35B-A3B RTX 3090 tok/s discussion — Hugging Face
• Mistral Large 2 VRAM requirements — Hardware Corner
• Introducing Mistral Small 4 (119B MoE) — Mistral AI
• Qwen3 model family GPU requirements — WillItRunAI

Last updated May 19, 2026. Prices and specs change; verify current rates before purchasing.

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Recommended Gear

The hardware mentioned in this guide, with current prices on Amazon (affiliate links — at no extra cost to you, purchases help support this site):

• RTX 4090
• RTX 3090
• RTX 4060 Ti
• RTX 4060
• RTX 5060 Ti
• RTX 5060