This article was originally published on runaihome.com

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

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.

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

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.

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