Llama 4 vs Qwen 3 vs DeepSeek R1 in 2026

These are the three open-weight model families that dominate local LLM in 2026. Each has a different strength: Llama 4 is multimodal-first, Qwen 3 is the all-round best for most users, DeepSeek R1 is the reasoning leader. This page helps you pick which one to download.

Model lineup

Model	Sizes	Quantized GGUF	Best for
Llama 4 Scout	109B (17B active MoE)	✓	vision, long context (10M tokens)
Llama 4 Maverick	400B (17B active MoE)	✓	vision, instruction following
Llama 4 Behemoth	2T (not released for local)	✗	reference model only
Qwen 3 0.6B – 32B	dense	✓	general, chat, agentic
Qwen 3 30B-A3B	30B (3B active MoE)	✓	chat, speed
Qwen 3 235B	235B (22B active MoE)	✓	frontier-class, needs 128GB+
Qwen 3-Coder 30B / 480B	30B / 480B	✓	coding agents
DeepSeek R1 distilled	1.5B – 70B	✓	reasoning on small hardware
DeepSeek R1 full	671B (37B active)	✓	frontier-class reasoning

Benchmarks (MMLU, GSM8K, HumanEval, MMLU-Pro)

Model	MMLU	GSM8K	HumanEval	MMLU-Pro
Llama 4 Scout 17B	76	88	78	64
Qwen 3 32B	81	92	82	70
Qwen 3 30B-A3B	79	90	80	67
DeepSeek R1 distilled 32B	78	95	80	68
DeepSeek R1 671B (full)	89	96	92	84

Llama 4’s MoE design trades raw accuracy for inference speed. Qwen 3 wins on the cost/quality curve for most users. DeepSeek R1 wins on reasoning.

Hardware fit

Model	Min VRAM (Q4)	Recommended	Tokens/sec on 3090
Llama 3.2 1B	1 GB	4 GB	95
Llama 3.2 3B	3 GB	6 GB	65
Qwen 2.5 3B	3 GB	6 GB	68
Llama 3.1 8B	5 GB	8 GB	42
Qwen 2.5 7B	5 GB	8 GB	48
Mistral Nemo 12B	8 GB	12 GB	28
Qwen 3 14B	9 GB	12 GB	30
DeepSeek R1 distilled 14B	9 GB	12 GB	32
Qwen 3 32B	20 GB	24 GB	16
DeepSeek R1 distilled 32B	20 GB	24 GB	17
Llama 4 Scout 109B (offloaded)	48 GB	64 GB+	6
DeepSeek R1 671B (offloaded)	256 GB	384 GB	2

When to use each

Choose Llama 4 when…

• You need multimodal (image + text in the same conversation).
• You need long context (10M tokens with Scout, useful for big codebases or full books).
• You are an enterprise buyer who needs Meta’s license terms and compliance support.

Choose Qwen 3 when…

• You want the best all-round model for a single desktop GPU.
• You do coding, agentic workflows, or general chat — Qwen 3 30B is the strongest in its size class.
• You need multilingual (Chinese + English + 100+ languages).
• You want the largest model ecosystem with 0.6B through 235B sizes.

Choose DeepSeek R1 when…

• You do math, science, or step-by-step reasoning.
• You want to see the chain-of-thought and learn from it.
• You are on a single 24GB GPU and want the best reasoning — the 32B distilled R1 is excellent.
• You need “thinking” mode in your application — R1’s reasoning trace is a feature.

Quick start

# All three are available in Ollama with the same CLI
ollama run llama4:scout
ollama run qwen3:32b
ollama run deepseek-r1:32b

# Or with Mullama (drop-in Ollama)
mullama run llama4:scout
mullama run qwen3:32b
mullama run deepseek-r1:32b

Decision matrix

Your hardware	Best fit
8 GB VRAM	Llama 3.1 8B or Qwen 2.5 7B
12 GB VRAM	Qwen 3 14B or DeepSeek R1 14B
16 GB VRAM	Qwen 3 14B at Q8, or Mistral Nemo 12B
24 GB VRAM (3090/4090)	Qwen 3 32B, DeepSeek R1 32B, or Llama 4 Scout 17B (MoE)
48 GB VRAM (A6000, M3 Ultra)	Llama 4 Maverick offloaded, or Qwen 3 32B at FP16
128 GB+ (M3 Ultra, multi-GPU)	DeepSeek R1 671B
16 GB Mac (M1/M2 Pro)	Qwen 2.5 7B or Llama 3.1 8B in MLX
32 GB Mac (M2/M3/M4 Max)	Qwen 3 14B or Mistral Nemo 12B in MLX
64+ GB Mac (M2/M3/M4 Ultra)	Qwen 3 32B in MLX

See also

• Best local LLM 2026 — broader roundup
• Best local LLM for coding 2026 — coding-specific recommendations
• Local LLM on RTX 3090 — 24GB-VRAM build guide
• How to choose a local LLM — full decision framework