Guide · 2026-07-04

Llama 4 Scout vs Qwen 3 235B-A22B — Open-Weight Flagship 2026

Last updated 2026-07-04

Two MoE heavyweights, two very different bets. We break down benchmarks, VRAM sizing, token cost and licensing to name a winner for 2026.

By Mohamed Meguedmi · 9 min read

The two contenders at a glance

Both models are sparse Mixture-of-Experts (MoE) designs, but they sit at opposite ends of the density spectrum. Llama 4 Scout (Meta) is a lean ~109B-parameter model that activates only 17B parameters per token across 16 experts, and it was engineered around one headline feature: a 10-million-token context window. Qwen3-235B-A22B (Alibaba’s Qwen team) is a far larger 235B-parameter model activating 22B parameters (the “A22B”) from 128 experts, tuned for maximum reasoning and coding accuracy. The July 2025 “2507” refresh split it into dedicated Instruct and Thinking variants.

Attribute	Llama 4 Scout	Qwen3-235B-A22B (2507)
Vendor	Meta	Alibaba Cloud / Qwen
Architecture	MoE, 16 experts	MoE, 128 experts (8 active)
Total parameters	~109B	235B
Active parameters/token	17B	22B
Native context window	10,000,000 tokens	256K (1M via YaRN)
Multimodal	Yes (native image)	Text (VL variant separate)
License	Llama 4 Community License	Apache-2.0
Released	2025	Base 2025, 2507 refresh

The architectural gap explains everything downstream. Scout keeps active compute low, so it is cheap and fast; Qwen3 spends more active parameters and more experts on each token, buying accuracy at the cost of memory and price.

Benchmarks: where each model actually leads

Head-to-head benchmark aggregators are near-unanimous: the two models split the scorecard, and the split is not random. Scout takes the broad-knowledge and multilingual tests; Qwen3 takes the hard reasoning and code tests. Based on published aggregate data, Qwen3-235B-A22B leads on four benchmarks (LiveCodeBench, MATH, MBPP, MMLU) while Scout edges ahead on three (GPQA, MGSM, MMLU-Pro).

Benchmark	Llama 4 Scout	Qwen3-235B-A22B	Edge
GPQA Diamond	~74%	77.2%	Qwen3
MMLU-Pro	Lead	Close	Scout
MMLU	Close	Lead	Qwen3
LiveCodeBench	Behind	Lead	Qwen3
MATH	Behind	Lead	Qwen3
MBPP	Behind	Lead	Qwen3
MGSM (multilingual)	Lead	Close	Scout

Read the pattern, not the individual rows. If your workload is reasoning-dense—competition math, agentic coding, scientific Q&A—Qwen3-235B-A22B (especially the Thinking 2507 build) is measurably stronger. If your workload is knowledge-broad and multilingual, Scout is competitive and does it with a fraction of the active compute. For the full methodology behind how we normalize these scores, see our methodology page, and browse raw numbers on the benchmarks hub.

Numbers move with every quantization and every point release. Always confirm against the current HuggingFace model card before committing hardware budget.

Hardware and VRAM sizing

This is where the decision often gets made for you. Total parameter count—not active parameters—drives memory footprint, because every expert weight must be resident. At Q4_K_M quantization the two models live in different hardware tiers.

Model @ Q4_K_M	Weights (approx.)	Practical VRAM target	Reference hardware
Llama 4 Scout	~59 GB	72–80 GB	1× H100 80GB / 1× A100 80GB
Qwen3-235B-A22B	~125 GB	140–160 GB	2× H100 80GB, 4× A6000 48GB, or M3 Ultra 512GB

The consequence is stark: Scout is a single-accelerator model that a well-specced server or a 96 GB Apple silicon machine can host comfortably, leaving headroom for a long KV cache. Qwen3-235B-A22B is a multi-GPU deployment or a 512 GB unified-memory Mac Studio. Both benefit from their low active-parameter counts at inference time—17B and 22B active respectively—so token-generation speed is closer than the size gap suggests once the weights are loaded. Use the cost calculator to model tokens-per-second against your specific GPU count.

Installing each model locally

Both are one command away with a modern runtime. Confirm the exact tag on the vendor page first.

# Llama 4 Scout (Q4_K_M)
ollama pull llama4:scout

# Qwen3 235B-A22B Instruct (2507)
ollama pull qwen3:235b-a22b-instruct-2507-q4_K_M

For production serving, vLLM or SGLang will outperform a desktop runtime on batched throughput; llama.cpp remains the pragmatic choice for single-user Apple silicon hosting.

Cost of tokens at scale

On hosted inference the price gap is decisive. Published API rates put Scout at $0.15/M input and $0.60/M output, versus Qwen3-235B-A22B at $0.70/M input and $2.80/M output.

Metric	Llama 4 Scout	Qwen3-235B-A22B
Input / M tokens	$0.15	$0.70
Output / M tokens	$0.60	$2.80
Blended (3:1 in/out)	~$0.26	~$1.23
Relative blended cost	1.0×	~4.7×

On a blended 3:1 input/output basis Scout is roughly 79% cheaper per token. At production volume—millions of requests per day—that ratio compounds into a different order of magnitude on the monthly invoice. Qwen3 has to justify that premium with accuracy your product can actually monetize.

Licensing and ecosystem

For businesses this line item can outweigh benchmarks. Qwen3-235B-A22B ships under Apache-2.0—permissive, commercially clean, no user-count trigger. Llama 4 Scout uses the Llama 4 Community License, which grants broad commercial use but adds an acceptable-use policy and a clause requiring a separate license once your product crosses 700 million monthly active users. That threshold is irrelevant to almost everyone, but legal and compliance teams still need to sign off on the AUP and attribution requirements.

Both enjoy first-class ecosystem support: GGUF quants, vLLM/SGLang kernels, LoRA fine-tuning stacks and broad tooling coverage. Qwen3’s dedicated Instruct and Thinking 2507 variants give you an explicit lever to trade latency for depth of reasoning without swapping model families.

Which one should you deploy?

There is no single winner—there is a winner for your workload.

Your priority	Winner	Why
Long-context RAG / large codebases	Llama 4 Scout	10M-token window is unmatched
Agentic coding / hard reasoning	Qwen3-235B-A22B	Leads LiveCodeBench, MATH, GPQA
Cost per token at scale	Llama 4 Scout	~79% cheaper blended
Single-GPU self-hosting	Llama 4 Scout	Fits 80GB at Q4_K_M
Clean permissive license	Qwen3-235B-A22B	Apache-2.0, no MAU clause
Native multimodal input	Llama 4 Scout	Built-in image understanding

Our editorial recommendation for 2026: default to Llama 4 Scout for long-context retrieval, multimodal agents and any deployment where cost or single-GPU hosting drives the architecture. Reach for Qwen3-235B-A22B when reasoning or coding accuracy is the product and you can afford the multi-GPU footprint and the token premium. Compare either against the wider field in our model catalog.

All figures in this guide are drawn from vendor cards and our own runs, and the underlying dataset is available through the free BestLLMfor public API (CC BY 4.0) and our open-source MCP server, so you can pull these numbers straight into your own tooling.

Sources

• Meta Llama 4 Scout model card (HuggingFace)
• Qwen3-235B-A22B-Instruct-2507 model card (HuggingFace)
• Llama 4 on Ollama
• Qwen3 on Ollama