Llama 4 Scout vs Qwen3 VL 4B Thinking
Llama 4 Scout and Qwen3 VL 4B Thinking are closely matched at 7.8 and 12.9 on the LLM Stats Score. Llama 4 Scout is 2.4x cheaper per token.
Meta · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Llama 4 Scout and Qwen3 VL 4B Thinking are closely matched on the overall LLM Stats Score at 7.8 and 12.9.
In the 3 individual benchmarks reported for both models, Qwen3 VL 4B Thinking wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Llama 4 Scout is roughly 2.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Llama 4 Scout also accepts a larger context window (10,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Llama 4 Scout
- cost matters — it's about 2.4x cheaper per token
- you process long inputs — it offers a 10,000,000 token context window
Choose Qwen3 VL 4B Thinking
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
- you want the most recent training data — it shipped Sep 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
12 reported for Llama 4 Scout · 48 for Qwen3 VL 4B Thinking
Llama 4 Scout outperforms in 1 benchmarks (MMLU-Pro), while Qwen3 VL 4B Thinking is better at 2 benchmarks (GPQA, MMLU).
Qwen3 VL 4B Thinking shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Llama 4 Scout ($0.08/1M tokens) is 1.3x cheaper than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, Llama 4 Scout ($0.30/1M tokens) is 3.3x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, Qwen3 VL 4B Thinking is more expensive than Llama 4 Scout.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Llama 4 Scout has 105.0B more parameters than Qwen3 VL 4B Thinking, making it 2625.0% larger.
Context Window
Maximum input and output token capacity
Llama 4 Scout accepts 10,000,000 input tokens compared to Qwen3 VL 4B Thinking's 262,144 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while Qwen3 VL 4B Thinking is limited to 262,144 tokens.
Input capabilities
Documented input modalities across available providers
Both Llama 4 Scout and Qwen3 VL 4B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Llama 4 Scout
Qwen3 VL 4B Thinking
License
Usage and distribution terms
Llama 4 Scout is licensed under Llama 4 Community License Agreement, while Qwen3 VL 4B Thinking uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Llama 4 Community License Agreement
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Llama 4 Scout was released on 2025-04-05, while Qwen3 VL 4B Thinking was released on 2025-09-22.
Qwen3 VL 4B Thinking is 6 months newer than Llama 4 Scout.
Apr 5, 2025
1.5 years ago
Sep 22, 2025
1.0 years ago
5mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together. Qwen3 VL 4B Thinking is available from DeepInfra.
Llama 4 Scout
Qwen3 VL 4B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against Llama 4 Scout and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about Llama 4 Scout vs Qwen3 VL 4B Thinking.