Llama 4 Scout
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
According to BenchLM.ai, Llama 4 Scout ranks #108 out of 119 models on the provisional leaderboard with an overall score of 22/100. It does not yet have enough sourced coverage for BenchLM's verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
Llama 4 Scout is a open weight model with a 10M token context window. It processes queries without explicit chain-of-thought reasoning, offering faster response times and lower token usage.
This profile currently has 17 of 225 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Its strongest category is Reasoning (#62), while its weakest is Instruction Following (#107). This performance profile makes it particularly strong for complex reasoning, multi-step problem solving, and analytical tasks.
Ranking Distribution
Category rank across 8 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
#89Coding
#95Reasoning
#62Knowledge
#94Math
#76Multilingual
#90Multimodal
#78Inst. Following
#107Chatbot Arena Performance
Benchmark Details
Only benchmark rows with an attached exact-source record are shown here. Source-unverified manual rows and generated rows are hidden from model pages.
Compare This Model
See how Llama 4 Scout stacks up against similar models
Frequently Asked Questions
How does Llama 4 Scout perform overall in AI benchmarks?
Llama 4 Scout currently ranks #108 out of 119 models on BenchLM's provisional leaderboard with an overall score of 22 (estimated). It is created by Meta and features a 10M context window.
Is Llama 4 Scout good for knowledge and understanding?
Llama 4 Scout ranks #94 out of 119 models in knowledge and understanding benchmarks with an average score of 15.5. There are stronger options in this category.
Is Llama 4 Scout good for coding and programming?
Llama 4 Scout ranks #95 out of 119 models in coding and programming benchmarks with an average score of 2.6. There are stronger options in this category.
Is Llama 4 Scout good for reasoning and logic?
Llama 4 Scout ranks #62 out of 119 models in reasoning and logic benchmarks with an average score of 40.9. There are stronger options in this category.
Is Llama 4 Scout good for agentic tool use and computer tasks?
Llama 4 Scout ranks #89 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 17.4. There are stronger options in this category.
Is Llama 4 Scout good for multimodal and grounded tasks?
Llama 4 Scout ranks #78 out of 119 models in multimodal and grounded tasks benchmarks with an average score of 35.9. There are stronger options in this category.
Is Llama 4 Scout good for instruction following?
Llama 4 Scout ranks #107 out of 119 models in instruction following benchmarks with an average score of 18.8. There are stronger options in this category.
Is Llama 4 Scout open source?
Yes, Llama 4 Scout is an open weight model created by Meta, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Does Llama 4 Scout have full benchmark coverage on BenchLM?
Not yet. Llama 4 Scout currently has 17 published benchmark scores out of the 225 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
What is the context window size of Llama 4 Scout?
Llama 4 Scout has a context window of 10M, which determines how much text it can process in a single interaction.
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