LLM Reference

Llama 4 Scout 17B-16E Instruct

Released
2025-04-05
Last refreshed
2026-07-09
Status
Researched 106d ago
Open weightsCommercial use: conditionalMultimodalCodingRAGAgentsLong contextVisionClassificationJSON / Tool use

Llama 4 Scout 17B-16E Instruct is worth evaluating for coding, rag, and agents when its provider route and context window match the workload.

Use it for

  • Teams evaluating coding, rag, and agents
  • Workloads that can use a 10m context window
  • Buyers comparing 4 tracked provider routes

Do not use it for

  • Workloads where another current model has stronger sourced task evidence
Specifications
Family
Llama 4
Released
2025-04-05
Context
10m
Parameters
109B (17B active)
Architecture
Mixture of Experts
Knowledge cutoff
2024-08
Specialization
general
Openness
Open weights
License
Llama 4 CommunityCommercial use: conditional
Weights
Unknown
Code
Unknown
Training
Pretrained
Created by

Large-scale open-source AI for social technologies.

Menlo Park, California, United States
Founded 2013
Website
Pricing
Output / 1M
$0.220
Input / 1M
$0.170

Cheapest of 12 routes · AWS Bedrock

About

Meta's Llama 4 Scout is a 17-billion parameter mixture-of-experts model with 16 expert routing. Optimized for efficient inference on edge and cloud environments with strong multi-turn conversation capabilities. Available on Cloudflare Workers AI.

Top use-case fit: coding, agents, and build tasks

Coding

Q/$ B

1 relevant benchmark in the decision map.

RAG

Included by capability and metadata signals in the decision map.

Agents

Q/$ A

1 relevant benchmark in the decision map.

Provider price ladder

Compare all 12

Compare API pricing across 4 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MRoute
AWS Bedrock$0.170$0.220
Serverless
DeepInfra$0.080$0.300
Serverless
OpenRouter$0.080$0.300
Serverless
GroqCloud$0.110$0.340
Serverless

Available via routers & gateways(16)

Capabilities

VisionMultimodalStructured Outputs

Benchmark peer barsfor Coding

Benchmark scores(5)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionEvaluationSource
Chatbot Arena1295.0—Observed 2026-04-15—Source
τ-bench62.3τ-benchObserved 2026-04-24—Source
Massive Multi-discipline Multimodal Understanding69.4—Observed 2025-04-05—Source
MMLU PRO74.3—Observed 2025-04-05—Source
LiveCodeBench32.8—Observed 2025-04-05—Source

Migration checks

No linked migration route is available for this model yet.

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