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Claude Opus 4.5 vs Llama 4 Scout

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Claude Opus 4.5 and Llama 4 Scout.

Anthropic

Claude Opus 4.5

Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...

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Meta

Llama 4 Scout

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...

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Technical Specifications

SpecificationClaude Opus 4.5Llama 4 Scout
ProviderAnthropicMeta
Context Window200,000 tokens10,000,000 tokens
Agent Suitability93/10082/100
Time to First Token (TTFT)550 ms350 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablebeta
API AvailableYesYes
Released Date2025-11-242025-04-05

API Pricing Comparison

Input Price per Million Tokens

Claude Opus 4.5

$5.00

Llama 4 Scout

$0.10

Output Price per Million Tokens

Claude Opus 4.5

$25.00

Llama 4 Scout

$0.30

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
9280.0%vs8720.0%
Claude Opus 4.5
Llama 4 Scout
HumanEvalPython coding & logic synthesis
9420.0%vs8950.0%
Claude Opus 4.5
Llama 4 Scout
MATHComplex mathematical problem solving
8750.0%vs8100.0%
Claude Opus 4.5
Llama 4 Scout
GPQAGraduate-level expert reasoning
7600.0%vs6680.0%
Claude Opus 4.5
Llama 4 Scout
HellaSwagCommonsense reasoning and inference
9720.0%vs9450.0%
Claude Opus 4.5
Llama 4 Scout
MT-BenchMulti-turn conversation flow quality
955.0%vs910.0%
Claude Opus 4.5
Llama 4 Scout

Claude Opus 4.5 Quirks & Gotchas

  • Deep analytical reasoning — best for structured problem-solving
  • 200K context is limiting compared to 1M of Opus 4.6+ — upgrade for long-document tasks

Llama 4 Scout Quirks & Gotchas

  • 10M context causes significant VRAM pressure — recommend 4-bit quantization
  • Primarily designed for RAG, not agentic tool calling