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Claude Opus 4.7 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 4.7 and Llama 4 Scout.

Anthropic

Claude Opus 4.7

Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on...

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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 4.7Llama 4 Scout
ProviderAnthropicMeta
Context Window1,000,000 tokens10,000,000 tokens
Agent Suitability96/10082/100
Time to First Token (TTFT)480 ms350 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablebeta
API AvailableYesYes
Released Date2026-04-162025-04-05

API Pricing Comparison

Input Price per Million Tokens

Claude Opus 4.7

$5.00

Llama 4 Scout

$0.10

Output Price per Million Tokens

Claude Opus 4.7

$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
9410.0%vs8720.0%
Claude Opus 4.7
Llama 4 Scout
HumanEvalPython coding & logic synthesis
9610.0%vs8950.0%
Claude Opus 4.7
Llama 4 Scout
MATHComplex mathematical problem solving
9150.0%vs8100.0%
Claude Opus 4.7
Llama 4 Scout
GPQAGraduate-level expert reasoning
8420.0%vs6680.0%
Claude Opus 4.7
Llama 4 Scout
HellaSwagCommonsense reasoning and inference
9880.0%vs9450.0%
Claude Opus 4.7
Llama 4 Scout
MT-BenchMulti-turn conversation flow quality
975.0%vs910.0%
Claude Opus 4.7
Llama 4 Scout

Claude Opus 4.7 Quirks & Gotchas

  • โ–ธTop-tier agentic coding model โ€” excels at autonomous software engineering
  • โ–ธRequires explicit tool_choice parameter for parallel function calling to work reliably

Llama 4 Scout Quirks & Gotchas

  • โ–ธ10M context causes significant VRAM pressure โ€” recommend 4-bit quantization
  • โ–ธPrimarily designed for RAG, not agentic tool calling