Model comparison

Cohere Command A vs Llama 4 Scout

Compare Cohere Command A vs Llama 4 Scout API pricing: input/output token costs, cache pricing, context windows, workload estimates, and routing fit.

Cohere Command A

cohere_chat · command-a-03-2025

Input
$2.5
Output
$10
Context
256K

Llama 4 Scout

deepinfra · deepinfra/meta-llama/Llama-4-Scout-17B-16E-Instruct

Input
$0.08
Output
$0.3
Context
327.7K

Quick take

Llama 4 Scout has the lower input price at $0.08 per 1M input tokens. Llama 4 Scout is cheaper for the example blended workload below. Llama 4 Scout has the larger context window at 327.7K tokens.

Choose Cohere Command A if...

  • Cohere Command A is a reasonable pick when its provider, latency, or integration path fits your stack better.

Choose Llama 4 Scout if...

  • Llama 4 Scout is the better default for cost-sensitive traffic and repeated high-volume calls.
  • Llama 4 Scout is safer for long documents, repository analysis, and RAG prompts because it has the larger context window.
  • Llama 4 Scout gives more room for long generated answers, reports, or code output.

Example workload cost

Estimates use input tokens plus 20% output tokens. They exclude provider discounts, cache hits, and tool/search surcharges.

Workload Cohere Command A Llama 4 Scout Cheaper
1M input + 200K output $4.50 $0.14 Llama 4 Scout
10M input + 2M output $45.00 $1.40 Llama 4 Scout
100M input + 20M output $450.00 $14.00 Llama 4 Scout

Context, output, and capability fit

Llama 4 Scout provides the larger context window. Check max output separately when the task needs long reports, code generation, or full-document rewrites.

Cohere Command A max output
8K
Llama 4 Scout max output
327.7K
Cohere Command A features
function calling
Llama 4 Scout features
function calling

Risk notes for Cohere Command A

  • No prompt caching in this snapshot: repeated long-context calls may be more expensive.

Risk notes for Llama 4 Scout

  • No prompt caching in this snapshot: repeated long-context calls may be more expensive.

Routing tags

enterprise-ragraglong-contextagentsbudgethigh-volumeopen-weightmultimodallocal-open

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