Model comparison

GPT-5.6 Luna vs Claude Opus 4.7

Compare GPT-5.6 Luna vs Claude Opus 4.7 API pricing: input/output token costs, cache pricing, context windows, workload estimates, and routing fit.

GPT-5.6 Luna

openai · gpt-5.6-luna

Input
$1
Output
$6
Context
1.1M

Claude Opus 4.7

anthropic · claude-opus-4-7

Input
$5
Output
$25
Context
1M

Quick take

GPT-5.6 Luna has the lower input price at $1 per 1M input tokens. GPT-5.6 Luna is cheaper for the example blended workload below. GPT-5.6 Luna has the larger context window at 1.1M tokens.

Choose GPT-5.6 Luna if...

  • GPT-5.6 Luna is the better default for cost-sensitive traffic and repeated high-volume calls.
  • GPT-5.6 Luna is safer for long documents, repository analysis, and RAG prompts because it has the larger context window.

Choose Claude Opus 4.7 if...

  • Claude Opus 4.7 is a reasonable pick when its provider, latency, or integration path fits your stack better.

Example workload cost

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

Workload GPT-5.6 Luna Claude Opus 4.7 Cheaper
1M input + 200K output $2.20 $10.00 GPT-5.6 Luna
10M input + 2M output $22.00 $100.00 GPT-5.6 Luna
100M input + 20M output $220.00 $1,000.00 GPT-5.6 Luna

Context, output, and capability fit

GPT-5.6 Luna provides the larger context window. Check max output separately when the task needs long reports, code generation, or full-document rewrites.

GPT-5.6 Luna max output
128K
Claude Opus 4.7 max output
128K
GPT-5.6 Luna features
prompt caching, function calling, vision
Claude Opus 4.7 features
prompt caching, function calling, vision

Risk notes for GPT-5.6 Luna

  • No major capability risk is flagged in this snapshot, but provider pages should still be verified before production routing.

Risk notes for Claude Opus 4.7

  • High output price: cap max tokens for verbose generation workloads.

Routing tags

frontierbudgetfastreasoningcodingagentslong-contextmultimodalcache-friendlyragpremium

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