Verified October 2026

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GPT-6 Luna vs Claude Sonnet 5

A straight cost comparison of GPT-6 Luna (OpenAI) and Claude Sonnet 5 (Anthropic), per 1,000,000 tokens, verified October 2026.

$0.10GPT-6 Luna input / 1M
$2Claude Sonnet 5 input / 1M
95%Blended price gap
GPT-6 LunaCheaper blended

Rate-by-rate comparison

USD per 1M tokens · October 2026
MetricGPT-6 LunaClaude Sonnet 5Winner
Input / 1M$0.10$2GPT-6 Luna
Cached input / 1M$0.01$0.20GPT-6 Luna
Cache write / 1M$0.125$2.50GPT-6 Luna
Output / 1M$0.50$10GPT-6 Luna
Context window1,050,0001,000,000GPT-6 Luna
Batch APIYesYesTie
Long-context tier> 272,000None—

For per-token metrics, the lower number wins. For context window and batch support, the higher or broader option wins. A dash means the provider does not publish that rate: a missing cache-write fee means writes are not billed per token, while a missing cached-input rate means cache hits bill at the full input rate.

Blended cost: 1M input + 250K output

ModelBlended costDifference
GPT-6 Luna$0.225Cheaper
Claude Sonnet 5$4.50+$4.28

Ten workloads, two bills

The same two models, priced across ten workloads that differ only in their input:output ratio. Nothing else changes — no retries, no system prompt, no middleware.

WorkloadGPT-6 LunaClaude Sonnet 5Winner
Classification
100K reqs · 500 in / 10 out
$5.50$110GPT-6 Luna
Data extraction
10K reqs · 2K in / 200 out
$3$60GPT-6 Luna
Support chat
1K reqs · 4K in / 600 out
$0.70$14GPT-6 Luna
Code review
2K reqs · 8K in / 1.5K out
$3.10$62GPT-6 Luna
Summarisation
1K reqs · 20K in / 1K out
$2.50$50GPT-6 Luna
Agent loop
5K reqs · 12K in / 2K out
$11$220GPT-6 Luna
Translation
500 reqs · 15K in / 15K out
$4.50$90GPT-6 Luna
Long-document QA
200 reqs · 120K in / 800 out
$2.48$49.60GPT-6 Luna
Cached RAG
1K reqs · 50K in (45K cached) / 1K out
$1.46$29.10GPT-6 Luna
Nightly batch
10K reqs · 10K in / 2K out
$10$200GPT-6 Luna

GPT-6 Luna wins 10 of 10, Claude Sonnet 5 wins 0 of 10. The winner is set by the token mix, not by the headline rate.

Where the two bills cross over

GPT-6 Luna is cheaper on both sides of the mix: $0.10 vs $2 per million input tokens, and $0.50 vs $10 per million output. No input:output ratio reverses that, so on price alone the other model never becomes the better choice — it has to win on context, latency or output quality.

Cost at four scales

The same support-chat workload (4K input, 600 output per request) at four monthly volumes.

Requests / monthGPT-6 LunaClaude Sonnet 5Difference
100$0.07$1.40GPT-6 Luna cheaper by $1.33
1,000$0.70$14GPT-6 Luna cheaper by $13.30
10,000$7$140GPT-6 Luna cheaper by $133
100,000$70$1,400GPT-6 Luna cheaper by $1,330

What caching does to each bill

Caching only helps the part of the prompt that repeats. This is the support-chat workload (1,000 requests, 4,000 input tokens each) with an increasing share of that prompt served from a warm cache, written once on the first request.

Cached share of the promptGPT-6 LunaClaude Sonnet 5Winner
0%$0.70$14GPT-6 Luna
50%$0.52$10.40GPT-6 Luna
80%$0.412$8.25GPT-6 Luna
95%$0.358$7.17GPT-6 Luna

Both models publish a cached-read rate, so the discount is comparable: GPT-6 Luna reads cache at $0.01 per million, Claude Sonnet 5 at $0.20.

Which one should you pick?

GPT-6 Luna wins on classification, data extraction, support chat, code review, summarisation, agent loop, translation, long-document qa, cached rag, nightly batch, while Claude Sonnet 5 wins on none of the ten workloads above.

Beyond price, the two differ on input rate ($0.10 vs $2); output rate ($0.50 vs $10); cached-input rate ($0.01 vs $0.20); cache-write rate ($0.125 vs $2.50); context window (1,050,000 vs 1,000,000); long-context tier (yes, above 272,000 vs none).

Batch mode is available on GPT-6 Luna and Claude Sonnet 5, which halves the nightly job. Both publish cached-input discounts, so a stable system prompt is worth caching on either.

FAQ

Is GPT-6 Luna cheaper than Claude Sonnet 5?

On a blended 1M-input-plus-250K-output workload, GPT-6 Luna is cheaper by 95% ($0.225 vs $4.50). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.

Which handles longer prompts, GPT-6 Luna or Claude Sonnet 5?

GPT-6 Luna has the larger context window (1,050,000 vs 1,000,000 tokens). Watch the long-context threshold on GPT-6 Luna: rates rise once input passes it.

Do both models support prompt caching?

GPT-6 Luna bills cached reads at $0.01 per 1M; Claude Sonnet 5 at $0.20.

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