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GPT-6 Luna vs Llama 4 Scout

A straight cost comparison of GPT-6 Luna (OpenAI) and Llama 4 Scout (Meta (Llama)), per 1,000,000 tokens, verified November 2026.

Rate-by-rate comparison

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

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.

GPT-6 Luna wins 10 of 10, Llama 4 Scout 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 $0.18 per million input tokens, and $0.50 vs $0.59 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.

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.

Both models publish a cached-read rate, so the discount is comparable: GPT-6 Luna reads cache at $0.01 per million, Llama 4 Scout at $0.018.

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 Llama 4 Scout wins on none of the ten workloads above.

Beyond price, the two differ on input rate ($0.10 vs $0.18); output rate ($0.50 vs $0.59); cached-input rate ($0.01 vs $0.018); cache-write rate ($0.125 vs —); context window (1,050,000 vs 10,000,000); long-context tier (yes, above 272,000 vs none).

Batch mode is available on GPT-6 Luna and Llama 4 Scout, 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 Llama 4 Scout?

On a blended 1M-input-plus-250K-output workload, GPT-6 Luna is cheaper by 31% ($0.225 vs $0.328). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.

Which handles longer prompts, GPT-6 Luna or Llama 4 Scout?

Llama 4 Scout has the larger context window (10,000,000 vs 1,050,000 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; Llama 4 Scout at $0.018.

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