Context Window

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GPT-5.6 Luna Pro vs Llama 3 1 8b

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Openai

Model

GPT-5.6 Luna Pro

Image inputTool calling

Context window

1.1M

1,050,000 tokens · ~788K words

Model page
Meta

Model

Llama 3 1 8b

Context window

131K

131,000 tokens · ~98K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

GPT-5.6 Luna Pro1.1M
Llama 3 1 8b131K

GPT-5.6 Luna Pro has about 8× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-5.6 Luna Pro has 701% more context capacity (1050K vs 131K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-5.6 Luna Pro. Its 1050K context fits entire documents without chunking (vs 131K).

  • Long output (reports, code files)

    Use GPT-5.6 Luna Pro. Its 128K max output lets you generate complete artifacts in one request.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecGPT-5.6 Luna ProLlama 3 1 8b
Context window1,050,000 tokens (1050K)131,000 tokens (131K)
Max output tokens128,000 tokens (128K)16,384 tokens (16K)
Speed tierBalancedFast
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateJul 2026N/A

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderGPT-5.6 Luna Pro inGPT-5.6 Luna Pro outLlama 3 1 8b inLlama 3 1 8b out
Novita——$0.020/M$0.050/M
Nscale——$0.030/M$0.030/M
Ovhcloud——$0.100/M$0.100/M
Perplexity——$0.200/M$0.200/M

Frequently asked questions

GPT-5.6 Luna Pro has a larger context window: 1050K tokens vs 131K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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Without Mem0~128K tokens sent
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Repeated info
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