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Model details

GPT-5.6 Luna (India)

GPT-5.6 Luna (India) is a Bedrock-hosted language model designed for general-purpose text generation, with input support spanning text, images, and PDFs and outputs limited to text. Its advertised capability set includes attachments, reasoning, tool calling, and structured output, signalling an intent to serve both conversational and more programmatic workloads such as document Q&A, agentic coding sessions, and structured extraction. The model is positioned inside the gpt-luna family and is delivered via Amazon Bedrock under the India region key, making it accessible to builders who already work within that managed inference environment.

Independent third-party coverage for this model is currently thin: the only aggregated evidence is a pricing-and-metadata listing that explicitly states no independent quality benchmark results are available yet, so any judgement of strength has to rest on intended use rather than measured scores. That same source frames derived workload cost estimates for chat replies, long-document summarisation, code edits, agentic coding sessions, and structured extraction, with a caching variant that assumes roughly three-quarters of input is served from prompt cache and excludes reasoning tokens from the calculation. In practice this makes the model most useful for teams that need a multimodal, tool-using generalist on Bedrock in India and are comfortable evaluating its real-world fit themselves rather than relying on published benchmark rankings.

Amazon Bedrockin.openai.gpt-5.6-lunagpt-luna

Quick Info

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Provider
Amazon Bedrock
Model key
in.openai.gpt-5.6-luna
Release date
Jul 9, 2026
Last updated
Jul 9, 2026
Knowledge cutoff
2026-02-16
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.22
Output token cost
$1.32

Limits

Input tokens
922,000 tokens
Output tokens
128,000 tokens
Context window
1,050,000 tokens

Transparent token rates

Compare GPT-5.6 Luna (India) pricing

Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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