OpenAI Releases GPT-5.6 Luna: $1/$6 Per 1M Tokens With 1M Context Window

OpenAI has released GPT-5.6 Luna, a cost-efficient model in its GPT-5.6 series. The model is priced at $1 per 1M input tokens and $6 per 1M output tokens, with a 1 million token context window and a knowledge cutoff of February 2026.

According to OpenAI, GPT-5.6 Luna is designed for high-volume, latency-sensitive tasks including chat applications, classification, and lightweight agentic workflows. The company claims the model provides "capable reasoning for its price tier."

Pricing and Specifications

GPT-5.6 Luna's pricing positions it as one of OpenAI's more economical offerings:

• Input: $1 per 1M tokens
• Output: $6 per 1M tokens
• Context window: 1M tokens
• Released: July 9, 2026
• Knowledge cutoff: February 2026

The model is currently available through OpenRouter, which forwards requests directly to OpenAI without routing decisions. OpenRouter data indicates that prompt caching can reduce effective costs by 60-80% below list prices for workloads with repeated context.

Technical Details

The model is listed as part of the GPT-5.6 series, though OpenAI has not disclosed specific details about parameter count, architecture changes from previous GPT-5 variants, or benchmark performance scores. The company describes it as "fast" and suited for latency-sensitive applications, though specific throughput and time-to-first-token metrics were not provided at launch.

GPT-5.6 Luna is accessible through OpenAI-compatible APIs, allowing developers to integrate it by changing only the model slug in existing codebases.

What This Means

GPT-5.6 Luna appears positioned as OpenAI's answer to demand for cost-effective models that can handle large context windows without premium pricing. At $1/$6 per 1M tokens with 1M context, it competes directly with mid-tier offerings from Anthropic and other providers targeting high-volume production workloads. The lack of disclosed benchmark scores makes it difficult to assess its capabilities relative to competing models, though its designation as suitable for "lightweight agentic workflows" suggests it may not match the reasoning performance of OpenAI's flagship models. The February 2026 knowledge cutoff indicates relatively recent training data for deployment in mid-2026.