LLMopenaiPlan: Pro and up

OpenAI: GPT-5.6 Luna Pro

GPT-5.6 Luna Pro is the same underlying model as [GPT-5.6 Luna](https://openrouter.ai/openai/gpt-5.6-luna), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Anyone in the Project can @-mention OpenAI: GPT-5.6 Luna Pro with the team's shared context - pooled credits, one chat, one memory.

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Verdict

GPT-5.6 Luna Pro delivers a massive 1M+ token context window at $0.50/$3.00 per Mtok—competitive pricing for handling entire codebases, multi-document analysis, or long-form content generation in a single pass. Without public benchmarks yet, it's hard to gauge reasoning quality against Claude Sonnet 4.5 or Gemini 2.0 Flash Thinking, but the economics and window size make it worth testing for context-heavy workflows. Reach for this when you need to process huge inputs without chunking and cost matters more than proven leaderboard scores.

Best for

Multi-document analysis across entire repositories
Long-form content generation with extensive context
Cost-sensitive workflows requiring large context windows
Codebase-wide refactoring and documentation tasks
Research synthesis from dozens of papers

Strengths

The 1.05M token context window handles entire codebases or document collections without splitting, and $0.50 input pricing undercuts many competitors at this scale. Multimodal support (text, image, file) means you can mix screenshots, PDFs, and code in one prompt. For teams that hit context limits on smaller models or pay premium rates for long-context tasks, the economics here are compelling—especially on input-heavy workloads where you're feeding large corpora repeatedly.

Trade-offs

No public benchmarks means you're flying blind on reasoning quality, code generation accuracy, and instruction-following compared to established models like Claude Sonnet 4.5 or GPT-4o. Early-generation models from new releases often lag on complex reasoning or produce verbose outputs that inflate token costs. The $3.00 output rate climbs fast if the model generates long responses, and without MMLU, HumanEval, or GPQA scores, you can't predict where it will stumble on technical tasks.

Specifications

Provider: openai
Category: llm
Context length: 1,050,000 tokens
Max output: 128,000 tokens
Modalities: file, image, text
License: proprietary
Released: 2026-07-09

Pricing

Input: $0.20/Mtok
Output: $1.20/Mtok
Model ID: openai/gpt-5.6-luna-pro

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.

Team cost calculator

Seats5 peopleMessages / seat / day80Avg turn size2 ktokOutput share30 %

Estimated monthly spend

$8.80

17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.

Providers

Provider-level routing data is not available yet for this model.

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Projects have used this model yet to share anonymised team stats. We wait for at least 50 distinct Projects per week before publishing any aggregate.

Starter prompts

Codebase Architecture Review

Review this codebase and identify architectural patterns, tightly coupled modules, and opportunities to improve separation of concerns. Provide a prioritized list of refactoring suggestions with code examples.

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Multi-Paper Research Synthesis

Synthesize the key findings, methodologies, and contradictions across these research papers. Organize by theme and highlight gaps in the current literature that warrant further investigation.

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Long-Form Content Expansion

Expand this outline into a detailed guide, incorporating examples and explanations from the provided reference documents. Maintain a consistent tone and ensure all sections connect logically.

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Contract Comparison Analysis

Compare these contracts and highlight differences in terms, obligations, and risk clauses. Flag any provisions that deviate significantly from standard industry language.

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Multimodal Documentation Generation

Generate user-facing documentation for this feature based on the code, UI screenshots, and design specs provided. Include setup steps, usage examples, and troubleshooting tips.

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