LLMopenaiPlan: Pro and up

OpenAI: GPT-5.6 Sol Pro

GPT-5.6 Sol Pro is the same underlying model as [GPT-5.6 Sol](https://openrouter.ai/openai/gpt-5.6-sol), 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 Sol Pro with the team's shared context - pooled credits, one chat, one memory.

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Verdict

GPT-5.6 Sol Pro offers a massive 1M+ token context window at mid-tier pricing, making it the go-to for document-heavy workflows where you need to process entire codebases or multi-hundred-page reports in one pass. The $30/Mtok output cost is steep for high-volume generation but reasonable for analysis-heavy tasks. Without public benchmarks yet, you're trading proven performance data for early access to OpenAI's latest context-handling architecture. Reach for this when context length is your bottleneck and you trust OpenAI's track record over published scores.

Best for

  • Analyzing entire codebases in one context
  • Multi-document research synthesis tasks
  • Long-form report generation with citations
  • Contract review across hundreds of pages
  • Technical documentation Q&A systems

Strengths

The 1.05M token context window handles nearly any real-world document set without chunking or retrieval tricks. Multimodal support means you can mix PDFs, screenshots, and text in the same analysis pass. At $5/Mtok input, it costs half what you'd pay for comparable context from Anthropic's extended models. File handling is native, so you skip preprocessing steps that plague RAG pipelines.

Trade-offs

Output pricing at $30/Mtok makes this expensive for generative tasks like drafting or code synthesis — you'll burn budget fast on long responses. No public benchmarks means you can't compare reasoning quality against Claude Sonnet 4.5 or Gemini 2.5 Pro on standardized tests. Early-release models sometimes show inconsistent instruction-following until the vendor tunes post-launch. If your task needs proven MMLU or HumanEval scores, wait for independent evals.

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
$2.00/Mtok
Output
$10.00/Mtok
Model ID
openai/gpt-5.6-sol-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

Estimated monthly spend
$77.44
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 Analysis

Analyze this codebase for architectural patterns, identify the core abstractions, map dependencies between modules, and flag any circular dependencies or tight coupling issues. Provide a summary diagram in text form.
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Research Paper Synthesis

Synthesize the main arguments from these papers, identify areas of consensus and disagreement, and suggest gaps in the current research. Cite specific papers when referencing claims.
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Technical Spec Q&A System

Using the provided technical specifications, answer this question with exact section references and page numbers. If the answer requires combining information from multiple docs, cite each source.
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Screenshot-Based UI Audit

Analyze these UI screenshots for consistency in spacing, typography, color usage, and component patterns. Flag any deviations from the design system and suggest fixes.
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Compare with

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Data last verified 7 hours ago.Sources aggregated hourly to weekly. See docs/architecture/model-directory.md.