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OpenAI: GPT-5.6 Sol

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series. It is suited for complex reasoning, coding, and agentic workflows, and is particularly strong at command-line and multi-step coding tasks...

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

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Verdict

GPT-5.6 Sol delivers OpenAI's largest context window to date at 1.05M tokens, making it the go-to for processing entire codebases, lengthy legal documents, or multi-hour meeting transcripts in a single pass. The trade-off is steep pricing at $30/Mtok output — roughly 6x GPT-4o — which makes exploratory work expensive. Reach for this when context size is the bottleneck and you can afford to pay for comprehensiveness over iteration speed.

Best for

Processing entire codebases in one context
Multi-document legal analysis and contracts
Long-form meeting transcript summarization
Research synthesis across dozens of papers
Deep technical documentation review

Strengths

The 1.05M token window eclipses GPT-4o's 128k and Claude Sonnet 4.5's 200k, enabling true whole-repository reasoning without chunking strategies. File and image modalities let you drop PDFs, spreadsheets, and screenshots directly into prompts. OpenAI's track record suggests strong instruction-following and coherent output even at extreme context lengths, though we lack public benchmarks to confirm performance parity with GPT-4o on standard evals.

Trade-offs

Output pricing at $30/Mtok makes this 6x more expensive than GPT-4o for generation-heavy tasks, which adds up fast during iterative workflows or high-volume production use. Without published benchmarks, we can't verify whether reasoning quality matches smaller OpenAI models or how it stacks up against Claude Sonnet 4.5 on long-context retrieval tasks. The premium you pay buys context capacity, not necessarily smarter answers.

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

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

$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 Review

I've uploaded our full codebase (47 files, ~85k tokens). Review the architecture, identify tight coupling between modules, and suggest three concrete refactoring moves to improve testability.

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Multi-Contract Comparison

I've attached five vendor contracts (total ~120k tokens). Compare indemnification clauses, liability caps, and termination terms. Flag any inconsistencies and rank contracts by risk exposure.

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Meeting Series Synthesis

Here are transcripts from our last 12 weekly planning meetings (~200k tokens). Identify recurring blockers, extract all action items assigned to engineering, and summarize how priorities shifted over time.

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Research Literature Map

I've uploaded 28 papers on transformer attention mechanisms (~340k tokens). Map how each paper builds on prior work, identify the three most-cited techniques, and suggest gaps no one has addressed.

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Technical Doc Deep Dive

I've attached our complete API documentation and internal architecture guides (~95k tokens). How should we implement rate limiting for webhook endpoints, and which existing patterns can we reuse?

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