--- title: "OpenAI GPT Integration Services: Kensink Labs" description: "Direct OpenAI GPT integration from a senior lab: structured output, evals, cost control, vendor-neutral abstraction. Full source ownership." source: "https://www.kensink.com/models/openai-gpt/" canonical: "https://www.kensink.com/models/openai-gpt/" --- ![OpenAI](https://www.kensink.com/assets/logos/openai-icon.svg) ★ OpenAI GPT LLM Models 8-week engagement OPENAI GPT · DIRECT INTEGRATION # Direct GPT integration. Structured output, validated, observable. OpenAI's GPT models are a strong, well-tooled default with broad capabilities and a mature ecosystem. We integrate them directly, with the evals, cost control, and structure that production demands. LLM API Eval pipelines TypeScript Vector store [Start a conversation →](https://www.kensink.com/contact) [All llm models →](https://www.kensink.com/models) Cycle 8 weeks · fixed price Stack OpenAI API, direct Output Production code + eval suite Handoff Full source ownership \[THE SHORT VERSION\] ## The default frontier model, engineered for production. GPT models are capable, broadly supported, and backed by mature tooling for structured output, function calling, and embeddings. The gap between a demo and a product is the same as always: evals, retries, cost and latency control, structured output validation, and a vendor-neutral abstraction. That gap is the work we do. When it fits - General-purpose reasoning, generation, and extraction - Function calling and structured output workflows - Teams wanting a broadly supported, well-documented model When it does not - On-prem-only requirements (use an open-weight model) - Tasks where a cheaper model meets the eval bar \[HOW WE BUILD IT\] ## How we build with OpenAI GPT. 01 ### Direct API, thin abstraction Calls go straight to the OpenAI API behind a small provider interface, so switching or adding models stays a config change. 02 ### Structured output, validated We use structured output and function calling, then validate against a schema. No hoping the JSON parses. 03 ### Evals before you trust it An eval set from your real tasks gates every prompt and model change. Quality is measured, not vibed. 04 ### Cost, latency, and fallback Token budgets, caching, streaming, and a fallback path, with observability on every call. \[ WHAT YOU GET \] ## What the engagement leaves behind. Direct No orchestration framework Schema Structured output validated Eval-gated Quality measured, not assumed Observed Every call, cost and latency \[SUITE + VERSIONS\] ## Pick the tier that fits. GPT-5.6 ships as Sol, Terra, and Luna. We integrate the whole suite directly behind a vendor-neutral abstraction, then route by task difficulty. Swapping tiers or versions is a config change, not a rewrite. Eval-gated, either way. [ Preview ★ Flagship 26 Jun 2026 ### GPT-5.6 Sol gpt-5.6-sol Input $5 / 1M tokens Output $30 / 1M tokens - Flagship of the 5.6 suite. State of the art on Terminal-Bench 2.1 - Stronger than GPT-5.5 across coding, reasoning, and agentic work - Limited preview now. General availability in the coming weeks Read the technical brief ](https://www.kensink.com/models/openai-gpt/gpt-5-6) Preview 26 Jun 2026 ### GPT-5.6 Terra gpt-5.6-terra Input $2.5 / 1M tokens Output $15 / 1M tokens - The balanced tier. Most of Sol's quality at half the token price - Our default for high-volume production steps in the 5.6 suite - Same 400K context and tool surface as Sol Supported · brief not yet published Preview 26 Jun 2026 ### GPT-5.6 Luna gpt-5.6-luna Input $1 / 1M tokens Output $6 / 1M tokens - Fastest and cheapest tier. Right for classification, routing, extraction - The cheap step inside a larger agentic workflow - $1 input and $6 output per million tokens Supported · brief not yet published Previous Q1 2026 ### GPT-5.5 gpt-5.5 Input $5 / 1M tokens Output $30 / 1M tokens - The prior frontier GPT. Still a solid, supported production baseline - Moving to 5.6 is a config change behind our vendor-neutral abstraction - Use it until the 5.6 eval pass clears on your tasks Supported · brief not yet published \[METHODOLOGY · K-FRAMEWORK\] ## Integrated through the K-Framework. Every model we integrate runs through the same operating system. Three pillars, sixteen layers, one Compound Growth Loop. The methodology that keeps AI work from rotting after the first ship. [Read the K-Framework](https://www.kensink.com/k-framework) 01 ### Foundations Direct API integration with the model. No LangChain, no orchestration vendor, no agent framework built on quicksand. Typed contracts, the same way we wire up Postgres. 02 ### Amplification An eval suite built from your real tasks gates every prompt and model change. Quality is measured before it ships, not vibed in a demo. 03 ### Judgment Governance, audit, and oversight wired in from day one. Who called what, with which prompt version, at what cost. Your auditors get answers, not screenshots. \[OBSERVABILITY\] ## Observability your team can read. A model in production without observability is roulette. We instrument every integration so engineering and finance can see the same numbers, and so a regression at 3am surfaces before a customer opens a ticket. Instrumented ### Cost per call Tokens in, tokens out, dollars spent. Sliced by feature, tenant, and route. Budgets enforced where it matters. Instrumented ### Latency p50 / p95 / p99 Real distributions, not averages. We know which routes are slow, and why. Instrumented ### Eval pass rates The same eval suite that gates a release runs continuously in production. A regression on real traffic surfaces fast. Instrumented ### Prompt + completion logs PII scrubbed at the proxy, shipped to your SIEM. Retention controls match your compliance window. Dashboards your team owns, not ours. At handoff you get the queries, the alerts, and the runbook. We are not in the path to read your metrics. \[COMMON QUESTIONS\] ## Questions we get asked. Which GPT model should I use? Usually a mix: a capable model for hard steps and a cheaper or smaller one for easy, high-volume steps. We route by task and prove the choice with evals rather than paying for the biggest model everywhere. How do you keep costs under control? Prompt and context trimming, caching, model routing by difficulty, and hard token budgets, all visible in observability. We treat cost as a first-class metric alongside quality and latency. Share[](https://twitter.com/intent/tweet?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fopenai-gpt%2F&text=OpenAI%20GPT%20%C2%B7%20LLM%20Models)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fopenai-gpt%2F) [View .md](https://www.kensink.com/models/openai-gpt.md) \[RELATED\] ## Worth a look next. [ LLM Models Claude Read more ](https://www.kensink.com/models/claude)[ LLM Models Embeddings Read more ](https://www.kensink.com/models/embeddings)[ Design Patterns RAG Read more ](https://www.kensink.com/llm/rag) APPLIED K-FRAMEWORK ## Bring the problem. We’ll bring the build. Senior engineers, eval suite at handoff, full source ownership. Sprint, program, or ongoing. We shape the engagement to the work. [Start a conversation →](https://www.kensink.com/contact) [Read the K-Framework](https://www.kensink.com/k-framework)