--- title: "GPT-5.6 API: Pricing, Thinking Modes, and the Shared Context Trap" slug: "openai-gpt-5-6-sol-terra-luna-guide" description: "GPT-5.6 API pricing ($1-$30/1M), the Ultra and Max thinking modes, and a 1.05M context window that is shared — with the fine print that breaks agent loops." publishDate: "2026-07-11" updatedDate: "2026-08-06" author: Umesh Malik canonical: "https://umesh-malik.com/blog/openai-gpt-5-6-sol-terra-luna-guide" category: "LLM Engineering" tags: - AI - OpenAI - ChatGPT - GPT-5.6 - API - AI Agents - LLMs - Machine Learning keywords: "OpenAI GPT-5.6, GPT-5.6 Sol, GPT-5.6 Terra, GPT-5.6 Luna, GPT-5.6 benchmarks, GPT-5.6 pricing, GPT-5.6 API, GPT-5.6 vs GPT-5.4, GPT-5.6 context window, GPT-5.6 ultra thinking" primaryKeyword: GPT-5.6 API secondaryKeywords: - OpenAI GPT-5.6 - GPT-5.6 Sol Terra Luna - GPT-5.6 pricing - GPT-5.6 API - GPT-5.6 benchmarks - GPT-5.6 vs GPT-5.4 - GPT-5.6 context window geoHooks: - TL;DR - GPT-5.6 benchmarks - Sol vs Terra vs Luna - GPT-5.6 pricing - FAQ image: "/blog/gpt-5-6-cover.svg" imageAlt: "OpenAI GPT-5.6 family showing Sol, Terra, and Luna tiers with benchmarks, pricing, and 1.05M context" featured: true published: true readingTime: "10 min read" series: "GPT-5.6 Deep Dive" seriesOrder: 1 faq: - q: "What is GPT-5.6?" a: "GPT-5.6 is OpenAI's July 2026 model family with three variants: Sol (flagship for complex work and coding), Terra (balanced everyday model), and Luna (fastest and cheapest). All share a 1.05M-token context window and a February 16, 2026 knowledge cutoff." - q: "Which GPT-5.6 model is best for coding?" a: "Sol. It scores 80 on the Artificial Analysis Coding Agent Index and hits 91.9% on Terminal-Bench 2.1 with ultra thinking, while using far fewer output tokens than comparable frontier models on the same tasks." - q: "How much does GPT-5.6 cost?" a: "Per 1M tokens: Sol is $5 input / $30 output, Terra is $2.50 input / $15 output, and Luna is $1 input / $6 output. Long-context prompts above the standard threshold are billed at higher rates." - q: "What are the GPT-5.6 API model names?" a: "The API exposes gpt-5.6-sol, gpt-5.6-terra, and gpt-5.6-luna. The alias gpt-5.6 routes to Sol. GPT-5.6 is available in ChatGPT, Codex, the OpenAI API, and GitHub Copilot." --- **The GPT-5.6 API** is OpenAI's July 2026 model family — three tiers (Sol, Terra, and Luna) that share a 1.05M-token context window and range from $1 to $30 per million tokens. Sol is the coding-and-reasoning flagship, Terra is the balanced everyday model, and Luna is the fastest and cheapest. OpenAI released **GPT-5.6 on July 9, 2026**, and for the first time in the GPT-5 line the headline is not a single model. It is **three**: **Sol, Terra, and Luna**. That naming change is the whole story. Instead of shipping one frontier model and a `-pro` step-up, OpenAI split the release into a **capability-and-cost ladder** where each rung is tuned for a different budget and workload. Sol is the flagship. Terra is the everyday workhorse. Luna is the speed-and-price play. And crucially, they all share the same **1.05M-token context window**, the same **February 16, 2026 knowledge cutoff**, and the same platform features — so you can move up and down the ladder without rewriting your integration. The short answer: **GPT-5.6 is OpenAI's most efficiency-focused release yet.** Sam Altman is on record calling the family "orders of magnitude more efficient and cost-effective than previous versions," and the coding numbers back the claim — Sol reportedly finishes agentic coding tasks with **54% better token efficiency** than the previous generation. If you run models at scale, this release is less about a new capability ceiling and more about **doing the same work for a fraction of the tokens**. ## TL;DR - **GPT-5.6 launched July 9, 2026** as a three-model family: **Sol** (flagship), **Terra** (balanced), and **Luna** (fast and cheap). - All three share a **1.05M-token context window**, **128K max output**, and a **February 16, 2026** knowledge cutoff. - **Sol is the best coding model in the family**, scoring **80** on the Artificial Analysis Coding Agent Index — about **2.8 points above** the previous frontier competitor — and **91.9% on Terminal-Bench 2.1** with ultra thinking. - **Sol is the first model to clear the halfway mark on Agent's Last Exam**, at roughly **50.9%** in code mode. - **Terra lands just above the previous frontier tier** on coding, and **Luna outperforms the last generation's flagship** while being the cheapest option. - **Pricing per 1M tokens**: Sol `$5 / $30`, Terra `$2.50 / $15`, Luna `$1 / $6`. - New **ultra thinking** and **max** reasoning modes push the ceiling on hard, long-horizon tasks. - OpenAI calls GPT-5.6 its **strongest cybersecurity model yet**, tuned for defensive work like threat modeling, code review, and blue teaming. - API names: **`gpt-5.6-sol`**, **`gpt-5.6-terra`**, **`gpt-5.6-luna`**; the alias **`gpt-5.6`** routes to Sol. Available in **ChatGPT, Codex, the API, and GitHub Copilot**. ![GPT-5.6 model family showing Sol, Terra, and Luna tiers with their coding scores, pricing, and shared 1.05M context window](/blog/gpt-5-6-family-tiers.svg) ## What the GPT-5.6 API Actually Is GPT-5.6 is not "GPT-5.5 but smarter." It is a **repackaging of the frontier into three price points**, and that is a more interesting decision than another benchmark bump. Here is the mental model: - **Sol** — the flagship. Built for complex work across **coding, knowledge work, research, cybersecurity, science, computer use, and design**. This is the one you reach for when the task is genuinely hard. - **Terra** — the middle tier. A deliberate **balance of capability, speed, and cost** for everyday production work. It is the model most teams will actually run by default. - **Luna** — the floor. The **fastest and lowest-cost** member of the family, aimed at high-volume, latency-sensitive, or cost-capped workloads. The important part is what they have in common. Every tier gets the **same 1.05M context window**, the **same 128K max output**, and the **same knowledge cutoff**. There is no "the cheap model also has a smaller brain for context" catch. You are trading raw reasoning depth for price and speed — not memory. The GPT-5.6 family uses celestial codenames — Sol (sun), Terra (earth), Luna (moon) — instead of the old `-mini` / `-pro` suffixes. It is a marketing rebrand, but it maps cleanly onto a real axis: Sol is the brightest and most expensive, Luna is the smallest and cheapest, Terra sits in between. ## 1. The Real Headline Is Efficiency, Not a New Ceiling Most model launches lead with "we beat the benchmark." GPT-5.6 leads with **"we beat it for less."** That is a genuine shift. The standout claim is that **Sol is 54% more token-efficient on AI coding tasks** than the previous generation. On head-to-head coding runs, OpenAI says Sol uses **less than half the output tokens** of a comparable frontier model, finishes in **less than half the time**, and costs about **a third less** to complete the same task. For anyone paying a real API bill, that math matters more than a two-point benchmark win. Why does this land now? Because the bottleneck for most production LLM systems in 2026 is **not** "the model cannot do it." It is "the model does it, but the token bill and latency make it uneconomical at scale." A model that produces the same answer with half the tokens changes which use cases are actually viable. > 💡 **Key insight**: GPT-5.6's most important number is not a benchmark score — it is the token count it takes to *reach* that score. Efficiency is the feature. ## 2. Coding and Agents: Where Sol Actually Wins Sol is positioned as **the best coding model in the family**, and the public numbers are strong. The Terminal-Bench and Agent's Last Exam numbers are the ones agent builders should care about. They measure **long-horizon, multi-step task completion** — the model has to plan, run commands, read output, recover from errors, and keep going without a human babysitting each step. Clearing 50% on Agent's Last Exam is a milestone; most models still stall well before the halfway point. ![GPT-5.6 Sol coding and agent benchmark scores including Terminal-Bench 2.1 and Agent's Last Exam](/blog/gpt-5-6-coding-benchmarks.svg) For a concrete sense of how Codex-class coding behaves inside a real agent loop, I walked through [building frontend UIs with Codex and Figma](/blog/figma-codex-react-2026) — the same plan-run-inspect-iterate pattern these benchmarks are trying to measure. Terra performs **just above** the previous frontier competitor on the coding index, and Luna **outperforms the previous generation's flagship** while being the cheapest option in the lineup. In other words: even the budget tier of GPT-5.6 is roughly a frontier model from one generation ago. ## 3. Thinking Modes: Ultra and Max GPT-5.6 introduces higher-effort reasoning modes that let you dial compute up for the hardest work. - **Ultra thinking** — the top reasoning setting, used to post Sol's record **91.9% on Terminal-Bench 2.1**. Reserve it for genuinely hard, long-horizon problems where extra deliberation pays for itself. - **Max mode** — a strong high-effort tier that still hit **88.76%** on the same benchmark without going all the way to ultra. The practical rule is the same as it has always been with reasoning models: **effort is a cost dial, not a free upgrade.** Higher thinking modes consume more (billed) reasoning tokens and add latency. Use them where the task genuinely needs multi-step planning — agentic coding, deep research, complex analysis — and drop back to lower effort for extraction, formatting, and simple transforms. As with every recent OpenAI reasoning model, the tokens spent "thinking" in ultra and max modes are billed as output and consume your context budget even though you never see them. Leave headroom, and measure incomplete responses before you cap `max_output_tokens` aggressively. ## 4. Cybersecurity: OpenAI's "Strongest Yet" OpenAI describes GPT-5.6 as its **strongest cybersecurity model to date**, explicitly tuned to help with **defensive** security work: - threat modeling and architecture review - security-focused code review and vulnerability triage - patch generation and remediation guidance - blue-team workflows and detection engineering This is a meaningful positioning choice. A model that is good at finding and fixing vulnerabilities is, by definition, also more capable in the offensive direction — so OpenAI pairs the capability with monitoring and access controls, and frames the sanctioned use cases around defense. If your security team has been waiting for a model strong enough to sit inside real review pipelines, Sol is the one to evaluate. If you are thinking about agents with this much capability touching production systems, the governance questions in [the agentic AI enterprise security model](/blog/agentic-ai-enterprise-security-model) apply directly here. ## 5. The 1.05M Context Window Is Shared — but Read the Fine Print Every GPT-5.6 tier ships with a **1,050,000-token context window** and **128,000 max output tokens**. That is a real capability, and the fact that even Luna gets the full window is genuinely useful. But the same caveat from every large-context model still holds: **a big window is not perfect recall.** Long-context retrieval quality degrades at the far edge of the window, and giant prompts carry hidden cost and latency. Treat 1M context as a tool for **broad synthesis and large working memory**, not as a replacement for retrieval discipline. ## Sol vs Terra vs Luna: The Decision If you only remember one section, make it this one. The simplest rule: - **Default to Terra.** It is the balanced everyday model and will be the right call for most production traffic. - **Escalate to Sol** when the task is genuinely hard — agentic coding, deep research, security review, or anything where a wrong answer is expensive. - **Drop to Luna** for high-volume, latency-sensitive, or cost-capped work where "good and fast and cheap" beats "best." I go much deeper on this — including the break-even math and a routing strategy — in the companion post: [GPT-5.6 Sol vs Terra vs Luna: which one to actually use](/blog/gpt-5-6-sol-vs-terra-vs-luna). ## Using GPT-5.6 in the API The models are available as `gpt-5.6-sol`, `gpt-5.6-terra`, and `gpt-5.6-luna`, with the alias `gpt-5.6` routing to Sol. Here is a minimal call: ```ts import OpenAI from 'openai'; const client = new OpenAI(); const response = await client.responses.create({ model: 'gpt-5.6-terra', // default workhorse; swap to sol/luna as needed input: 'Summarize this incident timeline and propose three remediation steps.', reasoning: { effort: 'medium' } }); console.log(response.output_text); ``` Escalating a single hard request to Sol with a higher thinking mode is a one-line change: ```ts const hard = await client.responses.create({ model: 'gpt-5.6-sol', input: 'Audit this auth module for vulnerabilities and produce a patch.', reasoning: { effort: 'high' } // dial up for long-horizon, high-stakes work }); ```