GPT-5.6 Sol: a flagship for complex agentic work
GPT-5.6 Sol is the flagship of OpenAI's GPT-5.6 series, generally available since July 9, 2026. It sets records in coding and browser work: 92.2% on BrowseComp and 80 points on the Artificial Analysis Coding Agent Index. On Genosai it holds 256,000 tokens of context and is billed by tokens: 400 ₽ per 1M input and 2400 ₽ per 1M output.
Updated: July 23, 2026
- Records in agentic work — 92.2% on BrowseComp and 62.6% on OSWorld 2.0, both state of the art at release.
- Best model for code — 80 points on the Artificial Analysis Coding Agent Index using less than half the output tokens.
- Token efficiency — It reaches the result with fewer tokens, so a long task costs less than the rate suggests.
- 256,000-token context — A large project, a body of documents, or a long conversation fits in a single pass.
- Vision and files — Accepts images in JPG, PNG, and WebP formats, up to 10 files per request.
Contents
- What is GPT-5.6 Sol
- Capabilities
- Examples prompt and response
- How to use on Genosai
- Prompts
- Generation cost
- How it compares
- Limitations and tips
- FAQ
What is GPT-5.6 Sol
GPT-5.6 Sol is the top model in OpenAI's GPT-5.6 series. The series became generally available on July 9, 2026, after a limited preview, and comes in three tiers: Sol the flagship, Terra the balanced model for everyday work, and Luna the most affordable. Sol targets frontier reasoning and long-horizon agentic tasks.
The headline of the release is not simply "smarter" but "more intelligence per token." OpenAI emphasizes performance per dollar: the same outcome reached with fewer tokens and in less time. On the Artificial Analysis Intelligence Index, Sol at maximum reasoning lands within one point of Claude Fable 5 while completing tasks 61% faster and at roughly half the estimated cost.
The agentic numbers are concrete too. On Agents' Last Exam, a test of long professional workflows across 55 domains, Sol scores a record 53.6, leading Fable 5 by 13.1 points. It reaches 92.2% on BrowseComp and 62.6% on OSWorld 2.0, both state of the art at release. On OSWorld it beats Claude Opus 4.8 while using 85% fewer output tokens.
On Genosai, Sol has a 256,000-token context window and image support. Billing is by tokens: for the client, 400 ₽ per 1M input and 2400 ₽ per 1M output — the same as the previous flagship GPT-5.5, at noticeably higher benchmark results. Moving to the new generation therefore does not require revisiting your budget: same price, better output.
Capabilities
GPT-5.6 Sol shows its strength where the task is long and multi-step.
Agentic scenarios and tool use
Sol builds a plan, calls tools, and carries the chain of actions to the end. A distinct feature of the series is programmatic tool calling: the model writes and runs lightweight programs that orchestrate tools, filter intermediate results, and choose the next action as work proceeds. The practical effect is fewer tokens, fewer model calls, and less hand-holding at every step. For a user it looks simple: a complex task can be stated in one request instead of walking the model through each stage.
Coding
OpenAI calls Sol its best coding model to date. On the Artificial Analysis Coding Agent Index it scores 80 points, 2.8 above Fable 5, using less than half the output tokens and costing roughly a third less. It also leads on Terminal-Bench 2.1 and DeepSWE, which measure complex command-line workflows and long engineering tasks in real codebases rather than isolated exercises. For a developer that means the model works both as an editor assistant and as the basis for automations where each step matters.
Design and front-end
The series makes a visible step forward in design decisions. From a high-level description Sol assembles interfaces that look clean and actually work, and improved computer use lets it inspect the rendered result and refine it rather than only generating code. That changes output quality on front-end tasks: you get a result you can show rather than a draft that needs finishing.
Knowledge work and large materials
Sol turns scattered context — documents, correspondence, notes — into coherent expert-level material. Combined with the 256,000-token window, this covers a common workflow: hand over an entire report, a body of regulations, or meeting transcripts and get a summary that rests on the full volume rather than a conveniently chosen fragment.
Examples prompt and response
Below are real GPT-5.6 Sol responses produced on Genosai. The first is a content plan, the second explains marketing metrics with a worked calculation. Texts are quoted verbatim, with the long answer shortened.
Prompt: Draft a Telegram post outline for launching a new cosmetics line: 5 bullet points with short subheadings.
GPT-5.6 Sol response:
- **✨ Big Reveal** — Introduce the new cosmetics line and its inspiration.
- **💄 Meet the Collection** — Spotlight hero products, shades, and key benefits.
- **🌿 Beauty Standards** — Highlight ingredients, formulas, and cruelty-free credentials.
- **🎁 Launch Offer** — Share limited-time discounts, gifts, or bundles.
- **🛍 Shop Now** — Add the launch date, purchase link, and clear call to action._Generated on Genosai.io with GPT-5.6 Sol._
Prompt: Explain in simple terms the difference between ROAS and ROMI in online advertising, with a numeric example.
GPT-5.6 Sol response:
**ROAS (Return on Ad Spend)** measures how much revenue your ads generate compared with the amount spent on ads.
> **ROAS = Revenue from ads ÷ Ad spend**
**ROMI (Return on Marketing Investment)** measures profit relative to the broader marketing cost. It can include ad spend, agency fees, creative production, software, and other campaign expenses.
### Example
An online campaign produces:
- Revenue: **$10,000**
- Cost of products sold: **$4,000**
- Ad spend: **$2,000**
- Creative and agency costs: **$1,000**
**ROAS:**
> $10,000 ÷ $2,000 = **5.0**, or **500%**
This means every $1 spent on ads generated $5 in revenue._Generated on Genosai.io with GPT-5.6 Sol._
How to use on Genosai
- Open the model catalog on Genosai and pick GPT-5.6 Sol among the text models.
- Describe the task in the chat: goal, tone, format, and answer length.
- Attach files if needed — a document, data, or an image — and refer to them in the request.
- Send the request and read the answer as it streams, without waiting for the full generation.
- Refine in the same thread: ask to shorten, add an example, or restructure.
A practical rule for a flagship: do not spend it on what a smaller model handles. Sol pays off where the task is long and made of several dependent steps — analyze a project, find a non-obvious cause of a failure, assemble material from several sources. Genosai runs in the browser with no installation, signing up takes a minute, and billing is by actual usage, so you can start with a single request and judge the model on your own task. Chat history is saved, so a long analysis is easy to continue later in the same thread.
Prompts
The prompts below play to Sol's strengths: multi-step logic, code, and structured analysis. The general principle is to specify not only the topic but the structure of the answer: sections, number of points, table format. The model holds those constraints well and returns a result that is ready to use without rework.
Analyze the attached project and draft a 6-step refactoring plan with dependencies between the steps.Find the cause of the bug in this code, explain the mechanism, and propose a fix with edge-case handling.Build one executive brief from the attached documents. Format: 5 sections, up to 4 points each.Design a report page interface: block structure, what to display, which states to account for.Draft a strategy for solving this task, list the risks, and say at which step to check each one.Analyze the attached interface screenshot and suggest improvements to the layout and the copy.Check the text for factual errors and flag the places that need a source check.Generation cost
On Genosai, GPT-5.6 Sol is billed by tokens — you pay for the actual size of the request and the response, so a short question costs less than a long analysis. The client price is 400 ₽ per 1M input tokens and 2400 ₽ per 1M output tokens.
There is a detail here that is easy to miss when comparing rates. Billing counts tokens, and Sol is deliberately trained to solve a task with fewer of them: in coding tests it delivers results on less than half the output tokens of a competing flagship. The rate line and the final bill for a task are therefore different things, and on long scenarios the gap works in your favor.
A starter balance after sign-up lets you try GPT-5.6 Sol for free. Current rates and your balance are in the Pricing section.
How it compares
Within the GPT-5.6 series the choice comes down to how hard the task is. Sol is the top step; Terra and Luna sit below it.
| Model | Context (Genosai) | Price ₽/1M (in/out) | Files | Niche |
|---|---|---|---|---|
| GPT-5.6 Sol | 256,000 | 400 / 2400 | yes | Flagship for hard tasks |
| GPT-5.6 Terra | 256,000 | 200 / 1200 | yes | Balance of power and price |
| GPT-5.6 Luna | 256,000 | 80 / 480 | yes | Economy and volume |
| GPT-5.5 | 250,000 | 400 / 2400 | yes | Previous-generation flagship |
For everyday work, take GPT-5.6 Terra — the same 200/1200 ₽ per 1M as GPT-5.4, but a new generation. For high-volume tasks and classification, GPT-5.6 Luna fits. The previous generation, GPT-5.5, costs exactly as much as Sol, so there is little reason to stay on it. Among flagship-class rivals, compare Claude Opus 4.8.
A practical rule of thumb: start with Terra and step up to Sol where you feel a limit — the model starts losing the thread in a long chain, or simplifies where precision is required. On Genosai all models are available in one account, so switching takes seconds and needs no separate subscriptions.
Limitations and tips
Sol is an expensive model by platform standards, and that is its main limitation. On simple tasks you overpay for depth that goes unused: short copy, translation, or classification are handled confidently by Luna at a fraction of the price.
The higher-effort and parallel-agent modes OpenAI describes are features of its own API and products. In the Genosai studio the effort level is not exposed and the provider default applies, so plan around the model's ordinary behavior.
A practical tip: describe the task as a whole rather than step by step. Sol is strong precisely at long-range logic, and splitting the work into small errands loses its main advantage while paying for several requests instead of one.
One more technique is to state quality criteria directly in the request. Describe what counts as a good answer, which phrasings to avoid, and which data must be preserved. The more precisely you describe the expected result, the fewer iterations you spend on rework. And if the topic needs fresh or exact data, ask the model to flag the places that need a source check.
FAQ
What is GPT-5.6 Sol?
It is the flagship model of OpenAI's GPT-5.6 series, generally available since July 9, 2026. It is built for frontier reasoning, coding, and long-horizon agentic work. OpenAI reports state-of-the-art results for it in coding, knowledge work, cybersecurity, and science.
How does Sol differ from Terra and Luna?
Sol is the top tier of the series, Terra balances capability and cost, and Luna is the most affordable. On Genosai the client price per 1M tokens (input/output) is: Sol 400/2400 ₽, Terra 200/1200 ₽, Luna 80/480 ₽. Pick Sol where the task is genuinely hard and the cost of a mistake is high.
What is the context window on Genosai?
Up to 256,000 tokens per request. That covers a large document, a substantial slice of a codebase, or an entire long conversation. The model also accepts images, up to 10 files at a time.
How much does a GPT-5.6 Sol request cost?
Billing is by tokens: for the client, 400 ₽ per 1M input tokens and 2400 ₽ per 1M output tokens. The exact amount depends on the size of the prompt and the response. That matches the previous flagship GPT-5.5 while delivering higher benchmark results.
How strong is GPT-5.6 Sol at coding?
OpenAI calls it their best coding model to date. It scores 80 points on the Artificial Analysis Coding Agent Index and leads on Terminal-Bench 2.1 and DeepSWE, which test complex command-line workflows and long engineering tasks in real codebases.
When is Terra enough instead of Sol?
Terra suits everyday work: copy, document analysis, ordinary code. For the client it costs 200/1200 ₽ per 1M tokens against 400/2400 ₽ for Sol, and it holds its own against strong flagships in coding tests. Reach for Sol on genuinely complex multi-step tasks.