GPT-6 Sol: a strong model for code and automation at a sensible price

GPT-6 Sol is the model in OpenAI's GPT-6 series that sits between the flagship GPT-6 Astra and the fast GPT-6 Luna. It is built for complex professional work, programming, and process automation, with factual accuracy on par with Astra. It has been available on Genosai since September 27, 2026: a 256,000-token context, image input, and a price of 160 / 800 ₽ per 1M tokens (input / output).

Updated: September 27, 2026

• Long tasks in real code — According to OpenAI, it is especially effective at long-horizon software engineering on real codebases, not just textbook exercises.
• Astra-level accuracy — Factual accuracy on par with the flagship GPT-6 Astra, at a client price five times lower.
• Reasoning effort — From none to xhigh: an instant answer for simple questions and deep thinking for math, logic, and code.
• 256,000-token context — A large slice of a project, a set of regulations, or a long conversation fits into one request.
• Vision and tools — Accepts JPG, PNG, and WebP, up to 10 files per request, and supports function calling for agentic workflows.

Contents

• What is GPT-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-6 Sol

GPT-6 Sol is the middle model of OpenAI's GPT-6 series. Above it sits the flagship GPT-6 Astra; below it, the fast and most affordable GPT-6 Luna. OpenAI describes Sol as a cost-efficient, high-performance model built for complex professional tasks, automated programming, business-process automation, and computer use.

The provider singles out long-horizon software engineering on real codebases. That is not a textbook exercise but work where the model has to keep many files, dependencies, and conventions in mind, go through several steps, and not lose the thread. Another key trait is factual accuracy on par with Astra, even though Sol costs several times less. The model's style is clear and concise, especially on technical and programming topics: less filler, more substance.

GPT-6 Sol arrived on Genosai on September 27, 2026. The context window is 256,000 tokens, input can be text and images, and output is text. Function calling is supported, so Sol works well as the brain of agents and automations. The client price is 160 / 800 ₽ per 1M tokens (input / output): five times cheaper than Astra and 2.5 to 3 times cheaper than the previous flagship GPT-5.6 Sol.

Capabilities

GPT-6 Sol is at its best where a task needs precision and several steps but does not justify flagship pricing.

Long engineering tasks

Sol writes and edits code, finds the causes of bugs, proposes refactoring, and checks edge cases. The 256,000-token context lets you pass several modules, an architecture note, and an error log at once, so the model reasons about the project as a whole rather than a single function. For tricky logic, turn on reasoning effort: the model thinks the solution through first and answers afterwards.

Business-process automation and agents

With function calling, Sol can run inside automations: parse incoming leads, fill CRM fields, choose the next action, and draft replies. The model holds a requested structure well — a table, a list of steps, JSON — so its output can be passed down the chain without manual cleanup.

Accuracy and a concise style

Technical documentation, explanations, and internal guides need two things: no made-up facts and no rambling. According to OpenAI, Sol is close to Astra on factual accuracy and answers briefly and to the point. That saves reading time as well as the output tokens you pay for.

Image input

The model accepts JPG, PNG, and WebP, up to 10 files per request. Send an interface screenshot, a diagram, a photo of a document, or a chart and ask it to analyze the content, find a mistake, or describe the structure.

Examples prompt and response

Below are real GPT-6 Sol responses obtained through the Genosai API. The first is a bug hunt with reasoning effort set to medium, the second is a lead-handling automation outline, and the third is a short technical explanation. Responses are quoted verbatim.

Prompt: Find the bugs in this JavaScript function, explain briefly, and give a fixed version. No preamble.

function chunk(arr, size) {
  const out = [];
  for (let i = 0; i <= arr.length; i += size) {
    out.push(arr.slice(i, i + size));
  }
  return out;
}

GPT-6 Sol response:
- `i <= arr.length` adds an empty chunk when `i` reaches `arr.length`, and returns `[[]]` for an empty array.
- `size <= 0` causes an infinite loop because `i` never advances meaningfully.
- Non-integer or invalid sizes can also produce unexpected behavior.

function chunk(arr, size) {
  if (!Array.isArray(arr)) {
    throw new TypeError("arr must be an array");
  }
  if (!Number.isInteger(size) || size <= 0) {
    throw new RangeError("size must be a positive integer");
  }

  const out = [];
  for (let i = 0; i < arr.length; i += size) {
    out.push(arr.slice(i, i + size));
  }
  return out;
}

_Generated on Genosai.io with GPT-6 Sol._

Prompt: Describe in 5 steps how a small company can automate handling website leads: from the form to the first reply to the client. For each step, name a tool and what to check. Be concise.

GPT-6 Sol response:
1. **Capture the lead** — Use **Webflow Forms** or **Typeform**. Check that required fields, consent, and spam protection work.

2. **Send data to a CRM** — Use **Zapier** to connect the form to **HubSpot CRM**. Check that every submission creates the right contact and source attribution.

3. **Notify the team** — Use **Slack** or **Microsoft Teams** through Zapier. Check that notifications arrive promptly with the lead’s key details.

4. **Send the first reply** — Use **HubSpot automated emails** or **Mailchimp**. Check personalization, sender details, mobile formatting, and that replies reach a monitored inbox.

5. **Track and improve** — Use **HubSpot reporting** or **Google Analytics 4**. Check delivery, open/reply rates, response time, duplicate leads, and failed automations.

_Generated on Genosai.io with GPT-6 Sol._

Prompt: Explain in 4-5 sentences why a payment API needs idempotency and how to implement it.

GPT-6 Sol response:
A payment API needs idempotency because clients may retry a request after a timeout, and a retry must not charge the customer twice. Have the client send a unique idempotency key for each intended payment, and store that key with the request parameters and resulting response. When the same key is received again, return the stored result instead of creating another payment; reject it if the parameters differ. Make the key check and payment creation atomic, and retain keys long enough to cover the expected retry window.

_Generated on Genosai.io with GPT-6 Sol._

Note the style: no introductions, problems listed straight away, and the requested format respected — exactly five steps or a fixed number of sentences.

How to use on Genosai

1. Open the Genosai studio on the Text tab.
2. Pick GPT-6 Sol in the model list.
3. If needed, open the chat settings panel: temperature, top_p, a response limit of up to 32,768 tokens, reasoning effort, and a system prompt.
4. Describe the task: goal, context, and answer format. Attach images if they help.
5. Read the answer and refine it in the same thread — the history is saved.

Billing is per token with no subscription, and you can top up your balance with a Russian bank card.

Reasoning effort

The chat settings panel has a Reasoning effort option. For GPT-6 Sol the values are none, low, medium, high, and xhigh. The default is none: the model answers at once without thinking first. That is fast and cheap, but it can let you down on tasks with a catch.

We checked this on a task that looks simple: list every natural n below 60 for which n² + n + 41 is composite, and output only the list. With none, Sol answered in about 4 seconds, and the list was wrong. With high, the model thought for 30 to 60 seconds, spent roughly 1,000 to 3,000 reasoning tokens, and returned the correct answer: 40, 41, 44, 49, 56. The takeaway is simple: turn reasoning on for math, logic, and complex code, and keep none for copy and short reference answers.

Through the API

The Genosai API follows the OpenAI format, so the usual SDKs and libraries work. The model list is available at GET /v1/models.

curl https://api.genosai.io/v1/chat/completions \
  -H "Authorization: Bearer sdk_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-6-sol",
    "reasoning_effort": "medium",
    "messages": [{"role": "user", "content": "Find the bug in this function..."}]
  }'

Prompts

The prompts below play to Sol's strengths: code, structure, and accuracy. Stating the answer format explicitly works well — a table, a number of points, a length limit.

Here are three project modules and an error log. Find the cause of the failure, explain the mechanism, and propose a fix with a test.

Draft a 6-step refactoring plan for this service. For each step, give the risk and how to verify it.

Parse this incoming lead and return JSON with the fields: name, phone, request summary, urgency, responsible department.

Describe the invoice approval process as a table: step, owner, deadline, what to automate.

Explain in 5 sentences the difference between a message queue and webhooks for a backend beginner.

Look at this form screenshot and list what prevents a user from completing it.

Generation cost

The client price of GPT-6 Sol on Genosai is 160 / 800 ₽ per 1M tokens (input / output). Cached input has its own price of 32 ₽ per 1M; caching is applied automatically when the provider uses it, but it is best not to count on it in advance.

For a rough budget: a request with 2,000 input tokens and a 1,000-token answer costs about 1.1 ₽. With reasoning effort turned on, thinking tokens are billed as output tokens: 3,000 reasoning tokens add roughly 2.4 ₽. So switch to high where it actually matters, not for every question.

There is no subscription; only actual usage is charged, and top-ups work with Russian bank cards. Current prices are in the Pricing section.

How it compares

Sol covers the middle of the GPT-6 series and, on price, competes with Terra-class models rather than with flagships.

Compared with GPT-6 Astra, Sol is five times cheaper with comparable factual accuracy, so keep Astra for the heaviest tasks. GPT-6 Luna costs 20 times less and suits classification, short copy, and bulk processing. If you have been on GPT-5.6 Sol, moving to GPT-6 Sol cuts the price by 2.5 to 3 times. GPT-5.6 Terra has the same input price but costs more on output, while Sol is a newer generation. Among competitors, look at Claude Sonnet 5: 190 / 940 ₽ per 1M and an 800,000-token context when you need to load a very large volume.

Limitations and tips

GPT-6 Sol has no built-in web search: it does not know fresh news, exchange rates, or prices. If the answer depends on current data, pass that data in the request. Output is text only — the model analyzes images but does not draw them.

The main tip is to manage reasoning effort deliberately. The none mode is fast, but, as our number test showed, it can confidently return a wrong result where a calculation is needed. For math, logic, algorithms, and complex code, choose medium or high.

State the answer format explicitly: a table, a list, JSON, a length limit. Sol holds such constraints well. For code, pass the full context — related files, library versions, the error text.

Finally, verify the result manually when money, security, or important decisions depend on it. Even an accurate model makes mistakes: run the code, and check numbers and facts against the source.

FAQ

What is GPT-6 Sol?

It is a model in OpenAI's GPT-6 series, the middle tier between the flagship GPT-6 Astra and the fast GPT-6 Luna. OpenAI positions it as a cost-efficient, high-performance model for complex professional tasks, programming, business-process automation, and computer use.

How much does GPT-6 Sol cost on Genosai?

The client price is 160 / 800 ₽ per 1M tokens (input / output), with cached input at 32 ₽ per 1M. There is no subscription: you pay only for the actual size of the request and the response. Reasoning tokens are billed as output tokens.

How does Sol differ from GPT-6 Astra?

Astra is the series flagship; Sol is the more affordable model with factual accuracy on par with Astra. For the client, Astra costs 800 / 4000 ₽ per 1M tokens and Sol costs 160 / 800 ₽, five times less. For most coding and automation work, Sol is enough.

What is reasoning effort and when should I turn it on?

It is a setting that lets the model think before answering: none, low, medium, high, or xhigh. The default is none, and Sol answers right away. For math, logic, and complex code, pick medium or high: the answer takes longer but is noticeably more reliable.

What context and files does the model support?

Up to 256,000 tokens of context per request. The model accepts JPG, PNG, and WebP images, up to 10 files at a time, and responds with text. It has no built-in web search, so fresh data has to be passed in the request.

Can I use GPT-6 Sol through an API?

Yes. The Genosai API follows the OpenAI format: send a request to /v1/chat/completions with the model id gpt-6-sol and an sdk_... key. Function calling and the reasoning_effort parameter are supported.

Try GPT-6 Sol on Genosai