GPT-6 Sol pricing: API cost, examples, calculator, and subscription choices

GPT-6 Sol pricing is easy to misread because two questions get mixed together. An API quote is a metered calculation based on tokens and a provider rate card. A subscription/workflow route is a broader decision about how often you need one model, several models, and connected tools.

This guide uses GPT-6 Sol in GlobalGPT for the hands-on checks. OpenAI’s official release page lists GPT-6 Sol at $2 per million input tokens and $10 per million output tokens, with a 90% discount on cached input reads (about $0.20 per million cached input tokens). The calculator below uses those published rates when you enter them and keeps the subscription route separate.

For broader interactive work, GlobalGPT keeps GPT-6 Sol alongside other models and AI functions in one dashboard. That matters when the real job moves from research to drafting, code, images, video, or a CLI-connected workflow.

A GlobalGPT-Arbeitsbereich offers a subscription route across models and AI functions. That is a different purchase from a provider’s metered API calls. The GPT-6 Sol review explains where the model fits into a broader workflow.

What “GPT-6 Sol pricing” can mean

There are three separate cost questions: the API’s input/output token meter, optional rules such as cached or batch processing, and the value of a subscription that bundles access and workflow tools. Only the first two can be reduced to a token formula.

• API: best when software sends predictable requests and you need a traceable per-call estimate.
• Subscription/workflow: best when people switch among models and functions during a broader task.
• Mixed route: useful when production calls are programmable but exploration, writing, or multimodal work is interactive.

Official API pricing status

OpenAI lists GPT-6 Sol at $2 per million input tokens and $10 per million output tokens. The same release page says cached input reads receive a 90% discount, which works out to $0.20 per million cached input tokens. OpenAI presents these rates as 50% lower than GPT-5.6 Sol promotional pricing.

At the same token count, output costs five times as much as uncached input. Keeping an answer concise can matter as much as shortening the prompt.

The discount turns $2 per million input tokens into $0.20 per million qualifying cached reads. A repeated prompt is not proof of a cache hit; use recorded cached-token usage when reconciling actual costs.

Worked estimates at the published rates

The following examples use the published GPT-6 Sol rates. They show how token counts turn into an API estimate; they are not a subscription price.

Divide tokens by one million before multiplying by the per-million rate. Apply the cached rate only to input tokens that actually qualify for a cache hit; charge the remaining input at the standard rate.

Cost = (total input − cached input) / 1M × $2
     + cached input / 1M × $0.20
     + output / 1M × $10

Interactive API and workflow calculator

Use the published GPT-6 Sol rates as the starting values: $2/M input, $10/M output, and $0.20/M cached input. The route selector explains the GlobalGPT decision boundary without mixing a subscription price into the API arithmetic.

Hands-on GPT-6 Sol checks

These checks use GPT-6 Sol in GlobalGPT. They show how the model handles pricing math, structured data, and calculator code; the provider billing table above comes from OpenAI’s published release page. P01 and P02 retain their first outputs. P03 was retried once because its first response stopped at the output limit before completing the function.

You are testing a pricing explainer. Use only this supplied fictional rate card: input $2.00 per million tokens, output $8.00 per million tokens. Question: what would 12,500 input tokens and 2,000 output tokens cost? Show the formula, label the result as fictional, and say what information is still needed for a real provider quote.

**Fictional cost:**  
(12,500 ÷ 1,000,000 × $2.00) + (2,000 ÷ 1,000,000 × $8.00) = **$0.041** (4.1¢).

For a real provider quote, you’d need the provider and model, their current rates, and any applicable discounts, extra charges, or taxes.

Return a compact JSON object with keys input_cost, output_cost, total_cost, assumptions, unknowns. Use this fictional ledger: 1,200,000 input tokens, 300,000 output tokens; rates are input $3/M and output $12/M; cached input rate is not supplied. Do not add a cached discount or any provider fact.

{"input_cost":3.60,"output_cost":3.60,"total_cost":7.20,"assumptions":["USD; all input tokens billed at $3/M with no cached discount"],"unknowns":["cached input rate","cached input token count"]}

Write a complete, runnable JavaScript function estimateTokenCost(inputTokens, outputTokens, inputRatePerMillion, outputRatePerMillion) for a pricing calculator. Requirements: return a clear validation error for empty, non-numeric, negative, or non-finite values; accept zero only when both token counts are zero; use ordinary arithmetic; include one normal example and one very large finite example without causing Infinity; do not claim these rates are real. Return only the complete JavaScript code and a short note.

```javascript
function estimateTokenCost(
  inputTokens,
  outputTokens,
  inputRatePerMillion,
  outputRatePerMillion
) {
  const values = {
    inputTokens,
    outputTokens,
    inputRatePerMillion,
    outputRatePerMillion
  };

  for (const [name, value] of Object.entries(values)) {
    if (typeof value !== "number" || !Number.isFinite(value)) {
      return { error: `${name} must be a finite number.` };
    }
    if (value < 0) {
      return { error: `${name} cannot be negative.` };
    }
  }

  const bothCountsZero = inputTokens === 0 && outputTokens === 0;
  if (!bothCountsZero && (inputTokens === 0 || outputTokens === 0 ||
      inputRatePerMillion === 0 || outputRatePerMillion === 0)) {
    return { error: "Zero values are allowed only when both token counts are zero." };
  }

  const cost =
    (inputTokens / 1_000_000) * inputRatePerMillion +
    (outputTokens / 1_000_000) * outputRatePerMillion;

  if (!Number.isFinite(cost)) {
    return { error: "The calculated cost is not finite." };
  }

  return { cost };
}

// Illustrative rates only; these are not real prices.
console.log(estimateTokenCost(1_000, 500, 2, 4));
console.log(estimateTokenCost(1e150, 2e150, 3e150, 4e150));
```

Note: The examples use hypothetical rates.

API vs GlobalGPT workflow route

Price is only one part of the decision. An API can be the right instrument for a backend call even when a subscription/workflow route is better for the surrounding human work.

Collect real token counts from your application, calculate API exposure with the verified rate card, and separately estimate the value of the multi-model dashboard across the rest of the task. Keep those ledgers separate.

FAQ

What is the official GPT-6 Sol API price?

OpenAI lists GPT-6 Sol at $2 per million input tokens and $10 per million output tokens. Cached input reads receive a 90% discount, equivalent to $0.20 per million cached input tokens on the published release page.

Is GPT-6 Sol included in a GlobalGPT plan?

Do not infer plan inclusion from a model directory. Check the current GlobalGPT plan page and model mapping shown for your account.

How do I estimate one API request?

Subtract cached input from total input first. Charge uncached input at $2/M, eligible cached input at $0.20/M, and output at $10/M. Add separately documented tool or other charges where applicable.

Should I use an API or a subscription?

Use the API for programmable metered calls with a verified rate card. Consider GlobalGPT when your work spans several models and AI functions in one dashboard. A mixed route can make sense when production calls and interactive work have different needs.

Make the rate card do the arithmetic, and make the workflow do the deciding. Recheck live pricing and plan terms before committing budget.