GPT-6 Sol Pricing: Complete API Rates and Token Cost Breakdown

In September 2026, OpenAI introduced GPT-6 Sol, a mid-tier frontier Large Language Model (LLM) designed to deliver complex reasoning and autonomous agent execution at half the operating cost of its predecessor. The model sits between the high-capacity GPT-6 Astra and the lightweight GPT-6 Luna, providing high-effort problem solving for operational business systems.

Unlike the earlier GPT-5.6 Sol or standard Application Programming Interface (API) tiers, GPT-6 Sol cuts input token fees to $2 per million and output fees to $10 per million. On AutomationBench 1.0.6, which measures multi-step tool execution across sales, finance, and human resources software, GPT-6 Sol at extra-high effort outscored Claude Opus 5 by Anthropic while operating at 9 percent of that model's task cost.

For technical directors and operations leads budgeting automated business workflows, this pricing structure alters the feasibility of running persistent software agents. Multi-turn reasoning loops that previously cost tens of dollars per incident now fit into predictable monthly software budgets, provided teams understand token consumption patterns and know when to route simpler steps to cheaper models.

Official API Rate Card and Per-Token Costs

OpenAI charges $2 per million input tokens and $10 per million output tokens for GPT-6 Sol API access. These standard rates represent an exact 50 percent price reduction compared to the promotional pricing of GPT-5.6 Sol, which billed at $4 per million input tokens and $20 per million output tokens. Billing applies directly to net token throughput processed by the OpenAI API endpoints.

The model family also includes GPT-6 Luna at $0.10 per million input tokens and $0.50 per million output tokens. GPT-6 Luna costs 95 percent less than GPT-6 Sol on input and output, establishing a clear dual-tier structure for system architects. While OpenAI maintains GPT-6 Astra for uncompromising flagship reasoning, GPT-6 Sol serves as the primary production engine for demanding multi-step workflows.

Buyers should verify current figures on the official OpenAI website, as cloud infrastructure rates, volume tiers, and prompt caching discounts can shift without prior notice. Standard commercial terms bill output tokens at five times the input rate, making output conciseness a major factor in overall expense management.

Consumer, Team, and Enterprise Subscription Availability

OpenAI publishes explicit per-token billing rates for API developers, but specific inclusion rules across consumer, ChatGPT Team, and Enterprise subscription seats require direct verification in client administrative consoles. OpenAI regularly adjusts seat-based rate limits and model picker options as new generations deploy into general availability.

Commercial deployments operating via API keys run strictly on metered usage rather than fixed monthly user seat licenses. Organizations deploying internal autonomous tools incur costs on actual consumption through their API organization workspace, separated from individual employee ChatGPT Plus or Team subscriptions.

Companies subject to strict data handling standards should confirm whether zero-data-retention agreements and Business Associate Agreements for healthcare compliance apply to GPT-6 Sol endpoints under their contract. Standard API requests do not use submitted data for model training, but regulatory workflows require explicit administrative confirmation.

Benchmark Task Economics Against Flagship Competitors

On standardized agent evaluations, GPT-6 Sol completes complex business tasks at a fraction of the cost required by competing models. On AutomationBench 1.0.6, which evaluates automated agents across 47 enterprise software tools in finance, support, and operations, GPT-6 Sol operating at extra-high effort scored 33.2 percent with an average cost of $0.27 per task. Claude Opus 5 by Anthropic scored 26.9 percent while costing 11.1 times more per task.

The model also achieved higher task completion than Claude Fable 5.1, which scored 31.4 percent while requiring Opus fallback routing on approximately 40 percent of runs, pushing its task expense beyond 8.9 times GPT-6 Sol. Even compared to low-effort runs of OpenAI's own GPT-6 Astra, which scored 30.3 percent at 3.9 times the cost, GPT-6 Sol demonstrated superior economic efficiency on workflow automation.

On Agents' Last Exam V1, an evaluation covering multi-hour professional workflows across 55 computer-based industries, GPT-6 Sol scored 56.4 percent at maximum effort. This result surpassed the highest score achieved by Claude Opus 5 while reducing task costs by 60 percent. Internal evaluations also showed GPT-6 Sol cutting factual errors in half relative to GPT-5.6 Sol, reaching reliability close to Astra.

• AutomationBench 1.0.6 score: 33.2 percent at $0.27 per task (GPT-6 Sol xhigh effort).
• Claude Opus 5 comparison: 26.9 percent score at 11.1 times the cost per task.
• Claude Fable 5.1 comparison: 31.4 percent score at over 8.9 times the cost per task.
• Agents' Last Exam V1 score: 56.4 percent at 60 percent lower cost than Claude Opus 5.
• Factuality error rate: reduced by approximately 50 percent compared to GPT-5.6 Sol.

Worked Monthly Workload Budget for an SMB Workflow

A mid-sized company running an automated customer onboarding and document review workflow can calculate monthly API expenses using projected token throughput. Consider an operation handling 1,500 client intake files each month, where each matter involves reading intake questionnaires, reviewing contracts, cross-referencing internal databases, and drafting structured summaries.

Assuming each intake file consumes an average of 40,000 input tokens across prompt templates, system instructions, and uploaded documentation, the monthly input volume totals 60 million tokens. At $2 per million tokens, raw input costs equal $120 per month. If each case produces 4,000 output tokens of structured analysis and client correspondence, the monthly output volume totals 6 million tokens, costing $60 at $10 per million tokens.

The combined direct API cost for this 1,500-case monthly workload equals $180 under standard GPT-6 Sol pricing, compared to $360 under previous GPT-5.6 Sol rates. Factoring in an extra 20 percent buffer for agent retry loops and tool-call overhead brings the realistic operating budget to $216 per month. This expenditure replaces dozens of manual review hours while remaining completely transparent on a balance sheet.

Who This Pricing Suits and When to Route to Smaller Models

GPT-6 Sol pricing suits organizations running multi-step reasoning agents, deep legal or financial document analysis, and complex code refactoring where factual precision outweighs millisecond response times. Teams managing high-liability workflows benefit from the model's 50 percent error reduction without paying the premium rates associated with GPT-6 Astra.

This rate card is not suitable for basic data extraction, high-frequency classification, or short conversational chat widgets. Running high-volume, low-complexity tasks on GPT-6 Sol wastes capital when GPT-6 Luna handles rote classification at $0.10 per million input tokens and $0.50 per million output tokens, delivering a 95 percent cost reduction.

At Layer3Labs, we examine workflow traces across client automation deployments to establish model routing boundaries before putting systems into production. Teams achieve the best return on investment by routing initial document parsing, sentiment detection, and data formatting to GPT-6 Luna, escalating only ambiguous edge cases, contract discrepancies, and complex tool-use loops to GPT-6 Sol.

Evaluating Model Economics and Next Operational Steps

Our economic verdict on GPT-6 Sol would change if competing frontier labs drop flagship rates below $1 per million input tokens or if OpenAI discontinues prompt caching concessions for agentic loops. Current performance data indicates that GPT-6 Sol delivers exceptional commercial value for autonomous software workflows across mid-market enterprises.

Engineering teams should review their token telemetry logs, identify repetitive classification steps that can migrate to GPT-6 Luna, and calculate projected savings from Sol's 50 percent price reduction. Check OpenAI's developer portal to verify production rate limits before upgrading production endpoints.

To implement these cost efficiencies inside your organization, run a pilot test on an existing multi-step task to measure your actual token consumption under current GPT-6 Sol pricing.