GPT-6 Sol Pricing: Rates, Cost per Task, and Access

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GPT-6 Sol pricing is built around one idea: make sustained agent work cheap. OpenAI released Sol on September 22, 2026, at half the promotional API rate of its predecessor, and priced the whole GPT-6 family to reward high-volume use. This guide covers the full rate card, what a task actually costs, the fees most pages skip, and where you can access Sol. For the performance side, see our GPT-6 Sol benchmarks breakdown.

The short version: Sol is a mid-tier model priced well below flagship Astra, and its low cost per task makes it a strong default for agents, automations, and coding tools.

What GPT-6 Sol costs

GPT-6 Sol costs $2 per 1M input tokens and $10 per 1M output tokens on standard pricing. Cached input reads cost $0.20, and cache writes cost $2.50, both per 1M tokens. Every one of those rates is exactly half of GPT-5.6 Sol's.

Table 1 - GPT-6 Sol standard pricing versus GPT-5.6 Sol (per 1M tokens, as of September 2026).

The cached-input rate is the one to plan around. At $0.20 per 1M, it is a 90% discount on standard input, so an agent that rereads the same system prompt, tools, and codebase on every turn pays a fraction of the headline rate after the first pass.

How GPT-6 Sol pricing compares to GPT-5.6 Sol

The move from GPT-5.6 Sol to GPT-6 Sol halves the token rates. Input, cached input, cache writes, and output all drop by 50% against GPT-5.6 Sol's promotional rates, which OpenAI lists at least through November 21, 2026. On the performance side, Artificial Analysis reports broadly comparable overall intelligence, with results varying by benchmark and reasoning setting, while OpenAI reports improvements on several professional-work, factuality, coding, and computer-use evaluations.

That makes the upgrade straightforward for anyone already running GPT-5.6 Sol: a comparable class of model for half the spend, with a small trade on peak coding scores covered in the benchmarks guide.

Long-context and processing-tier pricing

Two rules change the rate you actually pay. First, long context: any request over 272,000 input tokens moves to a higher rate for the entire request. Second, the processing tier: Batch and Flex cost less than standard, while Fast mode costs more.

Table 2 - GPT-6 Sol standard versus long-context pricing (per 1M tokens).

Processing tiers then scale that rate. Batch and Flex both run at 50% of standard, which suits background jobs that tolerate latency. Fast mode runs at 2 times standard for latency-sensitive work. Regional data-residency endpoints add a 10% uplift, and for Sol, EU data residency is available only on standard processing.

What a GPT-6 Sol task actually costs

Per-token rates are only half the picture, because reasoning effort changes how many tokens a task burns. Artificial Analysis, which runs every model through the same harness, measures Sol's cost per task from $0.13 at low effort to $1.06 at max. That is an 8x range on the same model.

Table 3 - GPT-6 Sol cost per task by reasoning effort (independent, Artificial Analysis).

The effort dial is a pricing lever, not just a quality one. A routine extraction task at low effort costs a small fraction of the same task run at max, so matching the effort setting to the job is one of the biggest drivers of your bill. At max effort, Sol still costs about 50% less per task than GPT-5.6 Sol did.

Tool and add-on costs

Built-in tools bill on top of tokens, and this is where real workloads drift above the headline rate. Tokens that tools consume are charged at Sol's per-token rates, and several tools add a per-call or storage fee.

Table 4 - GPT-6 Sol built-in tool pricing.

For a tool-heavy agent that searches the web and runs code on every task, these fees can materially change the effective cost. Container sessions are billed by the minute with a five-minute minimum, so budget them alongside the per-token rate rather than treating tokens as the whole bill.

GPT-6 Sol vs Luna, Astra, and Claude Opus 5.5 on price

Across the GPT-6 family, price scales with capability tier, and Sol sits in the middle. Luna is the budget option, Astra the flagship, and Sol the default in between. Anthropic's Claude Opus 5.5 launched the same day at twice Sol's token rate.

Table 5 - GPT-6 family and Claude Opus 5.5 standard pricing (per 1M tokens, as of September 2026).

The gaps are wide. GPT-6 Astra pricing runs 5 times Sol's rate per token, which is why most pipelines reserve it for peak reasoning. At the other end, GPT-6 Luna costs a twentieth of Sol on input, making it the pick for high-volume classification and routing. Against Claude Opus 5.5, Sol is half the per-token price, though total cost per task depends on caching, tools, and how much each model reasons.

Where to access GPT-6 Sol and what it costs

GPT-6 Sol is available across several surfaces, and the cost basis differs on each. In the API you pay the per-token rates above. In ChatGPT and partner tools, it is bundled into the plan or the tool's own pricing.

Table 6 - Where to access GPT-6 Sol.

GPT-6 Sol is available on Emergent through a single credential, so you do not manage an OpenAI account or API key separately. Anthropic's rival Opus 5.5 is also live on Emergent as well, so you can weigh both inside one platform.

Build with GPT-6 Sol on Emergent

You can put GPT-6 Sol's low cost per task to work without touching the API. Emergent is a vibe coding platform where you describe the app you want and its multi-agent architecture builds, tests, and deploys the full stack for you.

The Universal LLM Key turns pricing into a single line item. It gives you GPT, Claude, and Gemini through one credential and unified billing on Emergent Credits, so you can run Sol for high-volume steps and route the rare hard step to a pricier model without juggling separate accounts.

Start Building on Emergent and ship your next app on GPT-6 Sol.