Choosing an AI Model API for Your Business: Grok, GPT, Claude or Gemini?

Choosing an AI model API feels like choosing a supplier of electricity, and in one respect it is: you will pay by usage. In every other respect it is more like choosing a recruitment partner. The candidates change every few weeks, their abilities overlap in confusing ways, and the real cost of a poor choice is less the invoice than the work built around them. This article gives you the list prices as they stood on 10 October 2026 and, more usefully, a way of choosing that survives the next price change.

We make no benchmark claims in this article. Public rankings change weekly and often measure tasks unlike yours. What we can verify is what each provider publishes about price and terms.

What you are really choosing

You are not choosing "an AI". You are choosing four things at once:

1. A capability level for your particular task.
2. A price structure, which includes more than the headline rate.
3. Data terms: what the provider does with what you send.
4. A relationship: how quickly it changes, retires or reprices models.

Each of these can be tested, and none of them is answered by a leaderboard.

List prices on 10 October 2026

Prices are in US dollars per million tokens, as published by each provider on the date shown. A token is a short fragment of text, roughly three-quarters of a word. "Cached" means the discounted rate when a long prompt prefix is reused.

Selected API list prices per million tokens (USD), 10 October 2026

Provider and model	Input	Output	Cached input	Notes from the provider's page
xAI Grok 4.7	$2.00	$6.00	$0.50	Rates double for prompts of 200k tokens or more. 500k context window.
OpenAI GPT-6.1 Sol	$2.00	$10.00	$0.10	Short-context rates. Long-context rates are higher. Batch processing is half price.
Anthropic Claude Sonnet 5.5	$2.00	$10.00	$0.10	Cache hits are 0.05 times the base input price. Batch is $1 and $5.
Google Gemini 3.8 Flash	$0.75 to 31 Dec 2026, then $1.50	$3.75 to 31 Dec 2026, then $7.50	$0.075 to 31 Dec 2026, then $0.15	Introductory pricing. Context-cache storage is charged per hour. Batch is half price.
OpenAI GPT-6 Luna (lower-priced)	$0.10	$0.50	$0.01	Short-context rates.
Anthropic Claude Haiku 5.5 (lower-priced)	$0.10	$0.50	$0.01	Prompts over 100,000 tokens are billed at $0.50 and $2.50.
Anthropic Claude Opus 5.5 (higher-priced)	$4.00	$20.00	$0.20	Cache hits are 0.05 times the base input price.
OpenAI GPT-6 Astra (higher-priced)	$10.00	$50.00	$1.00	Short-context rates.

A worked example

Suppose an assistant handles 10,000 requests a month. Each request sends about 3,000 tokens in (instructions, context and the user's message) and gets about 500 tokens back. That is 30 million input tokens and 5 million output tokens a month. These figures are an assumption for illustration. Substitute your own.

Illustrative monthly model cost at list prices for 30M input and 5M output tokens (USD)

Model	Calculation	Monthly cost
Grok 4.7	30 × $2.00 + 5 × $6.00	$90.00
GPT-6.1 Sol	30 × $2.00 + 5 × $10.00	$110.00
Claude Sonnet 5.5	30 × $2.00 + 5 × $10.00	$110.00
Gemini 3.8 Flash, to 31 Dec 2026	30 × $0.75 + 5 × $3.75	$41.25
Gemini 3.8 Flash, from 1 Jan 2027	30 × $1.50 + 5 × $7.50	$82.50
Claude Haiku 5.5 or GPT-6 Luna	30 × $0.10 + 5 × $0.50	$5.50

Two lessons stand out. First, the spread between the mid-priced models in this example is tens of dollars a month, which is small next to the hours spent building and checking the system. Second, a lower-priced model is more than ten times cheaper again, so it is worth testing whether the easy parts of your workload can go to one.

The Gemini line also shows a trap: introductory prices end. If a model's price doubles on 1 January 2027, your forecast should show both numbers.

Cost levers beyond the headline rate

• Caching. Reusing a long, stable prompt prefix is billed at a fraction of the normal input price on all four providers' pages. Structure prompts so the unchanging instructions come first.
• Batch processing. OpenAI, Anthropic and Google each list roughly half-price batch rates for work that can wait. That suits overnight classification and reporting, not live conversations.
• Long prompts. Some rates jump with prompt length: Grok 4.7 doubles at 200k tokens and Claude Haiku 5.5 rises above 100,000 tokens. Sending whole documents every time is expensive.
• Region. OpenAI's page says regional processing endpoints carry a 10 per cent uplift on eligible models released from 5 March 2026, and Anthropic's page lists a 1.1 times multiplier for US-only inference. If you need data kept in a particular region, price that option, not the default.
• Tools. Web search, file search and similar tools are charged separately on several pages. Check them if your assistant uses them.
• Exchange rate and VAT. Everything above is in dollars and excludes tax.

Data terms matter as much as price

Before you send a customer's details to any provider, read the terms for the exact plan you will use. Google's pricing page, for instance, states that on its free tier content is used to improve its products and on the paid tier it is not. Other providers have their own terms and enterprise options. Practical rules we would apply:

1. Never use a free or consumer tier for customer or confidential data.
2. Record which provider processes what, so you can answer a customer or regulator.
3. Minimise what you send. Strip names, account numbers and anything the model does not need.
4. Check where processing happens and how long data is retained.

If your assistant takes calls or chats with the public, you also have transparency duties, which we cover in the EU and UK disclosure rules for chatbots and voice agents.

How to choose: your own evaluation

1. 01Define the taskOne job, with a clear definition of a good answer.
2. 02Collect examplesReal, anonymised cases with agreed correct outcomes.
3. 03Test candidatesRun two or three models, plus a lower-priced option.
4. 04Score and costAccuracy, speed and cost per completed task.
5. 05Pin and monitorPin a version, watch results, retest on change.

A few details make this work.

• Use your own examples, including awkward and ambiguous ones. A model that is excellent on tidy test cases can stumble on real emails.
• Measure cost per completed task, not per token. A cheaper model that needs two attempts may cost more.
• Pin a model version in production. xAI's documentation, for example, explains that a plain model name is an alias that moves to the latest stable version, while a dated name refers to a specific release that will not change. Know which one you are calling.
• Retest on a schedule. Models and prices change often, so rerun your examples whenever you consider a switch or a provider announces a change.

Designing so you can switch

Lock-in rarely comes from the contract. It comes from code and prompts written around one provider's quirks. To keep options open:

1. Put a single internal interface between your application and any model, and keep provider specifics behind it.
2. Treat prompts, test cases and tool definitions as your own assets, stored in version control.
3. Use open conventions for connecting tools where you can. We explain one of them in the Model Context Protocol for business.
4. Log requests and outcomes, with appropriate privacy controls, so you have real data to compare providers.
5. Keep business rules and permissions in your systems rather than in prompts. Our article on how prompt injection works and what limits it explains why.

Common mistakes

• Choosing on a ranking, then discovering it does not match your task.
• Forecasting from the headline price and forgetting caching, tools and prompt length.
• Sending customer data on a tier that permits training use.
• Using the latest alias in production and being surprised when behaviour changes.
• Building a system with no test set, so nobody can tell whether a change helped.

How SystemNexa can help

We design and build AI-powered workflows that connect to your existing systems, and we are deliberately provider-agnostic. See our AI workflow automation service, our AI automation and API integrations work, and for voice-specific cost questions, what an AI voice agent costs in the UK.

Conclusion

The best model for your business is the one that passes your tests at a cost you can sustain on terms you can defend, and it may not be the one you choose first. Check list prices and their expiry dates, protect your data, test on real examples and design so a switch is a configuration change. If you would like help setting up that evaluation, get in touch about AI workflow automation.