LLM API Pricing Comparison 2026: GPT vs Claude vs Gemini

Comparing GPT, Claude and Gemini in 2026 is harder than it looks, because the three families are priced on different scales, different context tiers and different caching rules. This guide explains how to read each provider's pricing, where the real differences are, and why a reseller gateway changes the entire picture.

The short version: in 2026, the frontier models from OpenAI, Anthropic and Google benchmark close enough that pricing becomes the deciding factor for most teams. And on pricing, resellers that pass through a fraction of official list prices are the cheapest way to run any of the three.

How LLM API pricing actually works

Every commercial LLM API bills per token — roughly, a unit of text on the input side and a unit on the output side. Two numbers anchor the bill: the input price per million tokens and the output price per million tokens.

Input is what you send (your prompt, system message and conversation history). Output is what the model generates. Output is almost always more expensive per token, because generation is slower and harder to serve than reading.

On top of that, providers add modifiers. Long context costs more. Cached input costs less, because repeating a long prompt can skip a chunk of the computation. Batch and background processing is cheaper than on-demand, and offline throughput targets trade price for latency.

The practical consequence is that the sticker price on a model page is only the start. Your real bill depends on how long your prompts are, how much history you repeat and whether you cache.

GPT, Claude and Gemini: the structural differences

Despite similar mechanics, the three families differ in how they expose pricing.

OpenAI's GPT family sets the reference pattern most of the industry copies: a per-million-token input and output price, with tiers for context and explicit cached-input discounts. OpenAI's official list prices are among the highest in the industry.

Anthropic's Claude family uses a comparable per-token structure but leans into long context and heavy caching. On long, repeated prompts the cached-input rate matters more than the headline number, and that is where Claude's effective price can be lower or higher than the sticker suggests.

Google's Gemini family has historically undercut both on list price, but with more segmentation — smaller "flash" models for throughput, larger "pro" models for quality, and different pricing for different context lengths and batch modes.

The comparison that actually matters is not "which sticker is lowest" but "what does my exact workload cost". That is why a living pricing table, tied to real per-model rates, beats a static blog post.

Where resellers change the math

Here is the part the official providers would rather you did not notice: list prices are not the floor. Resellers source model access from upstream and resell it with a much smaller margin than the provider's own list pricing reflects.

zcloud resells OpenAI-compatible and Anthropic-compatible models at roughly 5% of official pricing — up to 95% below the official list price. That single fact matters more than any of the fine-grained differences between GPT, Claude and Gemini, because it applies across all three families behind one API key.

So instead of asking "is Gemini cheaper than Claude?" you can ask "what does it cost through a reseller?" — and the answer, for all three, is a fraction of the sticker price. The pricing page is the living hub for this: it lists every model with its official price, its zcloud price and the percentage saved on input and output, pulled directly from the live catalog.

What a like-for-like comparison should include

To compare GPT, Claude and Gemini fairly, include all five of these in your math — not just the input price.

1. Input and output price per million tokens. Output dominates most workloads, so never compare on input alone.
2. Context length. Longer context costs more and matters if you ship long documents or long conversations.
3. Cached-input pricing. If you repeat a large prompt, the cached rate is what you actually pay.
4. Protocol and compatibility. OpenAI's chat/completions and responses, and Anthropic's messages, are the two protocols that matter. A gateway that speaks both lets you switch without rewriting code.
5. The reseller discount. A 95% discount swamps every other difference. Compare the official price to the reseller price first, then fine-tune among models.

Most pricing comparisons online skip the last one, which is the one that moves the needle most.

A worked example

Imagine a workload of one million input tokens and two hundred thousand output tokens per month — a small production app with modest traffic.

At official list prices, that workload costs a meaningful amount every month, and the exact number varies by provider and context tier. Multiply it by any real growth and the bill stops being pocket change.

Run the same tokens through a reseller at roughly 5% of official, and the monthly line drops by an order of magnitude. The model quality is identical — it is the same model served upstream — only the margin is different. That is the whole of it: quality is the same, price is structural, and the savings compound with every token.

You can see the exact numbers without signing up. The catalog shows per-model official-vs-reseller pricing side by side, including the percentage saved on both input and output.

GPT vs Claude vs Gemini: who is cheapest in 2026?

At official list prices, Gemini's smaller models often undercut equivalent GPT and Claude models on raw per-token cost, while Claude and GPT compete on quality and ecosystem. The ranking flips constantly as providers update prices, which is exactly why a static table in a blog post ages poorly.

Through a reseller, the question dissolves. All three families sit behind one OpenAI-compatible key, all at up to 95% off official pricing. The choice stops being "who is cheapest" and becomes "which model is right for this task" — because the price gap between them has been compressed by the discount.

That is the mental model to take into 2026: pick the model for quality and capability, and let the reseller handle the price. Then you get the best of both — the right model, at a fraction of what it lists for.

Frequently asked questions

Which is cheaper, GPT, Claude or Gemini? At official list prices, Gemini's smaller models are often cheapest, but the ranking shifts constantly. Through a reseller at up to 95% off, all three are far cheaper than any official list price, and the gap between them narrows significantly.

Why is output more expensive than input? Generation is slower and more compute-hungry than reading, so providers charge more per output token. Always weigh output price heavily in your comparison.

Does the discount change the model quality? No. A reseller serves the same upstream model; only the margin differs. You get identical output at a fraction of the price.

How do I see live prices? The zcloud pricing page lists every model with official price, reseller price and the savings percentage, refreshed from the live catalog.

Can I switch between GPT, Claude and Gemini easily? Yes — zcloud exposes every model behind one OpenAI-compatible key and also supports the Anthropic messages protocol, so switching is a single field change, not a rewrite.