Picking between GPT, Claude, and Gemini usually comes down to more than which one seems smartest. Cost per token, context window size, and how the pricing is structured all shape which one actually makes sense for what you’re building. Here’s how they stack up.
A quick rundown of the three
OpenAI’s GPT models are the most widely adopted option out there, with a range running from lightweight mini models up to flagship reasoning models. Pricing scales pretty predictably with capability, and the tooling and documentation around it are some of the most mature in the space.
Anthropic’s Claude models lean into strong reasoning and handling longer context well. The lineup runs from the fast and affordable Haiku models up through Sonnet and Opus for heavier lifting. Claude has built a solid reputation for careful, well thought out output on complicated tasks.
Google’s Gemini models plug directly into Google’s broader ecosystem and often lead the pack on sheer context window size, with some versions handling enormous inputs. Like the others, Gemini’s lineup ranges from fast and cheap up to top tier reasoning.
Context window matters just as much as price
A cheaper price per token doesn’t automatically mean a cheaper bill. If a model has a smaller context window, you might end up making more calls or truncating content just to fit, which adds up fast. Sometimes a provider with a much larger context window actually works out cheaper overall for document heavy work, even at a higher per token rate, simply because you need fewer round trips to get the job done. For more on how context windows actually work under the hood, see How AI Models Actually Read Your Text.
The input and output split isn’t the same everywhere
All three charge less for input than output, but by how much varies. Some price output at roughly double the input rate. Others charge significantly more. If you’re building something that generates long responses, like content writing or code generation, this ratio ends up mattering more than the headline input price most people compare first. Our pricing guide walks through this split in more detail.
Matching the provider to what you’re actually doing
- High volume, simple tasks like classification or tagging: the lightweight tier from any of the three tends to land in a similar price range, so it’s worth checking your actual numbers rather than assuming.
- Long documents or big codebases: a bigger context window can mean fewer total calls, which sometimes offsets a higher per token price.
- Complex reasoning or careful writing: the flagship tiers across all three are priced fairly close together for comparable quality, so output quality becomes the real deciding factor over cost.
Once you’ve picked a direction, 10 Ways to Actually Cut Your AI API Costs has practical steps for keeping whichever provider you choose as affordable as possible.
Skip the guessing
Marketing pages rarely make it easy to compare real costs side by side. The most reliable approach is to take an actual sample of your own prompts and run the numbers across providers directly.
Paste your text into our free tool and see the token count and estimated cost across GPT, Claude, Gemini, and 40 plus other models in one place, so you’re comparing apples to apples before picking a provider. Curious who’s behind this project? Read more about us here.
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