Claude vs ChatGPT

ChatGPT is OpenAI's consumer app, and Claude is Anthropic's family of models, which also ships as an app of the same name, often written Claude AI. If you just want an assistant to chat with, either app will serve you well, and if you are building software, you never integrate the app at all, you call the models underneath: the GPT-6 family from OpenAI and the Claude family from Anthropic.

So whichever way round you ask it, ChatGPT vs Claude is really two comparisons in one, and this page runs through both in order: what the apps cost, then the model families compared tier by tier with live official pricing, a practical verdict, and head-to-head pages for the pairings that matter.

The intelligence inside each app is an AI model: ChatGPT runs on OpenAI's GPT models, GPT-6 on the paid plans, and the Claude app runs on Anthropic's Claude family. The app is the car, the model is the engine, and the same engines can be plugged into any software through an API.

Models are also billed differently: not a flat subscription but metered like electricity, per million tokens (a token is a short chunk of text, roughly three quarters of a word). That is what the board below compares, tier by tier, because it is the choice you actually make when you build something of your own.

Claude's center of gravity is agentic coding and long-running work. Claude Opus 5.5 is Anthropic's leading model, performing at the level of the pricier Claude Fable 5.1 on most work, and in Anthropic's launch comparison it leads GPT-6 Astra on Terminal-Bench 4.0. Claude Sonnet 5.5 is the faster, cheaper complement at half the price, within two points of Opus on Anthropic's GDPval-AA knowledge-work evaluation, and both carry 1 million token context windows with up to 128K output tokens, enough context to hold a large codebase or a day-long agent session in view. Claude Haiku 4.5 rounds out the family for high-volume work, matching the coding quality of a previous-generation Sonnet at roughly a third of the cost and more than twice the speed.

In practice, teams reach for Claude when the work looks like software engineering: multi-file refactors, terminal-driven agents, code review, and tasks where the model needs to keep checking its own output over hours rather than seconds.

OpenAI's strength is the breadth of the lineup and the depth of its agent tooling. GPT-6 Astra is OpenAI's most capable model, built for computer use, browsing, software engineering and science, and the one OpenAI recommends for the most difficult research tasks. Below it, GPT-6.1 Sol nearly matches Astra on agentic coding, computer use and professional work at a fifth of its token price, and GPT-6 Luna handles classification, summarization and bulk pipelines at speed, with the 1M token context intact on every tier.

OpenAI also publishes GPT-OSS 120B under an Apache 2.0 license, so if open weights matter to you, the GPT side has an answer that Claude currently doesn't.

The board above shows the providers' own per-million-token prices live, and they move as providers reprice, so we won't repeat numbers here that would drift stale. The shape of the pricing is durable though: both makers step their tiers apart by multiples, not percentages, so the expensive question is rarely Claude or GPT, it is which tier of either family a task genuinely needs. Routing a workload from a flagship down to Sonnet, Sol or Haiku when the task allows it saves far more than switching brands at the same tier.

One subtlety worth knowing: reasoning models bill their thinking as output tokens, so a token-efficient model can undercut a nominally cheaper one on real workloads. Comparing on your own traffic beats comparing list prices.

A reasonable starting point: pick Claude Sonnet 5.5 when the work is agentic coding and you want the strongest quality-per-dollar in that lane, pick GPT-6.1 Sol when agents need to browse, use computers and sustain very long research sessions, and drop to Haiku 4.5 or GPT-6 Luna for the high-volume simple steps either way. Escalate to Opus 5.5 or GPT-6 Astra for the work you would otherwise hand a senior engineer.

Then verify against your own tasks. The LLM leaderboard tracks how these models rank on independent benchmarks, and every model page on this site shows live pricing, provider routes and privacy posture, so the comparison stays honest as the catalogue moves.