Google Gemini vs ChatGPT

Gemini vs ChatGPT usually means one of two things. As consumer assistants, both are polished apps and the choice is mostly about which ecosystem you already live in. As APIs, the comparison is between Google's Gemini model family and OpenAI's GPT-5 family, the models that actually power ChatGPT, and this page covers both.

In order: what the apps cost, then the model families tier by tier with live pricing and context windows, an honest reading of where each lineup earns its place, and head-to-head pages with full specs, privacy posture and benchmarks.

Confusingly, Google uses one name for both: Gemini is the assistant app and also the family of AI models that powers it. ChatGPT is OpenAI's app, powered by the GPT-5 model family. The app is the car, the model is the engine, and both engines can be used directly by any software through an API.

Unlike the apps, the models are metered per million tokens (a token is a short chunk of text, roughly three quarters of a word). That is what the board below compares, which is the choice you actually make when you build.

Gemini covers both ends. Gemini 3.1 Pro is Google's most advanced reasoning model, scoring 77% on ARC-AGI-2 abstract reasoning and 81% on SWE-Bench Verified real-world coding, and every current tier, including the budget Flash Lite, accepts images, audio, video and PDFs alongside text with a 1 million token context window, the natural fit for pipelines that chew through recordings, documents and screenshots.

The Flash line is where Google moves fastest: Gemini 3.7 Flash landed in August 2026 as a large step up in coding and long-running automation over 3.6 Flash, keeping the workhorse tier's speed and price while closing on far bigger models.

OpenAI's lineup goes deeper on agents. GPT-5.6 Sol leads on long-horizon agentic evaluations covering terminal work, web browsing and computer use, the kind of session that runs for hours and has to recover from its own mistakes. GPT-5.3 Codex holds state-of-the-art results on SWE-Bench Pro and Terminal-Bench, and the Terra and Luna tiers give the family clean price steps below the frontier.

OpenAI also maintains an open-weight option in GPT-OSS 120B, released under Apache 2.0, which Gemini has no equivalent for.

The board above shows the providers' own per-million-token prices live, so we won't repeat numbers here that would drift stale. Two durable patterns are worth knowing. First, Google prices its Flash tiers aggressively, and for high-volume multimodal work Gemini is often the cheapest way to hit a given quality bar. Second, list price per token is not cost per task: a model that solves the task in fewer output tokens, or without spending thinking tokens it bills as output, can be cheaper at a higher list price.

Send Gemini the workloads that look like media and volume: video and audio understanding, PDF-heavy document pipelines, high-throughput agents where latency compounds. Send the GPT lineup the workloads that look like open-ended agency: long research sessions, computer use, and agentic coding, where Codex's benchmark lead is hard to argue with. Many teams run both, with a gateway routing per task and falling back across families when a provider has a bad day.

Check the LLM leaderboard for how the current versions rank on independent benchmarks, and use the head-to-head pages below for full spec and privacy comparisons.