GPT vs Claude vs Gemini: An Honest Comparison

Model comparisons age badly, so this one focuses on durable differences in character and design rather than benchmark scores that will be stale in a month. For current specifications, our model matrix is the live reference.

The short answer

Claude for long documents, careful instruction-following, and writing that needs to sound human. GPT for the broadest ecosystem, tooling and general reliability. Gemini for very long context and multimodal work, especially video and audio.

If you only use one, any of the three is fine for most tasks. The differences matter at the edges — and the edges are where most professional work happens.

Writing

Claude tends to produce prose that needs less editing. It is less prone to the structural tics that make AI writing recognisable: fewer gratuitous headings, less compulsive listing, fewer sentences that begin "In today's rapidly evolving landscape." It also follows long, nuanced style instructions more faithfully.

GPT is more likely to reach for structure — headings, bullets, bold — whether the content calls for it or not. This is genuinely useful for documentation and instructional content, and mildly annoying for essays.

Gemini sits between the two, with a tendency toward comprehensiveness that sometimes reads as padding.

For anything published under your own name, Claude usually requires the least rewriting. That is a real difference in hours.

Coding

All three are strong. The distinctions are practical rather than raw capability.

GPT has the deepest ecosystem: more editor integrations, more examples in training data for obscure libraries, more third-party tooling built around it. If you are working in a niche framework, GPT is more likely to have seen it.

Claude handles large-context code work particularly well — reading an entire module and reasoning about it coherently, rather than losing track partway through. It also tends to be more forthcoming about uncertainty, which matters when it would otherwise invent a plausible API signature.

Gemini's very large context window means you can hand it an entire small codebase at once.

Reasoning

For genuinely hard analytical problems — multi-step maths, logic with dead ends, complex trade-off analysis — the dedicated reasoning modes across all three providers outperform their standard models substantially, and the gap between providers is smaller than the gap between reasoning and non-reasoning modes.

The practical guidance is routing, not brand loyalty: use a reasoning model for hard analytical work regardless of provider, and a standard model for writing, summarisation and conversation, where extended deliberation adds cost and latency without improving the result.

Long documents

This is where differences are clearest.

Gemini's context window is the largest by a wide margin, which makes it the default for genuinely enormous inputs — hours of video, very large document sets.

Claude handles long inputs with notably consistent attention. It is less prone to the "lost in the middle" effect where material buried in the centre of a long context gets under-weighted.

GPT is capable here but generally the least generous on window size among the three.

An important caveat that applies to all of them: a large window does not guarantee effective use of it. Retrieving the relevant 5% of a document reliably beats pasting all of it and hoping.

Multimodal

Gemini was designed multimodal from the outset and leads on video and audio. GPT is strong on images and has broad tooling. Claude handles images and documents well, with less emphasis on video.

For all three, reading text in images is reliable; precise spatial reasoning — exact positions, counting many similar objects, cluttered charts — remains noticeably weaker than text reasoning. Verify anything you extract from a complex chart.

Cost

Pricing changes too often to quote, but the structural pattern is stable: each provider offers a small, cheap tier and a large, expensive one, and the small tiers are dramatically cheaper — often 10-20x — while being entirely adequate for classification, extraction, summarisation and routine generation.

The largest available saving is almost never switching provider. It is routing simple tasks to small models instead of sending everything to a flagship. Our cost calculator lets you compare your actual token volumes across all of them with editable current rates.

Where each falls short

Claude: smaller third-party ecosystem than GPT. Occasionally over-cautious on legitimate requests.

GPT: structural verbosity in writing. Formatting habits that need explicit suppression.

Gemini: the least consistent of the three in following precise formatting instructions, in our experience. Comprehensiveness can read as padding.

All three: hallucinate specifics — numbers, citations, API signatures — with complete confidence. None of them reliably knows what it does not know. Verify anything consequential.

A practical recommendation

If you are choosing one, pick based on your dominant task: writing → Claude, building software with lots of integrations → GPT, huge documents or video → Gemini.

If you can use two, pair a flagship for hard work with a cheap small model for bulk work. That combination saves more money than any provider choice.

Our model index covers every major model including open-source options, and the model matrix compares specifications side by side.