ChatGPT vs Claude vs Gemini

All three in 30 seconds

ChatGPT (OpenAI) is the best-known all-round assistant. The GPT-5.6 family is strong at conversation, creation, analysis and tool use, with a rich ecosystem (GPTs, Assistants, integrations). A safe default for almost any use.

Claude (Anthropic) stands out at writing, long-form reasoning and code. The Claude Opus 5 / Sonnet 5 / Haiku 4.5 family is loved by developers and by anyone working with long documents and long-running agent tasks, with a strong emphasis on safety and accuracy.

Gemini (Google) is strong at multimodality (text, image, audio, video) and at integration with Google Workspace. The Gemini 3 Pro / 3.7 Flash family excels at very long context and at connecting to Search and Google products.

Comparison table

Ratings are qualitative and reflect relative strengths as of August 2026 — not benchmark scores. The gap between the leading models is small; our advice: test on a real task of your own.

A closer look at each model

ChatGPT (OpenAI)

The versatile choice. Excellent for everyday conversation, brainstorming, content creation, analysis and tool-based automation. The most mature ecosystem — custom GPTs, the Assistants API, and countless ready-made integrations. If you need one tool that does everything well, this is a safe starting point. Read the full ChatGPT guide ›

Claude (Anthropic)

The developers' and writers' winner. Especially strong at writing code, refactoring, and long agentic tasks that require planning and a sequence of actions. Excellent for working with long documents and for precise, human phrasing. Supports MCP for connecting external tools. Read the full Claude guide ›

Gemini (Google)

The king of multimodal and huge context. It processes text, images, audio and video well, with one of the largest context windows on the market — great for analyzing long documents and media. It integrates deeply with Gmail, Docs and Search. A natural choice for anyone living inside Google Workspace. Read the full Gemini guide ›

Pricing — how to think about it

All three providers offer a free/cheap tier for chat and a premium subscription (around ~$20/month) for access to the strongest models. If you're building a product or automation via the API, pricing is measured per million tokens (input/output) and varies by tier:

• Premium tier (GPT-5.6, Claude Opus 5, Gemini 3 Pro) — for complex tasks that need maximum quality.
• Fast, cheap tier (GPT-5.6 mini, Claude Haiku 4.5, Gemini 3.7 Flash) — for high volume, classification, extraction and simple tasks.
• Cost tip: route tasks — a cheap model for most calls, premium only when you need it. See the LLM pricing guide.

When to choose each

Choose ChatGPT if…

• You want one versatile tool for every task
• A rich ecosystem of GPTs and ready-made integrations matters to you
• You're building a general chat assistant or tool-based automations

Choose Claude if…

• You're a developer and need the strongest code and agents
• You work with long documents or need precise, human phrasing
• Safety, accuracy and long agentic work matter to you

Choose Gemini if…

• You work a lot with image, video or audio (multimodal)
• You live inside Google Workspace (Gmail, Docs, Sheets)
• You need the largest context window to analyze huge material

Why a comparison like this goes out of date, and what does not

Any page that names specific model versions is a snapshot, and the three labs ship often enough that the ranking on any given capability can change in a month. It is worth being explicit about that rather than pretending otherwise: treat every version number and every "excellent" in the table above as a description of a moment, and check the provider's own model page before you commit to anything that depends on a particular version being current.

The useful thing is that most of what actually determines your choice moves far more slowly than the benchmark leaderboard does.

Ecosystem barely moves at all. Which model is wired into the tools you already use, whether it is approved by your employer, whether there is a mature SDK for your language — these are the same this quarter as last, and they decide more day-to-day outcomes than a few points of capability difference.

House style moves slowly too. Each of these models has a recognisable default manner — how verbose it is, how readily it hedges, how it formats an answer when you did not specify, how it behaves when it is unsure. That character does shift between versions, but gradually, and it is often the thing people are actually responding to when they say they prefer one.

The shape of pricing is stable even when the numbers are not. All three offer a consumer subscription and a usage-priced API, with a cheap fast tier and an expensive capable tier. Which is to say: the structural decisions you make — route cheap work to a small model, reserve the expensive one for hard cases — survive every price change, and the specific numbers are worth looking up rather than remembering.

And the gap at the top has been narrowing for a while. On ordinary work — drafting, summarising, everyday code, analysis — all three of these are good enough that the differences show up at the edges rather than in the middle. Which makes the right question not "which is best" but "which is best at the specific thing I do most", and that is a question only you can run the test for.

Running your own comparison

An afternoon of structured testing on your own material beats any amount of reading, and most people never do it because it sounds more elaborate than it is. It is not.

Collect ten prompts you have actually sent in the last month. Real ones, with the real messy context — not clean examples written for the test, which are the single most common way these comparisons go wrong. The prompts should skew toward what you do most: if eighty percent of your use is rewriting emails, eight of them should be emails.

Run all ten through each model with the same wording, and save the outputs. Do not tune the prompt for each one at this stage — you are comparing defaults, and adapting the prompt to each model's habits is a second round, worth doing later with whichever two you shortlist.

Then score them blind. Strip the labels, shuffle, and rate each output against three or four criteria that matter for the work — was it factually right, did it need editing, did it follow the format, was the tone usable. Blind matters more than it sounds: brand expectation is a strong effect, and people who skip this step reliably rediscover their existing preference.

Two things to watch for beyond the scores. How the models behave when the prompt is ambiguous — asking a clarifying question, picking a reasonable interpretation, or confidently answering a different question — is a big determinant of how it feels to work with one over months. And what happens when a request brushes against a limit: an unnecessary refusal on legitimate work is a real cost, and how often that happens is very sensitive to the kind of work you do, which is why other people's complaints about it may not predict yours at all.

Repeat the exercise in six months. It takes an hour, and it is the only way to know whether the reasons you chose still hold.

What actually breaks when you switch

Using more than one is normal, and moving between them is cheaper than it used to be — but a few things do not transfer cleanly, and knowing which ones saves an afternoon.

Prompts are portable in substance and not in detail. The instruction survives; the fiddly parts do not. A prompt that was tuned over weeks against one model — the exact phrasing that stopped it over-explaining, the ordering that made it follow the format — is tuned to that model's habits, and will produce slightly different behaviour elsewhere. Expect to retune, budget an hour, and do not conclude from the first result that the other model is worse.

Structured output and tool-calling are where the real work is. All three support constrained JSON and function calling, and all three differ in the details: what the schema may contain, how strictly it is enforced, how parallel tool calls are represented, how the result is fed back. An abstraction layer that lets you swap providers is genuinely useful here, but understand that it is hiding differences rather than removing them, and the differences resurface the first time something fails.

System-prompt handling differs enough to notice. How much weight the system prompt carries relative to the conversation, and how gracefully it degrades over a long exchange, is not the same across providers — which is why a carefully built assistant sometimes behaves oddly on a new model in ways that have nothing to do with capability.

Token counting and cost do not map across either. The same text is a different number of tokens for different tokenisers, so a cost estimate carried over from one provider to another is approximate at best. Measure on the provider you are actually using.

What does transfer, and is worth keeping provider-independent from the start: your eval set, your logging, and the documents themselves. If those three live outside whichever SDK you happen to be calling, switching is a day's work rather than a project.

Data handling is an account-level question

One thing that genuinely matters and is almost never covered in a capability comparison: what each provider does with what you send. The important point is that this is decided far more by which tier you are on than by which provider you picked.

Broadly, consumer chat products and business or API tiers are governed differently, and the defaults are frequently not the same. That is the single most common misunderstanding here — someone reads the API terms and assumes they apply to the free chat account where their colleagues are pasting customer data.

Rather than repeating policies that change, the questions to check on each provider's own terms page: whether your inputs may be used to improve their models by default, whether that differs by tier, how to turn it off and at what level, how long conversations are retained and whether that is configurable, whether a zero-retention arrangement exists for API use, and which regions the processing happens in. If an organisation is involved, add: what an administrator can enforce centrally, and whether there is an audit trail.

For anything regulated, get the current answers in writing from the provider rather than from a comparison page — including this one. The policies are the part of this landscape that changes most consequentially and is least visible from the outside.

Subscription or API — they answer different questions

A point of confusion worth clearing up, since the same model is sold both ways.

The monthly subscription buys you a finished product: an interface, file uploads, memory across conversations, web access, image handling, a mobile app. You are paying for the surroundings as much as for the model, and for a person doing their own work by hand it is almost always the right purchase. It is also effectively flat-rate within its limits, which makes it predictable.

The API buys you the model and nothing else. It makes sense when something automated is doing the calling — a script, a workflow, a product you are building — where an interface would be in the way. It is priced by usage, which means it is dramatically cheaper than a subscription for light automated use and unboundedly more expensive for heavy use, and the unbounded direction is the one that surprises people.

Most people who do both end up paying for one subscription for their own work and a small amount of API usage for the things they have automated. That is a reasonable default. What does not work is trying to replace a subscription with API access and a homemade interface to save twenty dollars — you will spend the difference in the first week and maintain it forever.

The cost of using all three

"Use two or three in parallel" is sound advice and it is not free, so it is worth naming what it costs before you settle into it.

The subscriptions are the smallest part. The real overhead is that your working context is now split across three products: the conversation where you worked something out is in one of them, the uploaded document is in another, and remembering which is a small tax you pay several times a day. Each has its own memory of you, and none of them knows what you did in the others.

There is also a quiet pull toward using whichever one is already open rather than whichever one is better for the task, which defeats the point of the arrangement entirely.

Two things keep it manageable. Decide the split once, by task type rather than by mood, and write it down somewhere you will see it — two lines is enough. And keep your reusable prompts in a plain file of your own rather than inside any one product, so that switching costs nothing and none of the work you have put into phrasing is trapped in a subscription you might cancel.

Language & practical notes

As of 2026, all three models support Hebrew at a high level — writing, understanding, summarizing and translating. The differences are subtle and task-dependent: for long, complex Hebrew text, Claude and Gemini tend to be the most accurate; for everyday chat and quick creation, ChatGPT is very convenient. Our recommendation: test on real content of your own (a client email, a post, some code) and compare the results — the gap between the tools is small enough that it comes down to personal preference and the ecosystem you already use.