A multi-AI chat that sends one prompt to GPT, Claude, and Gemini at the same time. You see all three answers side by side and pick the one that fits — without juggling tabs or copy-pasting.

Why not just use ChatGPT, Claude, or Gemini directly?

Because no single AI is best at everything. If you already check answers across multiple models for important tasks, Polymind removes the friction — same workflow, but in one chat instead of three tabs.

Is Polymind really free?

Yes, completely free during open beta. New accounts get 200 credits (1 credit roughly equals 1 prompt to all 3 models). For more credits during beta, email leejahun0@gmail.com.

Will Polymind charge later?

Yes — paid plans launch when beta ends. Pricing will be pay-as-you-go credit packs, not subscription. Beta users keep their feedback-shaping influence on the v1 launch.

Where does my data go?

Prompts and responses are stored in your account so you can revisit them. They are never used to train models — Polymind routes through Vercel AI Gateway, which has zero data retention by default. You can delete any conversation anytime.

Can I switch which AI models Polymind uses?

Yes — each of the three columns can be set to any chat-capable model on Vercel AI Gateway (100+ models). The default is the latest frontier GPT, Claude, and Gemini. Click any column header to swap.

Is Claude better than GPT for coding?

It depends on the kind of coding. For writing new code from scratch, GPT is slightly better — it produces more idiomatic boilerplate. For reviewing or refactoring existing code, Claude is meaningfully better — it catches subtle bugs and respects "don't change behavior" constraints more reliably. Most engineering teams end up using both: GPT for greenfield, Claude for review.

Which is cheaper, GPT or Claude?

Pricing changes constantly and is roughly comparable in 2026. The more useful question is cost per finished task — Claude often needs fewer retries on format-sensitive prompts, which makes it cheaper in practice for structured output workflows. For one-shot generation, the two are within ~10% on most token budgets.

Can I use both GPT and Claude side by side?

Yes. That's exactly what Polymind is for — send one prompt, get answers from both at the same time, compare side by side. It's more useful than picking a single winner because the two models disagree productively, and the disagreement itself is information about how confident the answer is.

Which has the better context window?

Both support 200K+ tokens in 2026. The more useful question is how well they use that context — and Claude shows less degradation when you push past 100K tokens. If you're routinely processing long documents, Claude is the safer choice.

Which is less restrictive for edge cases?

Both refuse genuinely harmful requests. Claude tends to add legal/medical disclaimers more often; GPT engages and adds caveats inline. For legitimate work the differences are small — neither is meaningfully 'less restricted' than the other.

← All comparisons

Last updated May 8, 2026

GPT vs Claude in 2026: a hands-on, task-by-task comparison

In 2026, GPT and Claude are the two models knowledge workers reach for first — and the head-to-head that decides the most workflows. They've converged on similar raw capability but diverged sharply on tone, instruction discipline, and how they reason under constraints. The right answer is rarely "one wins" — it's "one wins at *what?*"

This page is a real comparison across the work people actually do: writing, code review, math, multimodal, tool use, and long-context analysis. Run the same prompts side by side in Polymind and you'll feel the difference in five minutes.

GPT-5.5

by OpenAI

Broadest ecosystem and most mature tool-use; best at structured reasoning under tool calls.

Context window: 256K tokens
Multimodal: Full (image, audio, video)

Claude Opus 4.7

by Anthropic

Best long-form writing, code review judgment, and instruction-following discipline.

Context window: 200K tokens
Multimodal: Partial (image only)

Task-by-task: which model wins, and why

Task	Winner	Why
Code generation from a blank slate	GPT	GPT produces more idiomatic boilerplate when starting from zero, especially for languages and frameworks with heavy ecosystem documentation. It feels closer to a senior engineer dictating familiar patterns. Claude can match it on simpler asks but tends to over-explain its own choices.
Code review on an existing diff	Claude	Given a 50-line diff and asked 'what's wrong, what's risky,' Claude finds genuine issues more often and invents fewer cosmetic nitpicks. Its calibration on subtle bugs — race conditions, off-by-one, error handling gaps — is meaningfully better.
Long-form writing (essays, blog posts, release notes)	Claude	Claude's prose is less hedged, more confident, and easier to read out loud. GPT writes competently but trends toward the safe middle — lots of "however" and "it's important to note." For anything you'll publish, Claude usually needs less editing.
Customer-facing copy with a tone constraint	Claude	Tell either model "draft a four-sentence email, direct but warm, don't apologize" — Claude obeys the constraint roughly 9 out of 10 times. GPT slips an apology back in maybe 1 in 3 attempts. This single test predicts a lot of writing-job outcomes.
Math and logical reasoning	Tie	On standardized math benchmarks the two are within margin of error in 2026. In real workflows, GPT shows assumptions more reliably; Claude is slightly better at recognizing when a problem is underspecified. Pick whichever you're already paying for.
Image understanding (vision)	GPT	GPT's multimodal pipeline is more mature — it handles charts, diagrams, screenshots, and handwritten notes more reliably. Claude handles images well but lags on edge cases (small text in screenshots, dense diagrams, multi-image reasoning).
Function calling and structured tool use	GPT	For agent workflows where the model chooses tools, fills structured arguments, and chains calls, GPT's tooling is more battle-tested. Function definitions resolve more reliably and the model is less prone to hallucinating tool names. Claude has caught up but is still the second-best choice.
Long-context analysis (100K+ tokens)	Claude	Both support 200K+ context windows in 2026, but Claude shows less degradation when you push past 100K — it cites earlier passages more accurately and is less likely to forget mid-document instructions. For document QA over large PDFs or codebases, Claude is the safer bet.
Brainstorming and divergent thinking	Tie	GPT generates more variety per prompt; Claude produces fewer but more thoughtful options. If you need 20 ideas, ask GPT. If you need 5 you'd actually use, ask Claude.
Following exact format constraints (JSON, schemas)	Claude	When a prompt says "respond only with valid JSON, no surrounding prose," Claude obeys more reliably. GPT is improving but still occasionally adds explanatory text ahead of the structured output. For programmatic pipelines, Claude wastes fewer retries.

Pick GPT when…

You're building autonomous agents that call tools and APIs in sequence.
Your work is multimodal — vision, audio, or OCR-style reading from images.
You need the widest plugin and integration ecosystem (browser, code interpreter, custom GPTs).
Your stack already runs on OpenAI and switching costs are non-trivial.

Pick Claude when…

Most of your output is writing — copy, content, documentation, customer comms.
You routinely review or refactor existing code rather than greenfield.
You need disciplined instruction-following (output format, exclusion rules, tone constraints).
You process long documents and rely on accurate mid-document references.

Run GPT and Claude side by side

Stop guessing which model wins for your work. Send one prompt to GPT, Claude, and Gemini at the same time and compare answers in five minutes. Free during open beta.

Get started

Frequently asked questions

Is Claude better than GPT for coding?
It depends on the kind of coding. For writing new code from scratch, GPT is slightly better — it produces more idiomatic boilerplate. For reviewing or refactoring existing code, Claude is meaningfully better — it catches subtle bugs and respects "don't change behavior" constraints more reliably. Most engineering teams end up using both: GPT for greenfield, Claude for review.
Which is cheaper, GPT or Claude?
Pricing changes constantly and is roughly comparable in 2026. The more useful question is cost per finished task — Claude often needs fewer retries on format-sensitive prompts, which makes it cheaper in practice for structured output workflows. For one-shot generation, the two are within ~10% on most token budgets.
Can I use both GPT and Claude side by side?
Yes. That's exactly what Polymind is for — send one prompt, get answers from both at the same time, compare side by side. It's more useful than picking a single winner because the two models disagree productively, and the disagreement itself is information about how confident the answer is.
Which has the better context window?
Both support 200K+ tokens in 2026. The more useful question is how well they use that context — and Claude shows less degradation when you push past 100K tokens. If you're routinely processing long documents, Claude is the safer choice.
Which is less restrictive for edge cases?
Both refuse genuinely harmful requests. Claude tends to add legal/medical disclaimers more often; GPT engages and adds caveats inline. For legitimate work the differences are small — neither is meaningfully 'less restricted' than the other.