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title: ChatGPT vs Gemini vs Claude: How They Differ | daily.dev
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# ChatGPT vs Gemini vs Claude: How They Differ

**[ByteByteGo](https://daily.dev/sources/bytebytego)** · 13 min read · 2 upvotes · 0 comments

## Summary

A structured comparison of ChatGPT, Gemini, and Claude across five architectural dimensions that explain their distinct user-visible behaviors. Google's Gemini uses Mixture of Experts and native multimodality from inception, enabling broad domain coverage and direct video ingestion. OpenAI evolved from bolted-on vision to a unified architecture and uses a real-time router to select between fast and reasoning sub-models in GPT-5.5. Anthropic maintains a text-first approach with Constitutional AI for alignment, producing Claude's tendency to push back on edge cases. All three have converged on explicit reasoning tokens at inference time despite diverging on every prior architectural choice, including model density, multimodality strategy, context window size, and alignment methodology.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.bytebytego.com/p/chatgpt-vs-gemini-vs-claude-how-they>

## Questions this post answers

### What is the difference between Mixture of Experts and dense architecture in large language models?

In a dense neural network, every parameter activates for every token, so doubling parameters roughly doubles compute cost per query. Mixture of Experts (MoE) instead routes each token to only two or three smaller expert sub-networks out of many, allowing enormous total parameter counts while activating only a fraction per token, at the cost of increased variance and load-balancing challenges during training.

_daily.dev surfaces architecture explainers like this for engineers weighing which model fits their workload._

### How does Claude's Constitutional AI alignment method differ from OpenAI's RLHF approach?

Constitutional AI uses an explicit written constitution against which the model critiques and revises its own outputs, then applies AI-generated feedback to refine itself, rather than relying purely on human raters. OpenAI's RLHF instead trains a reward model from human comparisons of output pairs and fine-tunes the language model toward preferred outputs; Anthropic published a 23,000-word constitution in January 2026 describing this process.

_Developers picking an AI assistant for sensitive tasks can track alignment approach differences on daily.dev._

### How large is the context window in Gemini 3 Pro compared to GPT-4 Turbo and Claude Opus 4.8?

Gemini 3 Pro and 3.1 Pro offer 1 million token context windows in production, matching Claude Opus 4.8 and Sonnet 5 which also provide 1 million tokens. GPT-4 Turbo takes a more conservative approach at 128K tokens, with OpenAI instead investing efficiency gains into its router architecture rather than raw context size.

_daily.dev helps developers comparing context window limits before loading large codebases into a model._

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- [I put Claude, ChatGPT, and Gemini through the same tasks — here's which one I'm keeping](https://daily.dev/posts/i-put-claude-chatgpt-and-gemini-through-the-same-tasks-here-s-which-one-i-m-keeping-x72ervgkc) · XDA Developers · 1 upvotes · 0 comments
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---

Tags: [#llm](https://daily.dev/tags/llm), [#mixture-of-experts](https://daily.dev/tags/mixture-of-experts)

[View this post on daily.dev](https://daily.dev/posts/chatgpt-vs-gemini-vs-claude-how-they-differ-du3af09ts)

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