Four names dominate the AI conversation. GPT from OpenAI. Claude from Anthropic. Gemini from Google. Llama from Meta. You have probably used at least one, wondered about the others, and asked yourself whether you picked right.

There is no correct answer for everyone.

That’s the frustrating reality. Each model family was built by different companies with different philosophies, trained on different data, and optimized for different outcomes. Asking which one is “best” is like asking whether a hammer is better than a screwdriver.

This guide breaks down what each model family actually does well, includes real quotes from developers who use them daily, and helps you figure out which tool fits your actual work.

The Quick Version

If you want the short answer before the long explanation:

GPT is the default. Most people start here because ChatGPT is what they have heard of. It does most things reasonably well and has the largest ecosystem of plugins, integrations, and documentation.

Claude is for nuance. It handles complex instructions better, writes with more natural variation, and tends to understand what you meant rather than what you literally said. Developers increasingly prefer it for coding.

Gemini is for scale. Google built it to process massive amounts of information at once. If you need to analyze a 500-page document or an entire codebase, Gemini’s context window gives you room that others cannot match.

Llama is for control. Meta open-sourced it, which means you can run it on your own hardware, fine-tune it on your own data, and never send a single byte to someone else’s server.

Now for the details.

GPT: The Name Everyone Knows

OpenAI built ChatGPT, and ChatGPT became the word people use for AI assistants the way Kleenex became the word for tissues. That brand dominance matters. When someone says they “asked the AI,” they usually mean they asked GPT.

The current flagship is GPT-5 with various updates continuing to roll out. The model is fast. Response times are snappy. The interface is polished. OpenAI has invested heavily in making the product feel good to use.

GPT handles breadth well. Need a recipe? It works. Need to debug code? It works. Need to summarize a document? It works. Need creative writing? It works. The model rarely fails catastrophically at common tasks because OpenAI has been tuning it against the widest possible range of use cases.

Here is where GPT shows weakness: it tends toward certain patterns.

You have probably noticed GPT loves bullet points. It favors particular sentence structures. Its vocabulary leans toward certain buzzwords. After extended use, you start recognizing the “GPT voice” in content, that slightly over-eager, definitional tone that explains things one more time than necessary.

For quick tasks and general queries, GPT remains hard to beat on convenience alone. The mobile app works well. The voice mode is useful. The plugin ecosystem is extensive. If you want an AI assistant that fits smoothly into everyday life without friction, GPT delivers.

But smoothness is not the same as depth.

Claude: The Detail Obsessive

Anthropic built Claude with a different priority. They wanted a model that follows instructions precisely, handles nuance in long conversations, and produces output that sounds less robotic.

The current models run across three tiers: Haiku (fast and cheap), Sonnet (balanced), and Opus (maximum capability). Most people use Sonnet for regular work and Opus when a task genuinely requires heavy reasoning.

Claude’s reputation for coding has grown significantly. A Hacker News user named thomasahle put it directly:

“My personal experience is that 80% of the time Opus is better than GPT-4 on coding.”

That tracks with what many developers report. Claude handles larger codebases more coherently. It remembers context across long conversations better. When you paste in 3,000 lines of code and ask for modifications, Claude is more likely to maintain consistency throughout its response.

Another user, mrbishalsaha, made a similar observation:

“I use claude sonnet for coding and it’s better than GPT4 most of the time.”

The difference shows up in how Claude interprets ambiguous requests. If you write a slightly unclear prompt, Claude tends to infer your intent rather than treating your words as a literal specification to be parsed.

Claude also writes differently.

The prose comes out less formulaic. Fewer buzzwords. More variation in sentence length. If you need content that does not immediately read as AI-generated, Claude gives you a better starting point. You still need to edit, but you start from a stronger baseline.

The downsides exist too. Claude can be slower, especially Opus. Response times sometimes lag behind GPT noticeably. And Claude has stronger content guardrails, which can be frustrating if you need to work with edgy material for legitimate reasons.

One Hacker News user named suby noted a specific weakness: “Claude is more likely to suggest things which simply won’t compile…Claude 3’s knowledge of C++ is lacking.”

No model is perfect at everything.

Gemini: The Context Monster

Google’s Gemini takes a different approach entirely. Where GPT and Claude compete on reasoning quality and writing style, Gemini competes on scale. The model can process enormous amounts of information at once.

Current Gemini models support context windows up to one million tokens. That is roughly 700,000 words. You can paste in an entire novel. You can upload hours of video. You can include a complete codebase. The model will hold all of it in working memory and answer questions about any part.

This is not a gimmick.

If you work with large documents, long transcripts, or comprehensive analysis tasks, Gemini’s context window changes what is possible. RAG systems, retrieval augmented generation, work around context limitations by only feeding relevant chunks to the model, but Gemini lets you skip that complexity for many use cases and just load everything directly.

A developer on Hacker News, samyok, compared Gemini Flash favorably to more expensive models:

“It’s so fast and it has such a vast world knowledge that it’s more performant than Claude Opus 4.5 or GPT 5.2…a fraction (basically order of magnitude less!!) of the inference time.”

Speed matters for workflows. When you can get good results in two seconds instead of ten, you iterate faster. Your productivity compounds.

Gemini also integrates tightly with Google’s ecosystem. If you use Google Workspace, Drive, Docs, and Sheets, Gemini can access your files directly. That integration saves time versus manually copying content into chat windows.

The catch: Gemini’s marketing claims and practical reality sometimes diverge. A well-documented frustration is that the web interface does not expose the full context window to casual users. You might hear “one million tokens” but find yourself limited to a much smaller window unless you use AI Studio or the API directly.

Google’s AI safety approach also differs from Anthropic and OpenAI. Gemini refuses certain requests that other models handle without issue. The boundaries are sometimes unpredictable. You might hit a content block on something seemingly innocuous, then succeed with a minor rephrasing.

For pure analysis of large information sets, though, nothing else currently matches what Gemini offers.

Llama: The One You Own

Meta’s Llama models break from the others fundamentally. They are open.

You can download the model weights. You can run Llama on your own hardware. You can fine-tune it on proprietary data. You can deploy it in air-gapped environments where no information leaves your network. You pay nothing to Meta for any of this.

For organizations with strict data governance, this matters enormously.

Healthcare companies cannot send patient information to OpenAI’s servers. Law firms cannot upload confidential documents to Google’s cloud. Financial institutions have regulatory obligations about data residency. Llama lets all of them use modern AI capabilities without those compliance headaches.

The self-hosting trade-off is real, though. Running Llama well requires serious hardware. The 70-billion parameter model needs multiple high-end GPUs. The smaller versions work on consumer hardware but produce noticeably worse output. You are trading API convenience for infrastructure management.

For small businesses and individuals, the economics usually favor API access to Claude or GPT. You would spend more on electricity and GPU depreciation than you would on API calls unless your usage volume is extremely high.

But for enterprises processing millions of requests, the math flips. Self-hosted Llama can cost a fraction of API pricing at scale. And you get full control over uptime, latency, and availability. No rate limits. No service outages because the provider is overloaded.

Llama also enables customization that API-based models cannot match. You can fine-tune on your company’s writing style, your industry’s terminology, your specific use cases. The resulting model speaks your language because you trained it to.

Open source attracts a community. Developers constantly improve Llama’s capabilities, create specialized versions, build tooling around it. The ecosystem evolves fast.

If control matters more than convenience, Llama deserves serious consideration.

How They Compare on Specific Tasks

The honest answer is that model rankings shift based on what you measure.

Coding: Claude currently leads most developer preference surveys. The reasoning is more coherent across complex codebases. But GPT remains strong for quick snippets and debugging, and Gemini’s large context helps when you need to work with entire projects at once.

Writing: Claude produces more varied prose. GPT is faster for bulk content. Gemini handles research-intensive writing well because you can load all your source material. Llama can be fine-tuned to match your exact voice.

Analysis: Gemini wins for sheer volume. Analyzing a 200-page report takes one query. Claude and GPT require chunking strategies that add complexity.

Conversation: Claude maintains context better across long chats. GPT’s memory features help but still lose track more often. Gemini and Llama vary by configuration.

Cost: Llama is free at the model level (hardware costs aside). Gemini Flash offers strong value in the API pricing tiers. GPT and Claude Premium tiers cost more but deliver incrementally better quality.

Practical Workflow: Using Multiple Models

The smartest users do not pick one model exclusively.

A Hacker News commenter named MrSkelter described the approach:

“Claude Opus is generally better for me but takes a long time to reply…most power comes from bouncing between them.”

Different models for different tasks. Claude for initial drafting. GPT for speed runs and ideation. Gemini for research synthesis. Llama for anything sensitive that cannot leave your systems.

This is not inefficient duplication. It is using the right tool for each job.

Build workflows that route tasks to appropriate models automatically. Simple classification at the start of a request can save cost and improve quality. Coding tasks go to Claude. Quick questions go to GPT Mini. Long-document analysis goes to Gemini.

The tools that make this easy are improving constantly. DatBot lets you switch between models mid-conversation. Other platforms offer similar capabilities. The future is polyglot, not monolithic.

Making Your Decision

Here is a framework for deciding:

Start with GPT if you want the easiest onboarding, need broad general capability, and value ecosystem integration with other tools and plugins.

Switch to Claude if you do significant coding work, need better instruction-following for complex tasks, or produce content where natural writing quality matters.

Use Gemini when you work with large documents, need to analyze extensive information quickly, or are already embedded in Google’s ecosystem.

Choose Llama if data privacy is non-negotiable, you need customization that API providers cannot offer, or your usage volume makes self-hosting economically sensible.

Most people will use two or more of these. That is fine. The models complement each other more than they compete.

The important thing is knowing what each one does well, so you can match tools to tasks rather than forcing one tool to do everything.

What Changes Next

This comparison will need updating soon.

Model capabilities evolve monthly. Pricing shifts quarterly. New competitors emerge yearly. Today’s rankings will not hold forever.

OpenAI is working on reasoning improvements that could close Claude’s coding gap. Anthropic keeps extending context windows to compete with Gemini. Google is improving Gemini’s reliability and tuning its safety filters. Meta continues expanding Llama’s capabilities and ecosystem.

The direction matters more than the current snapshot.

What will not change: different philosophies produce different strengths. OpenAI optimizes for broad consumer appeal. Anthropic optimizes for precision and safety. Google optimizes for scale and integration. Meta optimizes for openness and customization.

Those priorities shape everything downstream.

Pick based on what matters to you. Experiment with alternatives. Stay flexible as the landscape shifts.

The best AI model is whichever one helps you finish your actual work.