Popular LLMs (Claude, GPT, Gemini, Llama) Compared
Popular LLMs are the widely used large language model families that most developer tools and applications are built on: Claude from Anthropic, GPT from OpenAI, Gemini from Google and Llama from Meta. They differ in who can access the weights, where they are typically deployed and which strengths their makers emphasise.
- Claude: It is Anthropic's closed-weight model family, offered in several tiers of size and speed.
- GPT: It is OpenAI's model family behind ChatGPT, with closed-weight flagship models.
- Gemini: It is Google's closed-weight family, designed to accept text, images, audio and video.
- Llama: It is Meta's open-weight family, which organisations can download and host themselves.
- Moving target: Versions, prices and limits change often, so current documentation is the reliable reference.
For example, a developer assistant that explains Python stack traces can call Claude, GPT or Gemini through a hosted API, or run Llama on the team's own GPU server.
Quick Answer
Claude, GPT and Gemini are closed-weight models accessed through their makers' APIs and major cloud platforms, and Llama is an open-weight model that can be self-hosted. The right choice depends on data control needs, existing cloud platform, required modalities and results on the team's own tasks, not on general reputation. The open versus closed trade-off itself is covered in open source vs closed source LLMs.
Claude vs GPT vs Gemini vs Llama: Comparison Table
| Aspect | Claude | GPT | Gemini | Llama |
|---|---|---|---|---|
| Developer | Anthropic | OpenAI | Meta | |
| Weights | Closed | Closed for flagship models | Closed; Gemma is a related open family | Open, under Meta's licence |
| Typical access | Anthropic API, Amazon Bedrock, Google Cloud Vertex AI | OpenAI API, Microsoft Azure | Gemini API, Google Cloud Vertex AI | Self-hosted or through cloud providers |
| Consumer product | Claude apps | ChatGPT | Gemini app | Meta AI |
| Strengths its maker emphasises | Coding, agentic tasks and long documents | General-purpose tasks and a broad developer ecosystem | Native multimodal input and Google product integration | Customisation, fine-tuning and on-premise use |
| Self-hosting | Not available | Not available for flagship models | Not available | Yes |
| Fine-tuning | Limited, through selected platforms | Offered through the API for some models | Offered through Google Cloud for some models | Full, on own hardware |
When to Use Claude
- Coding assistants: Code generation, code review and agentic coding workflows are a documented focus.
- Long inputs: Large codebases, specifications or logs are analysed in a single request, within the model's context window.
- Multi-cloud access: The team already uses AWS or Google Cloud and prefers to access the model there.
When to Use GPT
- Broad ecosystem: Many libraries, tutorials and third-party tools support the OpenAI API first.
- General assistants: One model family covers chat, drafting, analysis and tool calling in a single product.
- Azure environments: The organisation runs on Microsoft Azure and wants the model inside that platform.
When to Use Gemini
- Multimodal inputs: The application processes screenshots, diagrams, audio or video together with text.
- Google Cloud and Workspace: The team builds on Vertex AI or connects to Google productivity tools.
- Mixed media documentation: Answers must combine images from documentation with written explanations.
- Gemini vs Claude decisions: When both are available on Google Cloud, the deciding factor is usually whether the task depends more on images and video or on long code and text inputs.
When to Use Llama
- Data stays internal: Source code and logs must never leave the company network.
- Customisation: The team wants full fine-tuning on internal code or a fixed output style.
- Cost control at scale: Constant high volume runs on owned hardware instead of per-token billing.
Example: An LLM Comparison on One Stack Trace
A team adds a feature that explains a Python KeyError stack trace and suggests a fix, then evaluates each model on the same task.
- Claude, GPT and Gemini: The trace and the relevant source file are sent to each provider's API with the same instruction and settings.
- Llama: The same prompt is sent to a Llama model served on an internal GPU server, so the code never leaves the network.
- Evaluation set: The team collects around 50 real stack traces with known root causes from its own incident history.
- Criteria: Each answer is scored for the correct root cause, a working fix, invented function names and response latency.
- Decision: The model with the strongest results on this internal set is chosen, because public rankings rarely reflect one team's codebase.
How to Choose Among Popular LLMs
- Start with constraints: Data residency, cloud platform and budget usually eliminate some options before any testing.
- Test on real tasks: Compare models on the team's own code and prompts, and check outputs for LLM hallucination.
- Plan for change: Wrap model calls behind one interface, because versions are updated and retired regularly.
- Consider smaller models: Simple tasks such as classification often run well on small language models.
Quick Quiz
Pick an answer to check yourself. Nothing is saved.
Question 1 / 3
1. Which of the four model families publishes downloadable weights?
Frequently Asked Questions
How should a team choose an LLM for coding?
No single model is the most accurate for every codebase, and rankings change with each release. Testing the candidate models on the team's own code and tasks gives a more reliable answer than public leaderboards.
Llama vs GPT: which is more capable?
Capability depends on the specific model version and task. Llama's main advantage is that its weights can be downloaded, customised and hosted privately.
What is the difference between ChatGPT and GPT?
GPT is the family of language models, and ChatGPT is the chat product built on those models. The API gives developers direct access to the models without the chat interface.
Can one application use more than one LLM?
Yes, many applications route different requests to different models. A common design sends simple tasks to a small, fast model and complex tasks to a larger one.
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