AI Providers & Models

Choosing an LLM in 2025 feels a bit like choosing a laptop.

You can buy the most expensive one and call it a day… but you’ll overpay, underuse it, or discover it’s the wrong fit for your workflow.

This post gives you a simple decision framework for four big buckets:

• OpenAI (hosted, polished, broad model lineup)
• Anthropic Claude (strong writing + long-context options)
• Google Gemini (fast “workhorse” + very strong long-context)
• Meta Llama (open-weight models you can run yourself)

OpenAI: Best for general-purpose apps and tool-rich workflows

If you’re building products quickly (or rolling out AI to a company), OpenAI is often the default because the ecosystem is mature: chat, multimodal, embeddings, moderation, speech, and open-weight options. (platform.openai.com)

Use OpenAI when you want:

• Strong general performance across writing + coding + “do the thing” tasks
• Easy integration with tools (search, functions, structured outputs)
• A broad menu: models + embeddings + speech (Whisper/TTS) (platform.openai.com)

Great for: product features, internal copilots, automation, prototyping.

Anthropic (Claude): Great for long documents, careful tone, and “clean writing"

Claude is popular for teams that care about writing quality, structured reasoning, and long-context work (think: policies, contracts, support macros, or giant internal docs). Claude’s docs also highlight very large context support (including optional 1M-token context for some configurations). (platform.claude.com)

Use Claude when you want:

• Excellent drafting, rewriting, and summarization
• Strong performance on “read a ton → produce something coherent”
• Long-context pipelines (depending on model/settings) (platform.claude.com)

Great for: legal/compliance review assists, executive memos, knowledge-base synthesis, long doc Q&A.

Google Gemini: Awesome for speed + long-context + Google ecosystem

Gemini has become a go-to when you need a fast, cost-efficient model for high throughput—and when long-context is central. Google’s model docs highlight options like Gemini 2.0 Flash with a 1M-token context window and newer “thinking” models. (ai.google.dev)

Use Gemini when you want:

• High throughput and responsive UX (chatbots, assistants)
• Long-context document analysis (huge PDFs, many files)
• Tight integration with Google tooling and workflows

Great for: document-heavy assistants, enterprise search, “read everything in Drive → summarize” style tasks.

Meta Llama: Best when you need open weights, control, or self-hosting

Llama shines when your constraints aren’t just accuracy—they’re control:

• You want to run models locally or in your VPC
• You need predictable costs at scale
• You want to fine-tune and own the full serving stack

Llama models are available as open weights under Meta’s community license (read the license terms carefully for your use case). (huggingface.co)

Use Llama when you want:

• Self-hosting, privacy, or offline operation
• Lower marginal cost at high volume
• Customization/fine-tuning without vendor lock-in

Great for: internal tools with sensitive data, edge deployments, bespoke assistants.

Two examples (same task, different best choice)

Example 1: HR / Recruiting — rewrite a sensitive email with the right tone

You want clarity + empathy + low risk. That usually means: lower temperature + a model known for strong writing.

You are an HR partner.
Rewrite this message to a candidate who was rejected after the final round.
Requirements:
- empathetic, direct, and respectful
- 110–140 words
- no legal claims, no company-sensitive details
- include one sentence inviting them to reapply in the future

Message: <PASTE EMAIL HERE>

Output:
Return only the final email.

Often a good fit: Claude (tone + long context if you paste interview notes), or OpenAI for strong general output.

Example 2: AI Engineer — long-context “read the codebase” debugging

If you’re stuffing lots of files, logs, and docs into context, long-context models matter.

You are a senior backend engineer.
Given the following:
- 8 log snippets
- 3 config files
- the current FastAPI router

Task:
1) Identify the most likely root cause
2) Propose the minimal patch
3) List 3 regression tests

Output indicator:
Return a table with columns: Symptom | Root Cause | Fix | Test

Often a good fit: Gemini for long-context + speed, Claude for long-context synthesis, OpenAI for tool-heavy debugging workflows.

Final cheat sheet (what to pick tomorrow)

• Need fastest path to production features? OpenAI (platform.openai.com)
• Need beautiful writing + long documents? Claude (platform.claude.com)
• Need high-throughput + long-context document analysis? Gemini (ai.google.dev)
• Need self-hosting, control, or customization? Llama (huggingface.co)

Takeaway

“What model should we use?” is the wrong first question.

Ask instead: What constraints matter most—latency, cost, context length, privacy, tool integration, or writing quality? Once you pick your constraints, the provider choice becomes obvious—and your prompts (and product) get better overnight.