Choosing Between OpenAI, Anthropic, and Open Source

Six months ago, the choice was easy: use OpenAI. Now? You've got OpenAI, Anthropic, Google, Mistral, Meta's Llama, and a dozen other options. Each has tradeoffs.

I've shipped production features with most of these. Here's what actually matters when choosing.

The Major Players

OpenAI (GPT-4, GPT-3.5-turbo)

Best for: General-purpose tasks, wide ecosystem, proven reliability

OpenAI is still the default choice for most projects, and for good reason. The API is rock-solid, documentation is excellent, and GPT-4 handles most tasks well.

The downsides? Cost (GPT-4 is expensive), occasional outages during high-traffic periods, and the model can be overly cautious about certain content.

When to use: You need something that works reliably, you're building a general-purpose feature, or you want the largest ecosystem of tools and libraries.

Anthropic (Claude 3 family)

Best for: Long documents, nuanced reasoning, safety-conscious applications

Claude has become my go-to for anything involving long-form content. The 200K context window is game-changing for document analysis. Claude also tends to give more nuanced, less robotic responses.

The API has been stable, though Anthropic is smaller than OpenAI so there's more business risk. Pricing is competitive with GPT-4.

When to use: Legal documents, research papers, anything over 10K tokens, or when you need thoughtful responses rather than quick answers.

Open Source (Llama 3, Mistral, etc.)

Best for: Privacy requirements, cost control at scale, customization

Open source models have gotten surprisingly good. Llama 3 70B competes with GPT-3.5-turbo on many benchmarks. Mistral's models punch above their weight class.

The catch? You're responsible for hosting. That means GPU servers, which aren't cheap. You're looking at $500-3,000/month for decent inference hardware, plus the engineering time to set it up.

When to use: Data can't leave your infrastructure, you're processing massive volumes where API costs would be prohibitive, or you need to fine-tune extensively.

Decision Framework

Ask these questions:

1. How sensitive is your data?

If you're handling health records, financial data, or anything regulated, open source might be your only option. Running models on your own infrastructure means data never leaves your control.

2. What's your volume?

Under 10,000 requests/month: API costs are negligible. Use OpenAI or Anthropic.

10,000-100,000 requests/month: Still probably cheaper to use APIs, but start optimizing.

Over 100,000 requests/month: Run the numbers on self-hosting. It might make sense.

3. How complex are your tasks?

Simple classification, summarization, basic Q&A: GPT-3.5-turbo or Mistral 7B works fine.

Complex reasoning, code generation, analysis: GPT-4 or Claude 3 Opus.

Long document processing: Claude 3 (that context window matters).

4. How much can you invest in infrastructure?

If you don't have DevOps expertise, stick with APIs. Self-hosting AI models is not a weekend project.

The Hybrid Approach

Here's what we often recommend: start with APIs, then optimize.

Use OpenAI or Anthropic for your MVP. Get the product working, understand your usage patterns, identify bottlenecks. Then make targeted moves:

• High-volume, simple tasks? Move those to a cheaper model or self-hosted open source.
• Complex tasks that need quality? Keep those on GPT-4 or Claude Opus.
• Sensitive data workflows? Isolate those on self-hosted infrastructure.

This isn't about picking one provider. It's about using the right tool for each job.

Practical Comparison

Here's how I'd rank the options for common use cases:

Customer Support Chatbot: GPT-3.5-turbo (cost-effective, good enough)

Code Generation: GPT-4 or Claude 3 Opus (quality matters here)

Document Summarization: Claude 3 Sonnet (long context, good value)

Content Generation: GPT-4 for quality, GPT-3.5-turbo for volume

Data Extraction: Fine-tuned GPT-3.5-turbo or Mistral

Privacy-First Applications: Llama 3 self-hosted

The Bottom Line

Don't overthink this. If you're just starting out, use OpenAI. Their API is the easiest to work with, and GPT-3.5-turbo is cheap enough that cost optimization can wait.

Once you have a working product and real usage data, then evaluate alternatives. Moving between providers isn't that hard if you've abstracted your AI calls properly (which you should do anyway).

The worst choice is analysis paralysis. Pick something, ship it, learn from real usage.