Comparing LLM Providers

There are now dozens of LLM providers and hundreds of models to choose from. This tutorial cuts through the noise and compares the major options — what they’re good at, how they differ, and how to decide which one to use for your project.

This isn’t an exhaustive benchmark. Models improve constantly, and today’s rankings will shift. Instead, we’ll focus on the factors that matter when making practical decisions.

The Major Providers

OpenAI

The company behind GPT-4, GPT-4o, and the model that started the GenAI wave.

Models: GPT-4o (flagship), GPT-4o-mini (fast/cheap), o1/o3 (reasoning)

Strengths:

• Largest ecosystem — most tutorials, libraries, and integrations assume OpenAI
• Strong all-around performance across coding, writing, and reasoning
• Best structured output support (JSON schema enforcement)
• Widest tool/function calling support

Considerations:

• Closed source — you can’t inspect or self-host the models
• Pricing can add up at scale

Best for: General-purpose applications, prototyping, teams that want the broadest ecosystem support.

Anthropic

The company behind Claude, founded by former OpenAI researchers with a focus on AI safety.

Models: Claude Sonnet 4 (balanced), Claude Opus (most capable), Claude Haiku (fast/cheap)

Strengths:

• Excellent at long-context tasks — 200K token context window used effectively
• Strong at following nuanced instructions and system prompts
• Tends to be more cautious and less likely to hallucinate
• Very strong at code generation and analysis

Considerations:

• Smaller ecosystem than OpenAI
• Structured output support is less mature (no native JSON schema mode)

Best for: Applications requiring long documents, careful instruction following, or code-heavy workloads.

Google

Offers the Gemini family of models, integrated with Google Cloud.

Models: Gemini 1.5 Pro (flagship), Gemini 1.5 Flash (fast/cheap), Gemini Ultra

Strengths:

• Massive context windows — up to 1M+ tokens (can process entire codebases or books)
• Native multimodal support (text, images, video, audio in one model)
• Deep integration with Google Cloud services
• Competitive pricing

Considerations:

• API ergonomics are less polished than OpenAI/Anthropic
• Smaller third-party ecosystem

Best for: Multimodal applications, very long context needs, teams already on Google Cloud.

Open-Source Models

Models you can download and run yourself: Meta’s LLaMA, Mistral, Qwen, and others.

Models: LLaMA 3.1 (8B–405B), Mistral Large, Qwen 2.5, DeepSeek

Strengths:

• Free to use — no per-token API costs
• Full control — run on your own infrastructure, no data leaves your network
• Customizable — fine-tune freely without provider restrictions
• No rate limits

Considerations:

• Requires GPU infrastructure (or services like Together AI, Fireworks, Groq)
• Smaller models are less capable than frontier closed models
• You handle scaling, updates, and reliability

Best for: Privacy-sensitive applications, high-volume workloads where API costs are prohibitive, teams that need full control.

AWS Bedrock

Amazon’s managed service that provides access to multiple model providers through a single API.

Models: Claude (Anthropic), LLaMA (Meta), Mistral, Amazon Titan, and others

Strengths:

• Single API for multiple providers — switch models without changing code
• Integrated with AWS services (IAM, CloudWatch, VPC)
• Data stays within your AWS account
• Enterprise security and compliance features

Considerations:

• Slight latency overhead vs. calling providers directly
• Model availability can lag behind direct provider releases

Best for: Enterprise teams on AWS, applications requiring multiple model options, compliance-heavy environments.

How to Choose

By Use Case

By Priority

Optimize for capability → Use the latest frontier model from OpenAI or Anthropic. These are the most capable but also the most expensive.

Optimize for cost → Use smaller models (GPT-4o-mini, Claude Haiku, Gemini Flash) or open-source models. For many tasks, these perform nearly as well at a fraction of the cost.

Optimize for latency → Use smaller models or providers with edge infrastructure (Groq, Fireworks). Smaller models generate tokens faster.

Optimize for privacy → Self-host open-source models or use AWS Bedrock with VPC endpoints. Your data never leaves your infrastructure.

Multi-Provider Strategy

In practice, many production applications use multiple providers:

import OpenAI from "openai";
import Anthropic from "@anthropic-ai/sdk";

const openai = new OpenAI();
const anthropic = new Anthropic();

async function chat(prompt, provider = "openai") {
  if (provider === "openai") {
    const res = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: [{ role: "user", content: prompt }],
    });
    return res.choices[0].message.content;
  }

  const res = await anthropic.messages.create({
    model: "claude-sonnet-4-20250514",
    max_tokens: 1024,
    messages: [{ role: "user", content: prompt }],
  });
  return res.content[0].text;
}

Reasons to use multiple providers:

• Fallback — If one provider is down, route to another
• Best tool for the job — Use Claude for long documents, GPT-4o for structured output
• Cost optimization — Route simple tasks to cheap models, complex tasks to capable ones
• Avoid vendor lock-in — Keep your options open as the landscape evolves

Evaluating Models for Your Use Case

Don’t rely on benchmarks alone. The best model for your application depends on your data and requirements. Here’s a practical evaluation approach:

1. Create a test set — Collect 20–50 representative inputs that your application will handle
2. Define success criteria — What does a “good” response look like? Accuracy? Format? Tone?
3. Test 2–3 models — Run your test set through each model with the same prompts
4. Compare results — Score each model’s outputs against your criteria
5. Factor in cost and latency — A model that’s 5% better but 10x more expensive may not be worth it

What’s Next?

With the Building with LLM APIs section complete, you now know how to call models, stream responses, handle errors, and choose providers. The next section covers RAG — the most important pattern for building applications that need access to your own data. Start with Introduction to RAG.