Compare Models

Comparing LLM Models

Major Model Families

Commercial Models
| Model | Provider | Context | Best For |
|-------|----------|---------|----------|
| GPT-4o | OpenAI | 128K | General, coding |
| GPT-4o-mini | OpenAI | 128K | Cost-effective |
| Claude 3.5 Sonnet | Anthropic | 200K | Long docs, analysis |
| Claude 3 Opus | Anthropic | 200K | Complex reasoning |
| Gemini 1.5 Pro | Google | 1M | Very long context |
| Gemini 1.5 Flash | Google | 1M | Fast, cheap |

Open Source Models
| Model | Provider | Params | Context | Highlights |
|-------|----------|--------|---------|------------|
| Llama 3.1 8B | Meta | 8B | 128K | Best small model |
| Llama 3.1 70B | Meta | 70B | 128K | Near GPT-4 |
| Llama 3.1 405B | Meta | 405B | 128K | Frontier open |
| Mistral 7B | Mistral | 7B | 32K | Efficient |
| Mixtral 8x7B | Mistral | 47B | 32K | MoE, fast |
| Qwen 2 72B | Alibaba | 72B | 32K | Multilingual |

Decision Framework

Cost Optimization

High Volume, Simple Tasks → Small model (GPT-3.5, Llama-8B)
Medium Complexity → Mid-tier (GPT-4o-mini, Claude Haiku)
Complex Reasoning → Frontier (GPT-4o, Claude Opus, Llama 405B)

Latency Requirements
| Requirement | Recommendation |
|-------------|----------------|
| Real-time (<500ms) | Smaller models, local inference |
| Interactive (1-2s) | GPT-4o, Claude Sonnet |
| Batch processing | Whatever maximizes quality |

Privacy/Deployment
| Requirement | Recommendation |
|-------------|----------------|
| Data never leaves infra | Open source, local deployment |
| Regulated industry | Local or approved cloud regions |
| Maximum capability | Commercial APIs |

Benchmark Comparison

General Reasoning (MMLU)
| Model | MMLU Score |
|-------|------------|
| GPT-4o | ~88% |
| Claude 3.5 Sonnet | ~88% |
| Llama 3.1 405B | ~88% |
| Llama 3.1 70B | ~83% |
| GPT-4o-mini | ~82% |

Coding (HumanEval)
| Model | Pass@1 |
|-------|--------|
| GPT-4o | ~90% |
| Claude 3.5 Sonnet | ~92% |
| DeepSeek Coder | ~90% |

Practical Selection Tips
1. Start with GPT-4o-mini or Claude Haiku for prototyping
2. Upgrade to stronger models only where needed
3. Consider fine-tuned smaller models for specific tasks
4. Benchmark on YOUR use case, not public benchmarks
5. Factor in rate limits, latency, and cost at scale