Case study: testing 5 models across summarization, extraction, ideation, and code—looking for eval ideas

I’ve been running systematic tests comparing Claude, Gemini Flash, GPT-4o, DeepSeek V3, and Llama 3.3 70B across four key tasks: summarization, information extraction, ideation, and code generation.

**Methodology so far:**

– Same prompts across all models for consistency

– Testing on varied input types and complexity levels

– Tracking response quality, speed, and reliability

– Focus on practical real-world scenarios

**Early findings:**

– Each model shows distinct strengths in different domains

– Performance varies significantly based on task complexity

– Some unexpected patterns emerging in multi-turn conversations

**Looking for input on:**

– What evaluation criteria would be most valuable for the ML community?

– Recommended datasets or benchmarks for systematic comparison?

– Specific test scenarios you’d find most useful?

The goal is to create actionable insights for practitioners choosing between these models for different use cases.

*Disclosure: I’m a founder working on AI model comparison tools. Happy to share detailed findings as this progresses.*