Developer finds paid LLMs tied on quality, judge model biased results

A developer conducted a benchmark test comparing five language models: Llama, GPT, DeepSeek, and two Claude models, focusing on cost per query, speed, and answer quality. The initial results showed minimal differences in quality scores among the paid models, suggesting cost and speed should be the primary decision factors. However, upon closer inspection, the developer found that the quality scores were not statistically significant and that the judge model used for grading was one of the contestants, potentially biasing the results. Re-grading the answers with a paid judge revealed that all paid models achieved perfect scores, indicating that quality is not a differentiator among them. AI

IMPACT Highlights the importance of rigorous benchmarking and the potential for bias in AI model evaluations.

RANK_REASON Developer's personal benchmark and analysis of existing models, not a new release or significant industry event.

Read on dev.to — LLM tag →

• Claude
• Claude Haiku 4.5
• Claude Sonnet-5
• DeepSeek
• DeepSeek V4-Pro
• generative pre-trained transformer
• GPT 5.6 Luna
• llama
• llama3.2
• pytest

• model release
• product

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