Source URL: https://simonwillison.net/2025/Feb/25/aider-polyglot-leaderboard/
Source: Simon Willison’s Weblog
Title: Aider Polyglot leaderboard results for Claude 3.7 Sonnet
Feedly Summary: Aider Polyglot leaderboard results for Claude 3.7 Sonnet
Paul Gauthier’s Aider Polyglot benchmark is one of my favourite independent benchmarks for LLMs, partly because it focuses on code and partly because Paul is very responsive at evaluating new models.
The brand new Claude 3.7 Sonnet just took the top place, when run with an increased 32,000 thinking token limit.
It’s interesting comparing the benchmark costs – 3.7 Sonnet spent $36.83 running the whole thing, significantly more than the previously leading DeepSeek R1 + Claude 3.5 combo, but a whole lot less than third place o1-high:
Model
% completed
Total Cost
claude-3-7-sonnet-20250219 (32k thinking tokens)
64.9%
$36.83
DeepSeek R1 + claude-3-5-sonnet-20241022
64.0%
$13.29
o1-2024-12-17 (high)
61.7%
$186.5
claude-3-7-sonnet-20250219 (no thinking)
60.4%
$17.72
o3-mini (high)
60.4%
$18.16
No results yet for Claude 3.7 Sonnet on the LM Arena leaderboard, which has recently been dominated by Gemini 2.0 and Grok 3.
Via @paulgauthier
Tags: aider, anthropic, claude, evals, generative-ai, ai, llms, paul-gauthier
AI Summary and Description: Yes
Summary: The text discusses the latest performance and cost metrics of the Claude 3.7 Sonnet model as evaluated through the Aider Polyglot leaderboard, highlighting its superior capabilities and comparing it with other models in terms of efficiency and expense. This is of particular relevance to professionals in AI and LLM security due to the implications of model performance on security practices and operational costs.
Detailed Description: The provided content focuses on the recent evaluation of the Claude 3.7 Sonnet model within the context of independent benchmarking of large language models (LLMs). Paul Gauthier’s Aider Polyglot benchmark is recognized for its focus on LLMs that involve coding, which is critical for development and operational efficiency in AI.
Key Points:
– **Model Performance**: The Claude 3.7 Sonnet model achieved the highest completion rate in the benchmark, demonstrating its effectiveness for tasks likely related to code generation or technical problem-solving.
– **Cost Efficiency**: The benchmarking results reveal varying costs for different models, suggesting that while higher performance models may incur more expenses, they may also yield better results, which can be critical in decision-making.
– **Comparative Analysis**: A comparison of costs among leading models indicates that the Claude 3.7 Sonnet, despite its higher operational cost compared to the previously leading combination, still offers significant advantages over the lower-ranked models.
– **Market Positioning**: The text also notes the absence of Claude 3.7 on the LM Arena leaderboard, indicating competitive dynamics in the market and reflecting how rapidly evolving benchmarks can impact model adoption and trust in AI systems.
– **Implications for Security Professionals**: The performance and cost metrics could influence security and compliance architects, as they evaluate models for deployment in secure environments, needing to balance performance with budget constraints and potential vulnerabilities associated with code generation models.
This analysis emphasizes the importance of performance metrics in assessing AI models, particularly amidst growing concerns about security implications tied to generative AI capabilities and deployment in sensitive applications.