LLM Models
A list of models

Gemini 2.5 Series
Developer/Trainer 

 Google 

 

 

 Homepage 

 https://deepmind.google/models/gemini/pro/

Anthropic
Anthropic is a silicon valley startup and AI company that develops and sells LLM models and LLM-based products and services. They also developed the Model Context Protocol for passing information between models. 
 Key Information 
 
 Founded as a competitor to OpenAI 
 Developed the Claude model series, including Claude 3 Opus, Sonnet, and Haiku 
 Known for safety-focused AI development approaches 
 Created the Model Context Protocol (MCP) standard 
 
 Relationship to Other Models 
 Anthropic are one of the earliest competitors to OpenAI and their Claude Sonnet and Opus series models were early competitors to GPT-4. 
 Claude Models 
 The Claude model series includes several versions: 
 Claude 3 Series 
 
 Anthropic Claude 3 Opus 
 Anthropic Claude 3 Sonnet 
 Anthropic Claude 3 Haiku 
 
 Other Models 
 
 Anthropic Claude 2 
 Anthropic Claude 1 
 
 Related Content 
 
 Model Context Protocol

Claude 4
Claude 4 is a large language model developed by Anthropic. 

 Key Features 

 

 Advanced reasoning capabilities 

 Improved safety measures 

 Better performance on complex tasks

Anthropic Claude 3 Opus
Overview 
 Claude 3 Opus is Anthropic's most powerful model, designed for highly complex tasks that require the highest levels of reasoning and understanding. 
 Capabilities 
 
 Advanced reasoning and problem-solving 
 Complex content creation and editing 
 Comprehensive understanding of nuanced topics 
 High-quality code generation and debugging 
 
 Performance 
 
 More capable than Claude 3 Sonnet and Haiku 
 Excellent at handling complex reasoning tasks 
 Strong performance in benchmarks like MMLU and GSM8K 
 
 Use Cases 
 
 Research and analysis 
 Complex content creation 
 Code generation with extensive reasoning 
 
 flowchart LR

A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]

Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?
Title: Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents? 
 Authors: Thibaud Gloaguen, Niels Mündler (ETH Zurich), Veselin Raychev, Mark Müller, Martin Vechev (LogicStar.ai / ETH Zurich) 
 Published: June 2026 — arXiv:2602.11988 
 Link: https://arxiv.org/abs/2602.11988 
 Summary 
 A widespread practice in software development is to tailor coding agents to repositories using context files (AGENTS.md, CLAUDE.md), strongly encouraged by agent developers. This paper presents the first rigorous evaluation of whether such context files actually improve task completion performance. 
 Key findings: 
 
 LLM-generated context files do NOT improve success rates — they slightly reduce them (−0.5% on SWE-Bench, −2% on CTX-Bench), though not statistically significantly 
 They increase inference cost by over 20% on average, as agents follow instructions and run more tests/exploration 
 Developer-written context files outperform LLM-generated ones by ~7%, but the improvement over having no file is marginal (p=21%) 
 Repository overviews are not helpful — agents don't discover relevant files faster with an overview section 
 Instructions ARE followed — if a tool is mentioned in the context file, usage increases dramatically. The cost increase comes from more thorough testing, not ignoring instructions 
 
 The authors created CTX BENCH , a new benchmark of 138 real-world Python tasks from 12 niche open-source repos with developer-committed context files, complementing SWE-Bench Lite evaluation. 
 Conclusion: Context files should only contain non-standard coding practices not already in the README. LLM-generated ones are not worth it yet. Any attempts to improve performance should be rigorously evaluated before deployment.