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<h1>BaseBench</h1>
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const markdown = `
BaseBench: A Foundational Language Model Evaluation Framework
Description:
BaseBench is a targeted evaluation framework designed to assess the fundamental capabilities of large language models across a spectrum of basic yet crucial tasks. This suite focuses on core competencies that serve as building blocks for more complex language understanding and generation.
**Features**:
1. Encoding/Decoding Proficiency: Tests the model's ability to work with common encoding schemes like Base64 and ROT13, evaluating its understanding of data representation and transformation.
2. Basic Mathematical Reasoning: Assesses the model's capacity to perform simple arithmetic operations and mathematical problem-solving, gauging its numerical processing capabilities.
3. Linguistic Analysis: Examines the model's grasp of fundamental language properties such as character counting and frequency analysis, probing its understanding of word structure and composition.
4. Error Detection and Correction: Challenges the model to identify and rectify typographical errors, testing its language pattern recognition and error handling abilities (tokenization).
**Purpose**:
BaseBench aims to provide a clear, quantifiable measure of a language model's proficiency in these foundational areas. By focusing on these essential skills, the benchmark offers:
1. A standardized baseline for comparing different models or versions.
2. Insight into a model's fundamental processing capabilities.
3. A tool for identifying potential gaps in basic language and data handling skills.
4. A means to track incremental improvements in core model competencies.
5. Difficult enough to avoid saturation
| Rank | Model | Accuracy | Time | Speed |
|------|------------------------------------|--------------------|-------|-----------|
| 1 | openai/gpt-4o | 59.00% (1475/2500) | 03:17 | 12.66it/s |
| 2 | anthropic/claude-3.5-sonnet:beta | 52.56% (1314/2500) | 14:44 | 2.83it/s |
| 3 | mistralai/mistral-large-2407 | 37.20% (930/2500) | 05:13 | 7.96it/s |
| 4 | openai/gpt-4o-mini | 36.92% (923/2500) | 08:28 | 4.91it/s |
| 5 | anthropic/claude-3-haiku:beta | 36.72% (918/2500) | 06:20 | 6.57it/s |
| 6 | google/gemini-pro-1.5 | 26.92% (673/2500) | 03:05 | 13.51it/s |
| 7 | google/gemma-2-27b-it | 25.24% (631/2500) | 05:52 | 7.08it/s |
| 8 | meta-llama/llama-3.1-405b-instruct | 24.24% (606/2500) | 07:19 | 5.69it/s |
| 9 | 01-ai/yi-large | 20.68% (517/2500) | 02:37 | 15.83it/s |
| 10 | mistralai/mixtral-8x22b-instruct | 19.60% (490/2500) | 04:32 | 9.18it/s |
| 11 | meta-llama/llama-3.1-70b-instruct | 19.04% (476/2500) | 18:01 | 2.31it/s |
**Insights**:
- GPT models lead (only Anthropic's flagship manages to beat 4o-mini)
- Mistral Large is an outlier, however it beats GPT-4o-mini easily (also corresponding to the MMLU-Pro score)
- Llama models score fairly low
- Closed source models/proprietry tend to score better (Mistral Large), due to training differences?
- Gemini is fast, however quality is comparable to Gemma
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