LLM Token Estimation Benchmark Data

This dataset presents benchmark results for token count estimation accuracy across 12 major large language models (LLMs) from leading providers, collected and published as part of the developer tooling research on mohitkhare.me. Accurate token estimation is a critical component of cost prediction, context window management, and prompt engineering for production LLM applications.
Each row corresponds to a single model and includes the following fields: model name, provider, tokenizer family, average tokens per English word, estimation accuracy percentage (measured against exact tokenizer output on a standardized 10,000-sentence corpus), context window size, prompt overhead in tokens, encoding type (BPE or SentencePiece), and the date the benchmark was conducted.
The 12 models benchmarked include GPT-4o and GPT-4 Turbo (OpenAI), Claude 3 Opus and Claude 3.5 Sonnet (Anthropic), Gemini 1.5 Pro (Google), Llama 3 70B (Meta), Mistral Large (Mistral), Command R+ (Cohere), Grok 3 (xAI), DeepSeek V3 (DeepSeek), Phi-3 Medium (Microsoft), and Qwen 2.5 72B (Alibaba). The benchmark corpus consists of diverse English text spanning news articles, technical documentation, conversational dialogue, and code snippets.
Key findings include: BPE-based tokenizers consistently achieve higher estimation accuracy (96-97%) compared to SentencePiece variants (95-96%); Anthropic models exhibit the lowest tokens-per-word ratio (1.28) indicating efficient vocabulary coverage; and prompt overhead varies from 3 to 6 tokens depending on the model chat template. Detailed methodology and analysis are available on the developer blog at mohitkhare.me/blog.