Claude Token Counter Updated with Model Comparison Feature

✍️ OpenClawRadar📅 Published: April 20, 2026🔗 Source
Claude Token Counter Updated with Model Comparison Feature
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Tool Update: Model Comparison Added

Simon Willison upgraded his Claude Token Counter tool to add the ability to run the same count against different models for comparison. The tool accepts any Claude model ID, with options for four notable current models: Opus 4.7 and 4.6, Sonnet 4.6, and Haiku 4.5.

Key Findings from Tokenizer Changes

According to Anthropic's Opus 4.7 announcement, the model uses an updated tokenizer that improves text processing but causes the same input to map to more tokens—roughly 1.0–1.35× depending on content type.

Specific test results:

  • When pasting the Opus 4.7 system prompt into the token counting tool, Opus 4.7 used 1.46× the number of tokens as Opus 4.6
  • Opus 4.7 maintains the same pricing as Opus 4.6 ($5 per million input tokens, $25 per million output tokens), but token inflation means it could be around 40% more expensive
  • Claude Opus 4.7 is the first model to change the tokenizer, making comparisons primarily relevant between 4.7 and 4.6
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Image Token Counting Results

The token counter tool also accepts images. Opus 4.7 has improved image support, accepting images up to 2,576 pixels on the long edge (~3.75 megapixels), more than three times as many as prior Claude models.

Image test results:

  • A 3456×2234 pixel 3.7MB PNG showed 3.01× more tokens for Opus 4.7 compared to 4.6
  • Update: This 3× increase is entirely due to Opus 4.7 handling higher resolutions. A 682×318 pixel image took 314 tokens with Opus 4.7 and 310 with Opus 4.6—effectively the same cost

PDF Processing Comparison

For a 15MB, 30-page text-heavy PDF:

  • Opus 4.7 reported 60,934 tokens
  • Opus 4.6 reported 56,482 tokens
  • This represents a 1.08× multiplier, significantly lower than the multiplier for raw text

This tool is useful for developers who need to estimate costs and understand token usage differences between Claude model versions, particularly when planning migrations or comparing processing efficiency.

📖 Read the full source: HN AI Agents

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👀 See Also