Claude 4.7 tokenizer costs made HN look past the sticker price

HN's thread was not really about whether Claude 4.7 is smarter. It was about the quieter question underneath developer pricing: what happens when the same working context becomes more tokens? The linked measurement compared Claude 4.6 and 4.7 token counts on material that looks like real Claude Code usage, including CLAUDE.md files, prompts, diffs, terminal output, stack traces, and technical prose.

The author used Anthropic's count_tokens endpoint rather than a full inference run, so the comparison focused on tokenization itself. Anthropic's migration notes put the new tokenizer in a rough 1.0-1.35x range, but the post found higher ratios for some technical-document samples and elevated counts across several coding-adjacent inputs. The practical concern is that the sticker price can stay the same while quotas, cache costs, and rate-limit pressure feel different.

The community did not settle on one reading. One thread framed frontier models as living on a performance-cost curve, where newer Opus releases may simply occupy a more expensive point on the curve. Another pushed back that for professional software work, the bigger cost is still the engineer's time spent steering, reviewing, and cleaning up AI output. A third line of discussion asked whether teams should stop defaulting to the strongest model and move routine work to smaller or local models when the task allows it.

That is the useful HN takeaway. Coding-agent pricing is no longer just a monthly subscription or per-token rate. It is tokenizer behavior, context compaction, cache hit rates, model routing, and human review time. Claude 4.7 may be worth the extra burn on hard tasks. But teams that care about cost need to measure per-task token use directly, because the model name alone no longer tells them what a coding session will consume.

The debate also matters for subscription buyers. Plan limits and model multipliers are policy surfaces; real workflows accumulate repository context, retained instructions, repeated compaction, and cached prefixes. A useful comparison is therefore not just a model card. It is the same task run across models with token use, latency, number of corrections, and final diff quality measured together.