[token-meter] Char-heuristic estimation diverges substantially from provider-reported usage tokens

[YaoQC-Ai]

Observation

token-meter estimates context size with a character-based heuristic(roughly ~4 chars per token) and exposes dual read-outs(surfaceTokens / pressureTokens). In a real long-running session theestimate diverged substantially from the provider's actual usage reported bythe API, in our observation by tens of percent.

This matters because downstream policies — most importantly compactionpressure triggering (thresholdRatio against the model's context window) —consume the estimate. A systematic bias shifts the effective compaction point:an over-estimate compacts too early (wasting usable context), an under-estimaterisks late compaction and provider-side context-window errors.

Suggestion

1. Calibrate with real usage: after each provider response that carriesusage statistics, fold the reported prompt_tokens into the meter(anchor the latest measured point; keep the heuristic only for the tailadded since). The data is already flowing through llm/stream — no newAPI surface needed.
2. Expose the read-out's nature: mark whether a given number isestimated or provider-reported, so UI surfaces (and plugins building onthe meter) can present the uncertainty honestly.
3. If calibration is out of scope for now, a doc note on the expected errorband of the heuristic would help deployments choose thresholdRatiomargins deliberately.

Environment

• @deepseek-ai/dsh: 0.1.0-rc.5 (global npm install)
• Observed in dsh web long-running sessions (context > 200K estimated tokens)

Note

Filed as an enhancement request; no functional bug — the heuristic works asdocumented, the ask is accuracy and transparency.

[denial123789]

This is a useful rc.5 observation. I checked the same boundary at rc.8 source revision 141eb6f, and much of the proposed calibration/transparency contract has since landed — with one important conservative fallback.

The rc.8 projection now separates:

• pressureTokens: the newest provider-reported prompt input (uncached + cache reads + cache writes);
• projectedTokens: that sample plus the signed heuristic surface movement since it was taken;
• heuristic systemTokens / toolsTokens / messageTokens, documented as approximate composition rows that are not expected to sum to the headline.

The Web occupancy display prefers projectedTokens, so the provider sample anchors the number while appended or compacted surface content updates the tail immediately.

Compaction does not read that UI projection; it calls tokenMeter.measure(). That service reuses provider usage only when the canonical request header still matches and provider total usage is no lower than the complete heuristic anchor. Otherwise it reprices the full request heuristically. The lower-bound check preserves conservative signed delta arithmetic, but it explains why an rc.8 compaction decision can still report an estimated baseline even when usage was returned.

For a surprising threshold decision, the most useful evidence is therefore: exact build, provider/model, adapter context window, resolved threshold, request-header identity, provider buckets, baseline.kind, baseline tokens, surfaceDeltaTokens, totalTokens, and the consumed log revision. A blanket change to four chars/token from one Session would be risky across CJK, JSON-heavy schemas, caches, and different model tokenizers.

I mapped the field contracts, anchor eligibility, UI mismatch, compaction diagnosis, calibration dataset, and regression gates here:

• Visual map: https://sandbaseai.github.io/deepseek-harness-handbook/token-meter-accounting.html
• Full guide: https://github.com/sandbaseai/deepseek-harness-handbook/blob/main/docs/en/operations/token-meter-accounting.md

[YaoQC-Ai]

Thank you for confirming that much of the proposed calibration/transparency contract has already landed in rc.8 — that is great news. The `pressureTokens`/`projectedTokens` split plus the documented approximate composition rows look like the right design, and we appreciate the conservative fallback in `tokenMeter.measure()` (reusing provider usage only when the request header matches and total usage is not below the heuristic anchor). Agreed on not blanket-changing the 4 chars/token heuristic from a single session — CJK, JSON-heavy schemas, caches and different tokenizers make that risky. We will gather the evidence fields you listed if we ever see a surprising threshold decision again. Thanks for the docs!

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