Skip to content
Supervertaler Docs
Searching: All docs

Start typing to search the docs.

Token Usage & Costs

Supervertaler Workbench keeps a persistent log of the AI tokens and cost of every operation, plus a built-in Usage & Costs report to total and export it. Use it to answer “how much did this project cost?”, “how many tokens did we use this month?”, or “what should I bill this client for AI?” — across every provider, including local and custom models.

The log uses the same format as the Supervertaler for Trados plugin, so if you use both, their logs merge into a single analysis.

The usage log file

Every AI call appends one line to a monthly file:

…\Supervertaler\workbench\usage\usage-2026-06.jsonl

It is JSONL (one JSON object per line) — open it in Excel, or parse it with a script. A line looks like:

{"ts":"2026-06-18T16:07:03Z","product":"workbench","task":"BatchTranslate",
 "provider":"claude","model":"claude-sonnet-4-6","project":"My Patent Job",
 "file":"","src_lang":"English","tgt_lang":"Dutch","in_regular":654,
 "in_cache_read":0,"in_cache_write":27843,"out":808,"source":"actual",
 "cost_usd":0.11849325,"cost_known":true,"duration_s":24.9,"ok":true}

It is on by default. Turn it off in Settings → AI Settings → AI Cost Monitoring.

The Usage & Costs report

Open Tools → 💰 Token Usage & Costs…. The window totals your usage and lets you slice it:

  • Range — This month, Last 3 months, This year, or All time.
  • Group by — Project, Client, Model, Provider, Task, Day or Month.

Each row shows calls, input/output tokens, cost, and a % actual column (the share backed by provider-reported figures rather than estimates). The footer shows the range total and your month-to-date spend against your budget.

Exporting

Export CSV… and Export Excel… write the detailed ledger (one row per call) for the selected range — ready for invoicing or analysis.

Settings & budget

Settings → AI Settings → AI Cost Monitoring has:

  • Keep a persistent token-usage log — the on/off switch.
  • Monthly budget (USD) — a soft monthly limit (cents allowed; 0 disables). Once this month’s logged spend reaches it, starting a batch translation shows a warn-and-continue prompt. It is advisory and never blocks.

Pricing custom / self-hosted models

Costs come from a single price list, pricing.json, shared with the Trados plugin. To price a custom or self-hosted model — or override any rate for both products at once — copy the bundled modules/pricing.json to …\Supervertaler\pricing.json and add an entry keyed by the exact model id:

{ "models": { "my-university-llama": { "input": 0.0, "output": 0.0 } } }

Until a rate is set, a custom model’s tokens are still logged, with the cost marked unknown rather than guessed. Local models (Ollama) are priced at 0.

How accurate are the figures?

Every record is flagged actual or estimated in its source field — and the difference matters.

  • actual (the usual case): the token counts are the exact numbers the provider’s API reported for that call — the same numbers it bills you against. These are as accurate as it gets. This covers OpenAI, Claude, Gemini, Mistral, DeepSeek and OpenRouter, including the cached-token breakdown.
  • estimated: a fallback used only when the provider returned no usage data — currently local models (Ollama) and the occasional unparseable response. The estimate is a simple characters ÷ 4 heuristic. It’s reasonable for English but can be well off for other content: scripts such as Chinese, Japanese, Korean, Arabic and Cyrillic pack a different number of characters per token, so the estimate often under-counts them. Ollama is free, so for local models only the token count is approximate — there is no cost involved.

For cost, an actual record’s figure is the exact token count × the per-model rate from pricing.json, with cached tokens priced at each provider’s cache discount (e.g. Claude cache reads at 10% of the input rate and cache writes at 125%, OpenAI cache reads at 50%, Gemini 2.5+/3 at 25%). This matches how the Supervertaler for Trados plugin computes cost, so the two products agree. The one thing that can make it differ slightly from your provider’s bill:

  • The price list can lag a provider’s recent rate change. The model id and the token counts are still exact — only the unit price might be a little behind.

For the definitive bill, your provider’s own usage dashboard is authoritative. The ledger is built for tracking trends, attributing usage to projects and clients, and capacity planning — where the exact, provider-reported token counts are precisely what you want.

See also