What Is dsh-whale-meter and Why You Need It Now (Post-8-17 Price Hike Context)

DeepSeek Harness is a powerful agentic coding harness, but for a long time it gave users almost no visibility into how many tokens they were actually consuming or what those tokens cost. That gap is exactly what dsh-whale-meter (仓库: Shiye-10Pages/dsh-whale-meter) was built to close. It is a native DSH plugin written in TypeScript under an MIT License, released on August 16, 2026, with a design goal of “one-line install, zero config, all data local.”

The timing is not accidental. On August 17, 2026, DeepSeek raised the prices of v4-flash and v4-pro and, for the first time, introduced peak/off-peak billing in China. That single change turned token spend from a background curiosity into a line item you must actively manage. As the broader industry scramble shows, teams are routinely blowing through their token budgets — TechCrunch reported in June 2026 that many teams were running at 3x their 2026 token budgets by April, with individual engineers seeing monthly token spikes in the $40,000 range. The days of “just let the model run” are over.

dsh-whale-meter answers the three questions every heavy DSH user now asks daily: How many tokens am I using? What am I actually spending? Is my usage tier creeping toward the top?

Key Features — Token Usage Tiers, Cost Dashboard, and the Whale Score Card

The headline feature is a gamified usage ladder with five tiers:

Each tier carries an estimated percentile — the project is careful to label this as an estimate, not a claimed global rank — plus a playful magnitude conversion that frames your usage as “≈ N reads of Three-Body.” It turns an abstract number of tokens into something you can actually feel.

The cost dashboard is the practical workhorse. It reports spend across four time windows — today, yesterday, month, and cumulative — in CNY by default, swappable to USD. Costs are estimated from the official DeepSeek pricing pages, so they track the real tariff rather than a stale hardcoded rate.

Two features make the dashboard genuinely useful rather than decorative:

• 8-17 price-hike comparison. The same usage is priced side-by-side under the old and new tariffs. Pre-August-17 usage is shown as a preview of what it would have cost under the new rate; post-August-17 usage gets a retroactive back-calculation of what it would have cost under the old rate. This makes the real impact of the hike measurable in one glance.
• Cross-price-day correctness. Price tables are organized by effective date ranges, so historical bills are never mis-computed against today’s rates.

How the 8-17 Price Change Works (v4-flash / v4-pro, Peak vs Off-Peak, Before-After Compare)

The August 17 change has two parts: a price hike and a new peak/off-peak structure. Both are baked into the whale-meter’s built-in price table (version 2026-08-16 already contains the new rates).

For v4-flash (per million tokens, cache miss), the changes are:

v4-pro adjusted in parallel: peak pricing of ¥0.30 / ¥9 / ¥27 (hit / miss / output), with off-peak exactly half. Peak hours are Beijing 9:00–12:00 and 14:00–18:00.

The non-obvious point most guides get wrong is this: even if you run entirely off-peak, the off-peak rate is still more expensive than the old flat pre-8-17 price. Look at the v4-flash table above — the off-peak miss rate of ¥1.50 beats the old ¥1.00 flat rate, and off-peak output at ¥4.50 is more than double the old ¥2.00. Off-peak is a discount relative to the new peak rate, not a return to the old prices. The whale-meter makes this visible with a ⚡½ badge shown during off-peak hours, and its before/after comparison panel surfaces exactly how much the hike costs you even on the cheapest schedule.

Multi-Vendor Precision Pricing: 46 Models Across 6 Providers, Including China Tiered Rates

whale-meter is not limited to DeepSeek. It ships with a pricing database covering 46 models across 6 vendors: DeepSeek, Anthropic, OpenAI, Google, Zhipu GLM, and Moonshot Kimi. Every model is itemized against the official pricing pages.

What sets it apart from most generic cost tools is how it handles tiered pricing from Chinese vendors, which most trackers simply ignore:

• GLM-5.1 input is priced by context length: <32K tokens costs ¥6, ≥32K costs ¥8 per million.
• GLM-4.7 sub-divides even further, tiering by output length.

Because the tier boundary depends on per-call context size, a flat price-table lookup produces wrong numbers. whale-meter applies the correct tier per call. When a model is not yet in the table, it is explicitly marked unpriced rather than silently estimated — an honest design choice that prevents fabricated cost figures.

Privacy & Data Locality: Fully On-Device, No Telemetry, Raw-Token-Only Storage

Privacy is a first-class design constraint, and it is the sharpest contrast with cloud-based dashboards. Everything runs on-device:

• Usage is stored in ~/.dsh/whale-meter/usage-YYYY-MM.jsonl.
• Only raw token counts are persisted — monetary amounts are recomputed on read, so no dollar figures are ever frozen into storage.
• There are no network requests except the optional balance query you explicitly enable.
• There is no telemetry of any kind.

For a tool that sits next to all of your agent activity, this matters. A cloud dashboard necessarily exfiltrates at least aggregate usage data; whale-meter keeps the entire ledger local. The raw-token-only design also means a later price change can be applied retroactively to historical usage without storing contradictory amounts.

Installation and Configuration (dsh plugin, zero-config, cordis.patch.yml options)

Installation is a single command. If you run DSH with a profile:

dsh plugin --profile <profile> add dsh-whale-meter

There is a web panel for TUI or headless setups at http://127.0.0.1:3080/whale-meter/api/summary. Configuration options live in cordis.patch.yml, letting you tweak behavior without touching source code. The project’s stated goal is zero-config for the common case: install, open the panel, and you immediately see your tiers and spend.

Limitations and Honest Caveats (Estimates Not Exact, Unknown Models Marked Unpriced, Community Plugin)

No cost tracker is perfect, and whale-meter is transparent about its boundaries:

• Costs are estimates, not invoices. Prices are drawn from official pages, but actual billing can differ due to rounding, promotions, or regional rules. Treat the dashboard as a budgeting guide, not an accounting record.
• Unknown models are marked unpriced. If whale-meter hasn’t been updated for a newly released model, it will not invent a price — you’ll see it flagged instead.
• It’s a community plugin. The repo is young (created August 16, 2026, roughly 3 stars at time of research) and MIT-licensed. You’re relying on an actively maintained community project rather than an official DeepSeek product.
• Tiers are percentile estimates, not confirmed global rankings. They give you a feel for where you sit, but “blue whale” is descriptive, not audited.

dsh-whale-meter vs Alternative Cost-Tracking Tools (CacheLens, AgentLedger, Agentic Metric)

How does the DSH-native plugin compare to the wider field of agent cost trackers?

The trade-off is clear. Generic proxy and ledger tools (CacheLens, AgentLedger, Agentic Metric) are broader — AgentLedger, for instance, tracks every real-world agent action with sub-second SSE latency rather than just LLM calls. But they require integration, a proxy, or an SDK, and they don’t understand DSH’s internals. whale-meter’s advantage is that it is a zero-config native plugin: no proxy to configure, no SDK to wire in, and it inherently knows the DSH data layout. If you’re all-in on DSH, the plugin approach wins on speed and locality; if you span many tools, a broader tracker makes more sense.

Verdict and Roadmap

For anyone running DeepSeek Harness seriously, dsh-whale-meter earns its place immediately. The 8-17 price hike means running blind on tokens now costs real money — and the peak/off-peak structure adds a scheduling decision you can only make with a meter. The gamified tiers make the abstract tangible, the before/after price comparison makes the hike measurable, and the fully-local, no-telemetry design makes it trustworthy.

The roadmap points in sensible directions: CSV/Markdown export for records and reporting, off-peak scheduling suggestions to help you shift work into the half-price window, and an enhanced TUI. Combined with the cache-ledger view — whose value grows after the hike, since the cache hit/miss price gap widened — these additions would round it into a complete cost-control tool.

If you use DSH and haven’t looked at your token bill yet, now is the moment. Install whale-meter, read your tier, and schedule your heavy runs off-peak. Your wallet will thank you.

FAQ

What exactly does dsh-whale-meter track? It tracks your DeepSeek Harness token usage across five gamified tiers (🐟 small fry through 🐳 blue whale) and estimates your cost over today, yesterday, the current month, and cumulative totals, in CNY or USD.

How does the 8-17 DeepSeek price change work? On August 17, 2026, DeepSeek raised v4-flash and v4-pro prices and introduced peak/off-peak billing. Peak hours are Beijing 9:00–12:00 and 14:00–18:00; off-peak costs exactly half the peak rate but is still more expensive than the old flat pre-8-17 price.

Is whale-meter data private? Yes. All data is stored locally in ~/.dsh/whale-meter/usage-YYYY-MM.jsonl, only raw token counts are persisted, and there is no telemetry or network traffic except an optional balance query you enable manually.

Does whale-meter support models beyond DeepSeek? Yes. It covers 46 models across 6 vendors — DeepSeek, Anthropic, OpenAI, Google, Zhipu GLM, and Moonshot Kimi — including China vendors’ tiered pricing such as GLM-5.1’s <32K vs ≥32K input rates.

Is whale-meter free and where do I get it? It is an MIT-licensed open-source project at Shiye-10Pages/dsh-whale-meter. Install with dsh plugin --profile <profile> add dsh-whale-meter, then open the web panel at http://127.0.0.1:3080/whale-meter/api/summary.