ModelCostSaver Predict the cost of an LLM call before you make it, and pick the cheapest model that still does the job, offline, from your editor.
No API keys Offline by default No telemetry License Dependencies
ModelCostSaver is a Model Context Protocol server. It gives any AI coding agent or IDE a free, zero-config tool that answers the three questions every agent should ask before an LLM call:
What will this prompt cost on each candidate model? (predict_cost, estimate_cost) Which is the cheapest model that meets the task? (select_optimal_model) How do my options compare side by side? (compare_models) It is pure pricing-and-routing math over a bundled, dated catalog, so the core needs no API keys and makes no network calls.
Quick start Run it directly with npx (no install, no keys):
npx -y @workswarm/modelcostsaver Or write the config for your editor in one command:
npx -y @workswarm/modelcostsaver install --client cursor Add to Cursor — one click installs it in Cursor. Or drop the block below into ~/.cursor/mcp.json, or run npx -y @workswarm/modelcostsaver install --client cursor.
Listed on the official MCP registry and editor MCP directories as io.github.sachinuppal/modelcostsaver.
The seven tools Tool What it answers estimate_cost Cost of one call when you already know (or can estimate) the token counts. predict_cost Forecast cost across a candidate set from a prompt, before the call. Ranked cheapest-first. select_optimal_model The cheapest model that meets the task tier, capabilities, and budget, with full reasoning. compare_models A side-by-side cost table for a fixed token shape, with relativeToCheapest. list_models / get_pricing The pricing catalog, filterable by provider, tier, capability, or max input price. optimize_request "I plan to call model X, can I do better?" Returns the cheaper option and the savings. record_usage Append a local usage record (opt-in; off unless MODELCOSTSAVER_LEDGER=on). Every cost-bearing result carries catalogVersion and asOf so you can see how fresh the prices are. Every selection carries a reasoning array, never a black-box pick.
Trust: no keys, offline, no telemetry For a tool that sits in your editor, trust is the whole pitch. ModelCostSaver is:
No API keys. The core does pricing math, not provider calls. Nothing to leak. Offline by default. The core tools return correct answers with no network access. The only outbound request is an opt-in catalog refresh (MODELCOSTSAVER_REFRESH=on), a single GET of a static JSON, zod-validated before it can replace the bundled catalog, and it always falls back to the bundle on any failure. No telemetry. Ever. The default is silent and local. record_usage only writes when you set MODELCOSTSAVER_LEDGER=on, and only to a JSONL file under your own config dir. Two dependencies. @modelcontextprotocol/sdk and zod. Nothing else. Small supply-chain surface, fast npx cold start. Apache-2.0. An open-source developer tool published by Workswarm as @workswarm/modelcostsaver. The shipped bundle contains no proprietary or internal-service code: no internal-framework imports and no internal identifiers, just dependency-free pricing-and-routing math. stdout carries only JSON-RPC; all logs go to stderr.
Install per IDE
ModelCostSaver speaks stdio MCP, so the entry is the same npx command everywhere. Use install --client
Cursor ~/.cursor/mcp.json (global) or .cursor/mcp.json (project):
{ "mcpServers": { "modelcostsaver": { "command": "npx", "args": ["-y", "@workswarm/modelcostsaver"] } } } npx -y @workswarm/modelcostsaver install --client cursor Claude Code claude mcp add modelcostsaver -- npx -y @workswarm/modelcostsaver or a .mcp.json in the repo root (which install --client claude writes):
{ "mcpServers": { "modelcostsaver": { "command": "npx", "args": ["-y", "@workswarm/modelcostsaver"], "env": { "MODELCOSTSAVER_PROVIDERS": "anthropic" } } } } Claude clients run Claude for their own inference, so the install seeds MODELCOSTSAVER_PROVIDERS=anthropic as a sensible default for target: self recommendations. Override it per call or with the env var. See Self vs code.
Claude Desktop Add the same mcpServers block to claude_desktop_config.json.
VS Code / GitHub Copilot .vscode/mcp.json:
{ "servers": { "modelcostsaver": { "command": "npx", "args": ["-y", "@workswarm/modelcostsaver"], "type": "stdio" } } } npx -y @workswarm/modelcostsaver install --client vscode Windsurf ~/.codeium/windsurf/mcp_config.json with the same mcpServers block, or:
npx -y @workswarm/modelcostsaver install --client windsurf Cline / Zed / Antigravity Same stdio command/args. Use the matching installer:
npx -y @workswarm/modelcostsaver install --client cline npx -y @workswarm/modelcostsaver install --client zed npx -y @workswarm/modelcostsaver install --client antigravity After adding the server, restart the client and confirm the seven tools appear in the tool list.
Two axes: self vs code ModelCostSaver advises; it does not route traffic. So every recommendation is filtered to what you can actually act on, along two independent axes.
Axis 1, availability. Recommendations are scoped to a set of allowed providers. The default is derived from the connected client (read from the MCP handshake): a Claude client defaults to anthropic because its own inference is Claude; multi-provider clients (Cursor, VS Code, Windsurf, Cline, Zed, Antigravity) and unknown clients default to all providers. The scope and its source are always echoed in reasoning, and it is overridable: a per-call providers arg, then MODELCOSTSAVER_PROVIDERS, then config, then the client default, then all.
Axis 2, target.
target: 'self' (default): the agent's or your own next inference in this client. The Axis-1 scope applies. In Claude Code this means cross-tier Anthropic moves (Opus to Haiku), which you can act on right now. target: 'code': a model you will call from your own application, where you supply that provider's key. The client scope does not apply, so all in-catalog providers are eligible. ModelCostSaver is always honest about the gap: if the globally-cheapest model is outside your actionable set, it is surfaced as cheaperIfAvailable with the reason, never silently chosen. For example, a Claude Code target: self summarize call selects claude-haiku-4-5 and notes that a cheaper non-Anthropic model exists if you pass target: code.
How it predicts Tokens. Exact counts if you supply them; otherwise a heuristic estimate (~4 chars/token, tunable via MODELCOSTSAVER_CHARS_PER_TOKEN). The heuristic is approximate but common-mode across candidates, which is what relative ranking needs. Output tokens come from your explicit value, then the task class cap, then the model cap, then a conservative default. Cost. (inTok / 1e6) * inputPerMillion + (outTok / 1e6) * outputPerMillion, in full-precision USD and as integer usdMicros. A prediction is never rounded to cents. Selection. Resolve the target tier (from an explicit taskClass, else a transparent keyword/length classifier), filter candidates by tier (degrade up, never below the floor), capabilities, and provider scope, forecast each, drop those over budget into rejected, and pick the cheapest survivor. Every step is recorded in reasoning, and a fallbackChain is returned for retry-on-failure. Configuration All config is optional. Precedence: tool-call arg, then env var, then modelcostsaver.config.json (cwd, then your user config dir), then the built-in default.
Key Env Default Purpose tier overrides MODELCOSTSAVER_TRIVIAL_MODEL, _FAST_MODEL, _STANDARD_MODEL, _REASONING_MODEL catalog cheapest per tier Pin a preferred model per tier. providers MODELCOSTSAVER_PROVIDERS client-derived Allowlist for recommendations (Axis 1). default provider MODELCOSTSAVER_PROVIDER none Bias select_optimal_model. include local MODELCOSTSAVER_INCLUDE_LOCAL off Surface self-hosted / $0 models. chars/token MODELCOSTSAVER_CHARS_PER_TOKEN 4 Tune the token estimator. refresh MODELCOSTSAVER_REFRESH off Enable the opt-in remote catalog refresh. catalog url MODELCOSTSAVER_CATALOG_URL bundled Override the refresh source. ledger MODELCOSTSAVER_LEDGER off Enable the local record_usage write. telemetry MODELCOSTSAVER_TELEMETRY off Kept off; listed for transparency. Pricing data Prices change often, so ModelCostSaver ships a versioned, dated seed and is honest about its freshness.
The bundled catalog.json carries a catalogVersion, an asOf date, and a source on every entry. Default behavior is offline: it reads only the bundled catalog. MODELCOSTSAVER_REFRESH=on fetches a single static JSON, validates it with zod, caches it with a TTL, and falls back to the bundle on any failure. A pricingOverrides map in modelcostsaver.config.json lets you inject negotiated or enterprise rates without forking. Verify before you trust a number for billing. The seed is re-checked against each provider's public pricing page at release; the asOf date tells you when. For absolute precision in your own accounting, confirm against your provider invoice.
Development npm install # first time only npm run build # tsup bundle to dist/index.js npm test # vitest npm run typecheck # tsc --noEmit npm run smoke # stdio JSON-RPC smoke test (asserts stdout stays clean)
Server Config
{
"mcpServers": {
"modelcostsaver": {
"command": "npx",
"args": [
"-y",
"@workswarm/modelcostsaver"
]
}
}
}