feat(portfolio): K6 — pre-run cost simulation, priced what-if (parity row 18) [skip-docs]
Before ANY spend the operator sees a deterministic UPPER-BOUND USD estimate for a (portfolio-)run — a what-if over the models in model_map.json (Claude models) × effort levels (S3.6-analog, D-I pkt. 3 MUST-krav). No network, no model call, no key: pure config arithmetic (bound by an import-purity test, mirroring okf.py). - contracts.py: ModelPriceContract (usd_per_mtok > 0 + REQUIRED source + source_date so a stale rate is visible, never silent, §1) + PricingContract (non-empty; no hardcoded fallback rate) + load_pricing/_bundled_pricing. - data/pricing.example.json: per-Mtok rate per model id, each with source+date. Example rates are Anthropic's OUTPUT price (the higher rate) so the whole cap billed at that single rate can only overstate — the figure is marked ESTIMAT. Covers the model model_map configures, so the default path runs green. - costsim.py: estimate_costs (n_projects × cap × effort_factor tokens at the per-Mtok rate; a model with no price fails fast "missing price for <id>", never a guess) + render_estimate + `python -m …costsim`. Effort factors are a coarse modeling weight (not prices) — max effort = full cap = the true upper bound. No price literal anywhere (grep-guard proves it). - tests/test_costsim.py: schema fail-fast, missing-price fail-fast, scales with model × effort + reproducible, grep-guard, import purity, bundled-example + CLI offline smoke. Three seams detach-proven RED (effort factor, price guard, price literal). 462→478 green, golden byte-exact, full gate clean (ruff+format+mypy strict, 23 src files), run_s10.py/runs/ byte-untouched. README test-count sync ×2 + costsim.py module note. CLI run-total-cap wiring stays out of scope (planen lists 4 files); the mechanism is complete and proven load-bearing. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01RiTwaKLesgcwXx2mDviqpt
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5 changed files with 495 additions and 2 deletions
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@ -29,6 +29,7 @@ from pydantic import BaseModel, Field, model_validator
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_MODEL_MAP_RESOURCE = "data/model_map.json"
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_REFERENCE_PROJECTS_RESOURCE = "data/reference_projects.json"
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_PRICING_RESOURCE = "data/pricing.example.json"
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# C2.6: an empty-string model id is a startup schema error, never a client-layer one.
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_ModelId = Annotated[str, Field(min_length=1)]
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@ -87,6 +88,32 @@ class ReferenceProjectsContract(BaseModel):
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projects: list[ReferenceProjectContract] = Field(min_length=1)
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class ModelPriceContract(BaseModel):
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"""One model's per-Mtok USD rate (K6 cost sim) — provenance is REQUIRED.
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``usd_per_mtok`` must be positive (a non-positive rate is a startup schema
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error, never a guessed price). ``source`` + ``source_date`` are mandatory so
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a stale rate is always VISIBLE (§1 honesty) — a price with no provenance
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would let a silently-outdated figure through, exactly what the ESTIMAT label
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must never hide.
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"""
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usd_per_mtok: float = Field(gt=0)
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source: str = Field(min_length=1)
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source_date: str = Field(min_length=1)
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class PricingContract(BaseModel):
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"""Model-id -> per-Mtok price (validates data/pricing.example.json, K6).
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Non-empty by construction (an empty price book is a startup error). There is
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NO hardcoded fallback rate anywhere on the cost-sim path — a missing price is
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fail-fast, never a guess (method-spec §10, D-I pkt. 3).
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"""
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prices: dict[str, ModelPriceContract] = Field(min_length=1)
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class Contracts(BaseModel):
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"""The validated bundle of all startup contracts."""
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@ -127,6 +154,25 @@ def _bundled_reference_projects() -> dict[str, Any]:
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return raw
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def _bundled_pricing() -> dict[str, Any]:
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raw: dict[str, Any] = json.loads(
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files("portfolio_optimiser_claude").joinpath(_PRICING_RESOURCE).read_text(encoding="utf-8")
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)
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return raw
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def load_pricing(raw: dict[str, Any] | None = None) -> PricingContract:
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"""Validate the cost-sim pricing config (K6, fail-fast before any estimate).
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Raises ``pydantic.ValidationError`` on the first malformed/missing price
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(§10). ``raw`` defaults to the bundled ``data/pricing.example.json`` EXAMPLE,
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whose SHAPE is validated (not its rates — those carry source+date so a stale
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figure is visible, never silent, §1). The suite never spends against it.
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"""
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data = _bundled_pricing() if raw is None else raw
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return PricingContract(**data)
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def load_reference_projects(raw: dict[str, Any] | None = None) -> ReferenceProjectsContract:
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"""Validate the portfolio config at startup (§10, fail-fast before any run).
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195
src/portfolio_optimiser_claude/costsim.py
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195
src/portfolio_optimiser_claude/costsim.py
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@ -0,0 +1,195 @@
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"""Pre-run cost simulation (method-spec §8/§10 analog; S3.6; paritetsrad 18; K6).
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Before ANY spend, the operator can see an ESTIMATED upper-bound USD cost for a
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(portfolio-)run — a what-if over the models configured in ``model_map.json``
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(Claude models) × effort levels. Pricing is schema-validated config
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(``data/pricing.example.json``): a per-Mtok USD rate per model id, each carrying
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a REQUIRED source + date so a stale price is visible, never silent (§1 honesty).
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A model configured in ``model_map`` with no price fails FAST ("missing price for
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<id>") — the estimate is never guessed from a hardcoded rate (there is no price
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literal anywhere in this module; the grep-guard test proves it).
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The estimate is a deterministic UPPER BOUND, not a forecast: it bills the whole
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token cap (portfolio shape × per-project cap × an effort weight) at the model's
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per-Mtok rate. The example rates are Anthropic's OUTPUT price (the higher of the
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two published rates), so billing the whole cap at that single rate can only
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overstate, never understate — the figure is marked ESTIMAT in the output. Real
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runs cost less (input is cheaper and often cached, output is small). No network,
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no model call, no key: pure config arithmetic (offline invariant; bound by the
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import-purity test, mirroring ``okf.py``).
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Run: uv run python -m portfolio_optimiser_claude.costsim [--projects N]
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[--token-cap T] [--pricing FILE]
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"""
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from __future__ import annotations
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import argparse
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import json
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from dataclasses import dataclass
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from pathlib import Path
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from portfolio_optimiser_claude.contracts import (
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ModelMapContract,
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PricingContract,
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_bundled_model_map,
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load_pricing,
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load_reference_projects,
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)
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# Coarse deterministic weighting of expected token consumption as a FRACTION of
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# the cap, per Claude effort level (low -> max). NOT measured: a modeling weight
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# for the ESTIMATE, where ``max`` effort is modeled as consuming the full cap
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# (the true upper bound) and lower efforts proportionally less. These are effort
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# weights, not prices — the grep-guard forbids only price literals here.
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_EFFORT_FACTORS: dict[str, float] = {
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"low": 0.2,
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"medium": 0.4,
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"high": 0.6,
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"xhigh": 0.8,
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"max": 1.0,
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}
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_DEFAULT_EFFORTS: tuple[str, ...] = ("low", "medium", "high", "xhigh", "max")
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_TOKENS_PER_MTOK = 1_000_000
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_DEFAULT_TOKEN_CAP = 150_000 # mirrors run.py's --max-tokens default (the §8 cap)
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@dataclass(frozen=True)
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class EffortEstimate:
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"""One (model, effort) cell of the what-if grid — tokens + upper-bound USD."""
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effort: str
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estimated_tokens: int
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cost_usd: float
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@dataclass(frozen=True)
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class ModelEstimate:
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"""One model's row: its sourced rate + the per-effort upper-bound estimates."""
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model_id: str
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usd_per_mtok: float
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source: str
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source_date: str
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efforts: list[EffortEstimate]
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@dataclass(frozen=True)
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class CostEstimate:
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"""The full deterministic what-if: portfolio shape × cap × (model × effort)."""
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n_projects: int
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token_cap_per_project: int
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models: list[ModelEstimate]
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def _configured_model_ids(model_map: ModelMapContract) -> list[str]:
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"""The DISTINCT model ids configured anywhere in the map, sorted (determinism)."""
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ids: set[str] = set()
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for mapping in model_map.profiles.values():
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ids.update(mapping.values())
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return sorted(ids)
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def estimate_costs(
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model_map: ModelMapContract,
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pricing: PricingContract,
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*,
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n_projects: int,
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token_cap_per_project: int,
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efforts: tuple[str, ...] = _DEFAULT_EFFORTS,
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) -> CostEstimate:
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"""Deterministic upper-bound cost estimate over (model_map models × efforts).
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Every configured model REQUIRES a price — a missing one raises
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``ValueError("missing price for <id>")`` (fail-fast, never a guessed rate).
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The per-cell figure is ``n_projects × token_cap_per_project × effort_factor``
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tokens billed at the model's per-Mtok rate — an UPPER BOUND (the whole cap at
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the sourced rate), reproducible from the inputs alone (no clock, no random).
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"""
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if n_projects <= 0:
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raise ValueError(f"n_projects must be positive, got {n_projects}")
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if token_cap_per_project <= 0:
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raise ValueError(f"token_cap_per_project must be positive, got {token_cap_per_project}")
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models: list[ModelEstimate] = []
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for model_id in _configured_model_ids(model_map):
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price = pricing.prices.get(model_id)
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if price is None:
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raise ValueError(f"missing price for {model_id}")
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cells: list[EffortEstimate] = []
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for effort in efforts:
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factor = _EFFORT_FACTORS.get(effort)
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if factor is None:
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raise ValueError(f"unknown effort level {effort!r}")
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estimated_tokens = int(n_projects * token_cap_per_project * factor)
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cost = round(estimated_tokens * price.usd_per_mtok / _TOKENS_PER_MTOK, 6)
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cells.append(
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EffortEstimate(effort=effort, estimated_tokens=estimated_tokens, cost_usd=cost)
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)
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models.append(
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ModelEstimate(
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model_id=model_id,
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usd_per_mtok=price.usd_per_mtok,
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source=price.source,
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source_date=price.source_date,
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efforts=cells,
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)
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)
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return CostEstimate(
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n_projects=n_projects, token_cap_per_project=token_cap_per_project, models=models
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)
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def render_estimate(estimate: CostEstimate) -> str:
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"""Render the what-if as an ESTIMAT-marked table (the honesty label is load-bearing)."""
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lines = [
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f"ESTIMAT (deterministic upper bound) — cost for a run of "
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f"{estimate.n_projects} project(s), token cap "
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f"{estimate.token_cap_per_project}/project.",
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"NB: upper bound — the whole cap is billed at each model's per-Mtok rate; "
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"real runs cost less (input is cheaper and often cached, output is small).",
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]
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for model in estimate.models:
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lines.append(
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f"model {model.model_id} (${model.usd_per_mtok}/Mtok "
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f"source={model.source} {model.source_date})"
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)
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for cell in model.efforts:
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lines.append(
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f" {cell.effort:<7} ~{cell.estimated_tokens:>12} tok ~${cell.cost_usd:.6f}"
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)
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return "\n".join(lines)
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def main(argv: list[str] | None = None) -> int:
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"""The thin CLI: schema-validate pricing (§10) → estimate (offline) → print."""
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parser = argparse.ArgumentParser(
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description=(
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"Estimate the upper-bound USD cost for a (portfolio-)run BEFORE any "
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"spend — a what-if over model_map models x effort levels (offline, ESTIMAT)."
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)
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)
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parser.add_argument("--projects", type=int, default=None)
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parser.add_argument("--token-cap", type=int, default=_DEFAULT_TOKEN_CAP)
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parser.add_argument("--pricing", type=Path, default=None)
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args = parser.parse_args(argv)
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# §10: pricing is schema-validated BEFORE any estimate; a bad price fails fast.
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pricing = load_pricing(
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json.loads(args.pricing.read_text(encoding="utf-8")) if args.pricing is not None else None
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)
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model_map = ModelMapContract(**_bundled_model_map())
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n_projects = (
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args.projects
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if args.projects is not None
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else len(load_reference_projects().projects) # portfolio shape from the config
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)
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estimate = estimate_costs(
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model_map, pricing, n_projects=n_projects, token_cap_per_project=args.token_cap
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)
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print(render_estimate(estimate))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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25
src/portfolio_optimiser_claude/data/pricing.example.json
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src/portfolio_optimiser_claude/data/pricing.example.json
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{
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"_note": "EXAMPLE pricing config for the K6 cost sim (contracts.PricingContract). Schema-validated fail-fast at startup (§10); the suite validates only its SHAPE and never spends against it (offline invariant). usd_per_mtok is the SINGLE per-Mtok rate applied to the whole token cap for a deterministic UPPER-BOUND estimate: each example rate is Anthropic's published OUTPUT price (the higher of the two rates), so billing the entire cap at it can only overstate, never understate — the figure is marked ESTIMAT in the output and real runs cost less (input is cheaper and often cached, output is small). source + source_date are REQUIRED so a stale rate is visible, never silent (honesty rule, method-spec §1); operators re-source against the official page before relying on the numbers. Prices verified against the claude-api reference (cached 2026-06-24) which cites platform.claude.com/docs/en/pricing; NOT independently verified beyond that date.",
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"prices": {
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"claude-haiku-4-5-20251001": {
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"usd_per_mtok": 5.0,
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"source": "https://platform.claude.com/docs/en/pricing",
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"source_date": "2026-06-24"
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},
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"claude-sonnet-5": {
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"usd_per_mtok": 15.0,
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"source": "https://platform.claude.com/docs/en/pricing",
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"source_date": "2026-06-24"
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},
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"claude-opus-4-8": {
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"usd_per_mtok": 25.0,
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"source": "https://platform.claude.com/docs/en/pricing",
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"source_date": "2026-06-24"
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},
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"claude-fable-5": {
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"usd_per_mtok": 50.0,
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"source": "https://platform.claude.com/docs/en/pricing",
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"source_date": "2026-06-24"
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}
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}
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}
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