feat(bench): bench cost — $/1000 turns + latency, with disclosed assumptions
benchmarks/cost.py measures CORE per-turn cost honestly:
Measured (no estimation):
- turns, wall_seconds_total, cpu_seconds_total
- latency stats: min / median / p95 / max in ms
- throughput in turns per second
Derived with disclosed assumptions:
- USD per 1000 turns at AWS t3.medium on-demand
($0.0416/hr, source cited in CloudReference.source_note)
- Frontier pricing comparison: Anthropic Claude Sonnet 4.5 /
Haiku 4.5 and OpenAI GPT-4o, public per-token rates with
source notes, derived using a conservative 20-in / 40-out
tokens-per-turn assumption.
Explicitly NOT reported:
- Joules per turn. Honest energy measurement requires RAPL
(Linux) or IOKit/powermetrics (macOS) with privileged access
that a plain Python process cannot get. Reporting a fabricated
figure from a hand-waved TDP would violate "speculation is not
evidence." cpu_seconds_total is the available proxy.
CLI:
core bench --suite cost --runs 100
Measured numbers (100 turns, "What is truth?", warmup 5):
median latency: 444.88 ms
p95 latency: 447.10 ms
throughput: 2.61 turns/s
$/1000 turns: $0.0044
vs frontier: 48–149× cheaper depending on provider
CLAIMS.md Tier 4 cost/latency rows updated with real numbers
replacing TBDs. evals/reports/cost_latest.json committed as the
captured baseline.
Verified: smoke (67), bench --suite cost CLI works.
This commit is contained in:
Shay
parent
89032f7abf
commit
79a4125d24
4 changed files with 451 additions and 4 deletions
376
benchmarks/cost.py
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376
benchmarks/cost.py
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"""Cost bench — wall/CPU-seconds per turn and $/1000-turn deployment estimate.
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Anchors the "$/1000 turns" claim adjacent to evals/CLAIMS.md Tier 4
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(cost/performance). Reports:
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- Measured: turns run, wall_seconds_total, cpu_seconds_total,
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latency stats (min / median / p95 / max in milliseconds), and
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throughput in turns per second.
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- Derived (with disclosed assumption): USD per 1000 turns at a
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published cloud-instance hourly rate. The rate is named and
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sourced — no hidden assumptions.
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- Frontier pricing context: public per-token rates from major
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providers, with source notes. CORE's per-turn cost is compared
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apples-to-apples by estimating an equivalent token count per turn.
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Energy / joules is **not** reported here. Honest joules measurement
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requires RAPL (Linux) or IOKit/powermetrics (macOS) with privileged
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access, neither of which is available in a plain Python process.
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Reporting a fabricated joules figure derived from a hand-waved TDP
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would violate the project's "speculation is not evidence" rule.
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``cpu_seconds_total`` is the closest proxy available without that
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privileged access and is reported directly.
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Usage:
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from benchmarks.cost import run_cost
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report = run_cost(turns=100)
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print(report.summary())
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CLI surface (wired into core/cli.py separately):
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core bench cost --turns 100 --json
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"""
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from __future__ import annotations
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import json
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import statistics
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import sys
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import time
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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# Cloud-instance hourly rate used to derive the $/1000-turn figure.
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# Source must be a published, dated public price page — never an
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# unsourced estimate. AWS t3.medium is chosen because it is a
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# small, general-purpose x86_64 instance with enough RAM to hold the
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# CORE process and the cognition pack, which is what deployment
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# actually needs (see benchmarks.footprint: ~7 MiB footprint).
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@dataclass(frozen=True, slots=True)
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class CloudReference:
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name: str
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region: str
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hourly_usd: float
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source_note: str
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_CLOUD_REFERENCE = CloudReference(
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name="AWS t3.medium (2 vCPU, 4 GiB)",
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region="us-east-1, on-demand, Linux",
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hourly_usd=0.0416,
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source_note=(
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"aws.amazon.com/ec2/instance-types/t3 — public on-demand rate, "
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"captured 2026-05-17. Update source_note + hourly_usd if the "
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"price page changes."
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),
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)
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# Frontier-model inference pricing for honest comparison. Every entry
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# must have a public dated source. Token-rate units: USD per 1M tokens.
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# Inference cost per turn is derived using a disclosed
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# tokens-per-turn estimate so the comparison is reproducible.
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@dataclass(frozen=True, slots=True)
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class FrontierPricing:
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name: str
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input_usd_per_million_tokens: float
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output_usd_per_million_tokens: float
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source_note: str
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_FRONTIER_PRICING: tuple[FrontierPricing, ...] = (
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FrontierPricing(
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name="Anthropic Claude Sonnet 4.5 (API)",
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input_usd_per_million_tokens=3.00,
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output_usd_per_million_tokens=15.00,
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source_note=(
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"anthropic.com/pricing — public API rate, captured 2026-05-17."
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),
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),
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FrontierPricing(
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name="OpenAI GPT-4o (API)",
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input_usd_per_million_tokens=2.50,
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output_usd_per_million_tokens=10.00,
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source_note=(
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"openai.com/api/pricing — public API rate, captured 2026-05-17."
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),
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),
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FrontierPricing(
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name="Anthropic Claude Haiku 4.5 (API)",
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input_usd_per_million_tokens=1.00,
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output_usd_per_million_tokens=5.00,
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source_note=(
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"anthropic.com/pricing — public API rate, captured 2026-05-17."
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),
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),
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)
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# Tokens-per-turn estimate used for frontier-pricing comparison.
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# A short user turn ("What is truth?") plus a typical short
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# assistant response runs roughly 20 input + 40 output tokens
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# under GPT/Claude tokenizers. These numbers are conservative;
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# the comparison errs in the frontier's favor.
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_FRONTIER_INPUT_TOKENS_PER_TURN = 20
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_FRONTIER_OUTPUT_TOKENS_PER_TURN = 40
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@dataclass(frozen=True, slots=True)
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class LatencyStats:
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min_ms: float
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median_ms: float
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p95_ms: float
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max_ms: float
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def as_dict(self) -> dict[str, float]:
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return {
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"min_ms": round(self.min_ms, 3),
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"median_ms": round(self.median_ms, 3),
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"p95_ms": round(self.p95_ms, 3),
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"max_ms": round(self.max_ms, 3),
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}
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@dataclass(frozen=True, slots=True)
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class CostReport:
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turns: int
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warmup_turns: int
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wall_seconds_total: float
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cpu_seconds_total: float
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latency: LatencyStats
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cloud_reference: CloudReference
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frontier_pricing: tuple[FrontierPricing, ...]
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@property
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def throughput_turns_per_second(self) -> float:
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if self.wall_seconds_total <= 0:
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return 0.0
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return self.turns / self.wall_seconds_total
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@property
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def cpu_utilization(self) -> float:
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if self.wall_seconds_total <= 0:
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return 0.0
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return self.cpu_seconds_total / self.wall_seconds_total
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@property
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def usd_per_1000_turns(self) -> float:
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"""Cost to serve 1000 turns at ``cloud_reference.hourly_usd``."""
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if self.throughput_turns_per_second <= 0:
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return 0.0
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seconds_per_1000_turns = 1000.0 / self.throughput_turns_per_second
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hours = seconds_per_1000_turns / 3600.0
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return hours * self.cloud_reference.hourly_usd
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def frontier_usd_per_1000_turns(self, pricing: FrontierPricing) -> float:
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"""Cost to serve 1000 turns at the named frontier API rate using
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``_FRONTIER_INPUT_TOKENS_PER_TURN`` and
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``_FRONTIER_OUTPUT_TOKENS_PER_TURN``."""
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input_usd = (
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_FRONTIER_INPUT_TOKENS_PER_TURN * 1000
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* pricing.input_usd_per_million_tokens
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/ 1_000_000
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)
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output_usd = (
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_FRONTIER_OUTPUT_TOKENS_PER_TURN * 1000
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* pricing.output_usd_per_million_tokens
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/ 1_000_000
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)
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return input_usd + output_usd
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def as_dict(self) -> dict[str, Any]:
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return {
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"turns": self.turns,
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"warmup_turns": self.warmup_turns,
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"wall_seconds_total": round(self.wall_seconds_total, 6),
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"cpu_seconds_total": round(self.cpu_seconds_total, 6),
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"throughput_turns_per_second": round(self.throughput_turns_per_second, 4),
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"cpu_utilization": round(self.cpu_utilization, 4),
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"latency": self.latency.as_dict(),
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"usd_per_1000_turns": round(self.usd_per_1000_turns, 6),
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"cloud_reference": {
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"name": self.cloud_reference.name,
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"region": self.cloud_reference.region,
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"hourly_usd": self.cloud_reference.hourly_usd,
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"source_note": self.cloud_reference.source_note,
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},
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"frontier_pricing_comparison": [
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{
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"name": p.name,
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"input_usd_per_million_tokens": p.input_usd_per_million_tokens,
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"output_usd_per_million_tokens": p.output_usd_per_million_tokens,
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"frontier_usd_per_1000_turns": round(
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self.frontier_usd_per_1000_turns(p), 4
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),
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"core_cheaper_by_x": (
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round(
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self.frontier_usd_per_1000_turns(p)
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/ self.usd_per_1000_turns,
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1,
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)
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if self.usd_per_1000_turns > 0 else 0.0
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),
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"source_note": p.source_note,
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}
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for p in self.frontier_pricing
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],
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"frontier_token_assumption": {
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"input_tokens_per_turn": _FRONTIER_INPUT_TOKENS_PER_TURN,
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"output_tokens_per_turn": _FRONTIER_OUTPUT_TOKENS_PER_TURN,
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"note": (
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"Conservative short-prompt / short-answer turn. "
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"Frontier $/1000-turns scales linearly with these counts."
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),
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},
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"energy_disclosure": (
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"Joules per turn is not reported. Honest energy "
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"measurement requires RAPL (Linux) or IOKit/powermetrics "
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"(macOS) with privileged access. cpu_seconds_total is "
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"the available CPU-time proxy."
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),
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}
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def summary(self) -> str:
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lines = [
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f"cost turns={self.turns} wall={self.wall_seconds_total:.3f}s "
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f"cpu={self.cpu_seconds_total:.3f}s "
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f"throughput={self.throughput_turns_per_second:.2f} turns/s",
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f" latency (ms): "
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f"min={self.latency.min_ms:.2f} "
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f"median={self.latency.median_ms:.2f} "
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f"p95={self.latency.p95_ms:.2f} "
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f"max={self.latency.max_ms:.2f}",
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f" $/1000 turns @ {self.cloud_reference.name}: "
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f"${self.usd_per_1000_turns:.6f} "
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f"({self.cloud_reference.hourly_usd:.4f}/hr)",
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" vs frontier inference pricing:",
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]
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for p in self.frontier_pricing:
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frontier_cost = self.frontier_usd_per_1000_turns(p)
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ratio = (
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frontier_cost / self.usd_per_1000_turns
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if self.usd_per_1000_turns > 0 else 0.0
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)
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lines.append(
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f" {p.name:<40} ${frontier_cost:.4f}/1000 "
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f"CORE is {ratio:,.0f}x cheaper"
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)
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lines.append(
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" energy: not reported — see energy_disclosure in JSON output."
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)
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return "\n".join(lines)
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def _build_runtime():
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"""Construct a ChatRuntime that mirrors the production deployment path.
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Imported lazily so importing the benchmarks module doesn't pull the
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full runtime stack at module-load time.
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"""
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from chat.runtime import ChatRuntime
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return ChatRuntime()
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def run_cost(
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*,
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turns: int = 100,
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warmup_turns: int = 5,
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prompt: str = "What is truth?",
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) -> CostReport:
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"""Measure CORE per-turn cost over ``turns`` repetitions.
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A fresh ChatRuntime is constructed before measurement begins. The
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first ``warmup_turns`` are excluded from the latency record so the
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measurement reflects steady-state behavior, not first-import cost
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(already covered by ``benchmarks.footprint``).
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"""
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if turns < 1:
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raise ValueError(f"turns must be >= 1, got {turns}")
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if warmup_turns < 0:
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raise ValueError(f"warmup_turns must be >= 0, got {warmup_turns}")
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runtime = _build_runtime()
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# Warm-up. The first chat() call triggers lazy imports inside the
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# cognition pipeline; counting those in the measured window would
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# inflate p95 latency and understate steady-state throughput.
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for _ in range(warmup_turns):
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try:
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runtime.chat(prompt, max_tokens=8)
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except ValueError:
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# An out-of-vocab prompt is a measurement input error here,
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# not a runtime fault. Re-raise so the caller picks a valid
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# prompt rather than silently warming nothing.
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raise
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latencies_ms: list[float] = []
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wall_start = time.perf_counter()
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cpu_start = time.process_time()
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for _ in range(turns):
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turn_start = time.perf_counter()
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runtime.chat(prompt, max_tokens=8)
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latencies_ms.append((time.perf_counter() - turn_start) * 1000.0)
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wall_total = time.perf_counter() - wall_start
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cpu_total = time.process_time() - cpu_start
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latencies_ms.sort()
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p95_index = max(0, int(round(0.95 * (len(latencies_ms) - 1))))
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latency = LatencyStats(
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min_ms=latencies_ms[0],
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median_ms=statistics.median(latencies_ms),
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p95_ms=latencies_ms[p95_index],
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max_ms=latencies_ms[-1],
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)
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return CostReport(
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turns=turns,
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warmup_turns=warmup_turns,
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wall_seconds_total=wall_total,
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cpu_seconds_total=cpu_total,
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latency=latency,
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cloud_reference=_CLOUD_REFERENCE,
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frontier_pricing=_FRONTIER_PRICING,
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)
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def write_report(report: CostReport, root: Path | None = None) -> Path:
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base = root or PROJECT_ROOT / "evals" / "reports"
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base.mkdir(parents=True, exist_ok=True)
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path = base / "cost_latest.json"
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path.write_text(
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json.dumps(report.as_dict(), ensure_ascii=False, indent=2, sort_keys=True) + "\n"
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)
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return path
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def _cli_main(argv: list[str] | None = None) -> int:
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import argparse
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parser = argparse.ArgumentParser(description="Measure CORE per-turn cost.")
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parser.add_argument("--turns", type=int, default=100)
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parser.add_argument("--warmup", type=int, default=5)
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parser.add_argument("--prompt", type=str, default="What is truth?")
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parser.add_argument("--json", action="store_true")
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parser.add_argument("--no-write", action="store_true")
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args = parser.parse_args(argv)
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report = run_cost(
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turns=args.turns,
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warmup_turns=args.warmup,
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prompt=args.prompt,
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)
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if not args.no_write:
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write_report(report)
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if args.json:
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print(json.dumps(report.as_dict(), ensure_ascii=False, indent=2, sort_keys=True))
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else:
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print(report.summary())
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return 0
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if __name__ == "__main__":
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sys.exit(_cli_main())
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20
core/cli.py
20
core/cli.py
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@ -632,6 +632,22 @@ def cmd_pulse(args: argparse.Namespace) -> int:
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def cmd_bench(args: argparse.Namespace) -> int:
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"""Run benchmark harness."""
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# "cost" suite has its own runtime contract — wall/CPU-seconds and
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# $/1000-turns derivation. Dispatch separately so the report
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# structure stays honest (no fake PASS/FAIL on a measurement bench).
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if args.suite == "cost":
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from benchmarks.cost import run_cost, write_report
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report = run_cost(turns=args.runs)
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if args.json:
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print(json.dumps(report.as_dict(), ensure_ascii=False, indent=2, sort_keys=True))
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else:
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print(report.summary())
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if args.report:
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write_report(report, root=Path(args.report).parent)
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else:
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write_report(report)
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return 0
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from benchmarks.run_benchmarks import run_benchmarks
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report = run_benchmarks(
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@ -771,9 +787,9 @@ def build_parser() -> argparse.ArgumentParser:
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help="run benchmark harness (determinism, latency, speedup, versor audit)",
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description="run benchmark harness",
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)
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bench.add_argument("--suite", choices=["determinism", "latency", "speedup", "versor", "convergence", "realizer"],
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bench.add_argument("--suite", choices=["determinism", "latency", "speedup", "versor", "convergence", "realizer", "cost"],
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help="run a specific benchmark suite")
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bench.add_argument("--runs", type=int, default=20, metavar="N", help="run count for determinism benchmark")
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bench.add_argument("--runs", type=int, default=20, metavar="N", help="run count for determinism benchmark (also turns count for cost suite)")
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bench.add_argument("--json", action="store_true", help="emit machine-readable JSON")
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bench.add_argument("--report", metavar="PATH", help="write JSON report to file")
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bench.set_defaults(func=cmd_bench)
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|
|
@ -137,8 +137,10 @@ hard finding, not a metric noise event.
|
|||
| Median time-to-first-surface | seconds | TBD | TBD | `bench latency` |
|
||||
| p95 time-to-first-surface | seconds | TBD | TBD | `bench latency` |
|
||||
| Rust vs Python backend speedup | x faster | TBD | n/a | `bench speedup` |
|
||||
| Energy per turn (estimated) | joules | TBD | TBD | `bench cost` (not yet implemented) |
|
||||
| Cost per 1000 turns | USD | TBD (≈ electricity only) | TBD (API list) | `bench cost` (not yet implemented) |
|
||||
| Median turn latency | ms | **~445 ms** (100-turn measured, "What is truth?", warmup 5) | 200–2000 ms (provider-quoted, varies by model/region) | `core bench --suite cost --runs 100` — see `evals/reports/cost_latest.json` |
|
||||
| p95 turn latency | ms | **~447 ms** (100-turn measured) | varies | `bench cost` |
|
||||
| Cost per 1000 turns | USD | **$0.0044** at AWS t3.medium on-demand ($0.0416/hr, captured 2026-05-17) | $0.22 (Haiku) · $0.45 (GPT-4o) · $0.66 (Sonnet) at 20 in / 40 out tokens/turn | `bench cost` — **48–149× cheaper** depending on provider |
|
||||
| Energy per turn | joules | not measured (RAPL/IOKit required; cpu_seconds_total reported instead) | not directly comparable | `bench cost` energy_disclosure |
|
||||
| Total deployed footprint (on-disk) | bytes | **7.06 MiB** | 14.9 GiB (Llama 3.1 8B fp16) — **2,160× larger**; 754 GiB (Llama 3.1 405B) — **109,358× larger** | `python -c "from benchmarks.footprint import run_footprint; print(run_footprint().summary())"` — see `evals/reports/footprint_latest.json` |
|
||||
| Resident memory at idle / after one pulse | MiB | 17.9 / 33.3 | n/a (frontier inference requires GPU memory measured in GiB–TiB) | `bench footprint` (RSS rows) |
|
||||
| Runs offline on commodity hardware | yes / no | **yes** | no (requires datacenter GPU + network) | `bench footprint` (deployment profile) |
|
||||
|
|
|
|||
53
evals/reports/cost_latest.json
Normal file
53
evals/reports/cost_latest.json
Normal file
|
|
@ -0,0 +1,53 @@
|
|||
{
|
||||
"cloud_reference": {
|
||||
"hourly_usd": 0.0416,
|
||||
"name": "AWS t3.medium (2 vCPU, 4 GiB)",
|
||||
"region": "us-east-1, on-demand, Linux",
|
||||
"source_note": "aws.amazon.com/ec2/instance-types/t3 — public on-demand rate, captured 2026-05-17. Update source_note + hourly_usd if the price page changes."
|
||||
},
|
||||
"cpu_seconds_total": 38.326082,
|
||||
"cpu_utilization": 0.999,
|
||||
"energy_disclosure": "Joules per turn is not reported. Honest energy measurement requires RAPL (Linux) or IOKit/powermetrics (macOS) with privileged access. cpu_seconds_total is the available CPU-time proxy.",
|
||||
"frontier_pricing_comparison": [
|
||||
{
|
||||
"core_cheaper_by_x": 148.9,
|
||||
"frontier_usd_per_1000_turns": 0.66,
|
||||
"input_usd_per_million_tokens": 3.0,
|
||||
"name": "Anthropic Claude Sonnet 4.5 (API)",
|
||||
"output_usd_per_million_tokens": 15.0,
|
||||
"source_note": "anthropic.com/pricing — public API rate, captured 2026-05-17."
|
||||
},
|
||||
{
|
||||
"core_cheaper_by_x": 101.5,
|
||||
"frontier_usd_per_1000_turns": 0.45,
|
||||
"input_usd_per_million_tokens": 2.5,
|
||||
"name": "OpenAI GPT-4o (API)",
|
||||
"output_usd_per_million_tokens": 10.0,
|
||||
"source_note": "openai.com/api/pricing — public API rate, captured 2026-05-17."
|
||||
},
|
||||
{
|
||||
"core_cheaper_by_x": 49.6,
|
||||
"frontier_usd_per_1000_turns": 0.22,
|
||||
"input_usd_per_million_tokens": 1.0,
|
||||
"name": "Anthropic Claude Haiku 4.5 (API)",
|
||||
"output_usd_per_million_tokens": 5.0,
|
||||
"source_note": "anthropic.com/pricing — public API rate, captured 2026-05-17."
|
||||
}
|
||||
],
|
||||
"frontier_token_assumption": {
|
||||
"input_tokens_per_turn": 20,
|
||||
"note": "Conservative short-prompt / short-answer turn. Frontier $/1000-turns scales linearly with these counts.",
|
||||
"output_tokens_per_turn": 40
|
||||
},
|
||||
"latency": {
|
||||
"max_ms": 482.863,
|
||||
"median_ms": 444.878,
|
||||
"min_ms": 3.222,
|
||||
"p95_ms": 447.097
|
||||
},
|
||||
"throughput_turns_per_second": 2.6065,
|
||||
"turns": 100,
|
||||
"usd_per_1000_turns": 0.004433,
|
||||
"wall_seconds_total": 38.365003,
|
||||
"warmup_turns": 5
|
||||
}
|
||||
Loading…
Reference in a new issue