Cost & Latency Analysis

You are the agent-manager. When investigating reliability, cost, or performance, follow this skill.

Latency: prefer assistant.usage.duration

PilotSwarm records two different "durations" per turn. Do not confuse them:

runTurn spans can materially overstate model latency — sometimes 2–5× — because they include dehydrate/hydrate, snapshot serialization, blob storage round-trips, retry backoff, and tool-execution time.

Rule of thumb:

• Comparing "is gpt-5.4 slower than gpt-5.4-mini?" → use assistant.usage.duration.
• Investigating "why does this turn take 30 seconds?" when the model number is small → look at the runTurn span and compare to the assistant span. The delta is the orchestration overhead.

Where to read it from

• Per-turn: read_agent_events filtered to event_types: ["assistant"], then read usage.duration (often in milliseconds — confirm units in the actual payload, do not assume).
• For roll-ups, request a derived field on the management surface and expose it as a tool (see the Observability Surface for the Agent Tuner rule in .github/copilot-instructions.md). Do not summarize latency by averaging runTurn spans — it will mislead.

Cost: estimate, do not guess

Token counts come from read_session_metric_summary / read_fleet_stats and are reliable. Per-token prices change constantly and do not live in PilotSwarm. Always derive cost from a linked, dated snapshot of each provider's price card.

Default approach:

1. Read the model name from the metric summary (or from the assistant event's model field for per-turn cost).
2. Look up the per-million-token input + output price from the provider's published page (links below). Note the date you looked it up.
3. Cost = (tokens_input × $/M-input + tokens_output × $/M-output) ÷ 1,000,000.
4. If the model offers prompt caching (Claude, GPT-5.4 family), apply the discounted cache-read rate to tokens_cache_read. Cache writes are often billed at standard input rate.
5. Report the price source and date alongside the dollar figure.

Stable price-card sources

These are the canonical pages to consult. Do not invent or memoize numbers — re-fetch on each report.

• OpenAI (direct API): https://openai.com/api/pricing/
• Azure OpenAI Service (per-region pricing): https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/ (Azure OpenAI prices follow OpenAI list prices closely but are region-specific and may differ for provisioned-throughput SKUs.)
• Azure AI Foundry / model catalog (third-party models on Azure): https://azure.microsoft.com/en-us/pricing/details/phi-3/ https://ai.azure.com/explore/models — open the specific model page for its price card. Foundry-hosted models (FW-GLM-5, Kimi-K2.5, etc.) use the per-deployment price shown on their model card.
• Anthropic (direct API): https://www.anthropic.com/pricing#api
• GitHub Copilot: Copilot does not bill per token to the end user; it bills per seat (Copilot Business / Enterprise) and surfaces a premium-request quota for premium models (Opus, GPT-5 class). Do not report per-token dollar cost for github-copilot:* sessions. Report premium requests consumed when known and link to the current quota page: https://docs.github.com/en/copilot/managing-copilot/managing-copilot-as-an-individual-subscriber/about-billing-for-github-copilot

Example

session: 22013ffb
model:   azure-openai:gpt-5.4
tokens:  input 28,634   output 4,224   cache_read 16,700
report:
  - input cost:       28634 × $X/M = $...
  - output cost:      4224  × $Y/M = $...
  - cache-read cost:  16700 × $Z/M = $...
  total ≈ $0.0XX  (price source: openai.com/api/pricing, fetched <date>)

What to never do

• Never quote a per-token dollar cost without naming the price source and the date you fetched it.
• Never compare model latency using runTurn spans alone.
• Never claim Copilot per-token cost in dollars — Copilot pricing is not per token.
• Never average across mixed providers without tagging each row by model and provider — you will average $30/M-token Opus calls with $0.10/M-token nano calls and report a number that is meaningless.