benchmark-models

name	benchmark-models
version	1.0.0
description	Cross-model benchmark. Runs one prompt or skill through Claude, GPT (Codex CLI), and Gemini side by side and compares latency, tokens, cost, tool calls, and optionally output quality via an Anthropic-API judge. Answers "which model is actually best for this skill?" with data. Use when you say "benchmark models", "compare models", "which model is best for X", "cross-model comparison", or "model shootout". Do NOT use to measure web page performance.
license	MIT
model	claude-opus-4-8
metadata	{"domains":["benchmarking","model-evaluation","cost-analysis"]}
allowed-tools	["Bash","Read","AskUserQuestion"]

benchmark-models

Run the same prompt across providers and compare them on data, not vibes. The deterministic work lives in scripts/model_benchmark.py (stdlib-only, ported from gstack's bun benchmark; no SDKs, no gstack runtime). This skill is the interactive wrapper: pick a prompt, preview auth with a dry-run, confirm providers, run, interpret, optionally save.

Providers wrap existing CLIs and their own auth (no API keys needed except the optional judge):

• claude via claude -p --output-format json
• gpt via codex exec ... -s read-only --json
• gemini via gemini -p ... --output-format stream-json

Triggers

Invoke this skill when the user says any of:

• benchmark models
• compare models
• model shootout
• which model is best
• cross-model comparison

Do NOT invoke for web page performance (that is a different concern).

Preconditions

• At least one provider CLI installed and authenticated. The Step 1 dry-run reports exactly which are ready; if none are, the skill stops.
• The optional quality judge needs ANTHROPIC_API_KEY (adds ~$0.05/run).

Process

Resolve the driver first. In a repository checkout, use BENCH="$(git rev-parse --show-toplevel)/.claude/skills/benchmark-models/scripts/model_benchmark.py". From an installed skill copy, use the local skill path: BENCH="$PWD/scripts/model_benchmark.py" when your shell is in the benchmark-models skill directory.

Step 1: Pick a prompt and preview auth (dry-run)

Decide the prompt with AskUserQuestion:

• A) Benchmark a skill: pass that skill's SKILL.md path as the prompt. To list candidates, search the installed skills dir, not just . (skills usually live under ~/.claude/skills/ or ~/.copilot/skills/, not the project root).
• B) Inline prompt: pass --prompt "<text>".
• C) A prompt file on disk: pass its path (verify it exists first).

Then preview availability without spending anything:

python3 "$BENCH" --prompt "dry-run" --models claude,gpt,gemini --dry-run

The "Adapter availability" block shows OK or NOT READY (with a remediation hint) per provider. If ALL are NOT READY, STOP: nothing can run. Suggest claude, codex login, or gemini login / export GOOGLE_API_KEY.

Step 2: Confirm providers and judge

From the dry-run, AskUserQuestion which providers to include (default: all authed). Unauthed providers are skipped cleanly and never abort the batch.

If ANTHROPIC_API_KEY is set, AskUserQuestion whether to enable --judge (quality scoring, ~$0.05). Never auto-enable it; it costs real money. If the key is absent, omit --judge.

Step 3: Run

python3 "$BENCH" <prompt-spec> --models <picked> [--judge] --output table

<prompt-spec> is --prompt "<text>" (B) or a file path (A/C). Stream the output; each provider runs the full prompt, so expect 30s-5min.

Step 4: Interpret and optionally save

Summarize: fastest (latency), cheapest (cost), highest quality (if judged), and a best-overall call that names the tradeoff. Call out any provider that errored, with its remediation path. Offer to save a JSON baseline so future runs can be diffed for regressions:

python3 "$BENCH" <prompt-spec> --models <picked> [--judge] --output json > benchmark-$(date +%Y%m%d)-<slug>.json

Scripts

model_benchmark.py

Runs the benchmark. Pure stdlib; safe to run directly.

python3 scripts/model_benchmark.py <prompt-file | --prompt "text"> \
  [--models claude,gpt,gemini] [--judge] [--dry-run] \
  [--output table|json|markdown] [--workdir PATH] [--timeout-ms N] [--skip-unavailable]

Exit codes (ADR-035): 0 success, 1 logic/runtime error, 2 config or usage error, 3 external dependency failure, 4 authentication or authorization failure.

Notes:

• --dry-run resolves auth and prints availability without sending any prompt.
• The judge posts to the Anthropic Messages API via stdlib http.client (no SDK) and fails soft: a judge error warns and leaves quality blank, it never aborts the run.
• Do not place secrets in prompts. The gpt and gemini CLIs receive the prompt through command arguments, which some systems expose through process listings or crash reports.
• Safety asymmetry: the gpt/codex adapter runs -s read-only; the gemini adapter passes --yolo (auto-approve), which is NOT sandboxed. Benchmark in a disposable --workdir if the prompt could trigger file writes.
• The driver only runs providers concurrently when every selected provider is read-only GPT/Codex. Claude and Gemini run sequentially because they can mutate the workdir.
• Pricing lives in the PRICING table at the top of the script. An unpriced model costs 0.0 with a stderr warning; add a row rather than guess.

Anti-Patterns

Avoid	Why	Instead
Running the benchmark before the dry-run	Spends API calls on unauthed providers that will just error	Always Step 1 dry-run first; only run authed providers
Auto-enabling --judge	It adds real per-run cost the user did not approve	Opt-in only, after the Step 2 question
Benchmarking in a live repo with a write-prone prompt	The gemini adapter is not sandboxed (--yolo)	Use a disposable --workdir
Guessing a price for an unlisted model	Produces a wrong cost number that looks authoritative	Add a row to the PRICING table; unlisted stays 0.0 with a warning
Listing skills to benchmark via find . only	Installed skills live under ~/.claude / ~/.copilot, not the project root	Search the installed skills dir

Verification

The skill run is complete when:

• Step 1 dry-run ran and its availability block was shown to the user.
• If zero providers were authed, the run STOPPED (no benchmark attempted).
• The benchmark ran only the user-confirmed providers.
• --judge was included only after explicit user opt-in.
• Results name the fastest, cheapest, and (if judged) highest-quality model, with errors and their remediation surfaced.
• python3 -m pytest .claude/skills/benchmark-models/tests passes (the driver carries unit tests at 100% coverage).