LLM Speed Bench

Know whether a model change is safe before it reaches production. LLM Speed Bench runs a small local preflight across providers and compares validated output, response speed, tokens, and estimated cost.

It is a local preflight tool—not a hosted evaluation platform, tracing system, RAG framework, or public leaderboard. Its results are evidence for your account, network, prompts, and validation rules.

Try it in 60 seconds

Create and run a deterministic local benchmark—no API key or network request:

llm-bench --init
llm-bench benchmark.json --no-save

From a source checkout:

python3 -m llm_bench.cli --init
python3 -m llm_bench.cli benchmark.json --no-save

--init never overwrites an existing config. It creates a mock benchmark so you can see the report and exit behavior before making a paid request.

Use it when

• You are switching models or providers.
• A provider publishes a new model or changes a latest alias.
• You need to compare your own prompt's validity, latency, and cost.
• You want local result artifacts instead of a hosted dashboard.

It measures deterministic test validity, end-to-end latency (p50/p95), time to first token, throughput when usage is available, token totals, and estimated cost. Result files retain request metadata and per-request observations for reproducibility.

First live run

Python 3.10+ is required; there are no runtime dependencies.

cp benchmark.example.json benchmark.json
cp .env.example .env.production
# Edit benchmark.json and add only the provider keys you use.
python3 -m llm_bench.cli benchmark.json --dry-run
python3 -m llm_bench.cli benchmark.json

The CLI reads .env.production beside the config without overriding environment variables already set by your shell. Use --no-env-file or --env-file PATH when needed. Runs print a terminal report and, unless --no-save is used, write JSON and Markdown results under results/.

Install the command globally in a virtual environment if preferred:

python3 -m pip install llm-speed-bench
llm-bench --init

Run --doctor and --dry-run before the final command. They make no generation requests; the final command is the paid work.

Change a model safely

This is the core workflow. Put your approved model and candidate model in one config, then run the small response-and-contract preflight:

llm-bench benchmark.json --migration-check --dry-run
llm-bench benchmark.json --migration-check

It sends three short representative cases to each selected model, once each. It answers: did the API work, did each response meet the basic contract, and how quickly did the provider start and finish responding? It is a cheap compatibility check, not a statistical performance conclusion.

When that passes, run the task-specific checks that match your application—for example exact-routing-check or structured-output-check—before approving a switch. Use custom contract tests to express the outputs your own feature must preserve.

Choose your path

I am new and want to see the tool safely. Start with the Getting started guide. It uses a no-key local mock before any provider request.

I know the current and candidate model IDs. Edit one config, run the migration check, then add a custom contract test for the output your feature must preserve. You do not need the catalogue.

I want to find and review provider releases. Use the local catalogue lifecycle below. It keeps broad provider metadata separate from the small set of models you approve for ongoing testing.

llm-bench catalog init
llm-bench catalog refresh benchmarks/watch.json
# If a model is shown as “Needs one probe”, review and confirm a minimal request:
llm-bench catalog probe benchmarks/watch.json
llm-bench catalog prepare benchmarks/watch.json \
  --against benchmarks/approved.json --output benchmarks/candidates.json
llm-bench benchmarks/candidates.json --interactive \
  --approve-to benchmarks/approved.json

Refresh reads metadata only. A probe sends one minimal request only for text candidates you select and confirm. The interactive benchmark then lets you approve passing models explicitly. Follow the complete catalogue tutorial for the decision points.

I am automating an established contract. Use CI and JSON output, with a saved baseline and --ci where a regression should fail the pipeline.

Useful commands once you know your path

# Inspect configuration, credentials, and model selection without generation.
llm-bench benchmark.json --doctor
llm-bench benchmark.json --dry-run
llm-bench benchmark.json --pricing-check

# Run a reduced live benchmark.
llm-bench benchmark.json --smoke

# Run a single ad hoc prompt.
llm-bench --quick "Return only valid JSON with a status field." \
  --models openai:gpt-5.4-mini

For advanced discovery, interactive runs, CI, baselines, replay, and stop modes, see workflows. For models, environment files, custom prompts, and provider-specific options, see configuration.

What makes a comparison useful

• Keep prompts, system instructions, temperature, and output limits fixed.
• Validate outputs: a fast malformed response is a failed result.
• Run from the same host; network distance and provider load affect latency.
• Treat single-user latency and load testing as separate experiments.
• Prefer dated model IDs over moving aliases.

The CLI distinguishes API FAIL (transport, credentials, provider, or request failure) from API OK / TEST FAIL (a response that fails your validator). Recommendations only consider models that pass every selected test.

Documentation

• Getting started — safe demo, first paid run, and choosing the right workflow.
• Workflows — discovery, smoke mode, CI, replay, matrix, baselines, and request safety.
• Configuration — providers, custom prompts, presets, aliases, and environment overlays.
• Custom contract tests — copyable JSON extraction, exact-routing, and content-rule migration tests.
• CLI reference — every command-line option, default, and incompatibility.
• Interactive mode — selection, paid-run preview, and live progress.
• CI and JSON output — machine-readable output and exit-code gates.
• Model watch and approval — discover, compare, and deliberately promote, re-test, and retire provider models.
• Troubleshooting — installation, credentials, catalogue, and benchmark-result recovery.
• Tests, pricing, and safety — built-in tests, validators, pricing confidence, retries, and sensitive data.
• Contributing — development setup and the TDD workflow.
• Security — reporting vulnerabilities.

Contributing and license

Contributions are welcome; see CONTRIBUTING.md. Released under the MIT License.