AI Gateway Models & Pricing
www.computesdk.com- Category
- Developer Tools
- Rank
- No. 1237Tools index
Previous survey · No. 1220 ·
- Type
- TOOL
- Use case
- Data, Retrieval & Knowledge
- GitHub
- 131 stars
- Latest release
- @benchsdk/runner@0.8.0
- Date
About
ComputeSDK's AI Gateway Models & Pricing page pulls the public model-list endpoint of each AI gateway, including OpenRouter, Vercel AI Gateway, Cloudflare AI Gateway and others, and unifies them into one matrix showing which gateways route a given model and what each charges per million tokens. It indexed 1,429 models across 12 gateways as of its last run and refreshes the comparison weekly.
What it does
A catalogue crawler inside ComputeSDK's open benchmark suite. It sends no prompts at all. Instead it asks every listed LLM router for its published list of what it serves, then normalizes the answers into one JSON table, adding provider and routing choices whenever a router's reply includes them. Those results feed the live pages on ComputeSDK's site, so you can look up one LLM and see who carries it.
Why it's ranked here
The data trail is open. The suite is MIT licensed, every result is committed to the repository as JSON, and runs happen on GitHub Actions rather than on someone's laptop. Each catalogue run writes both a dated snapshot and a rolling latest file, so a reader can diff what changed between runs. It also sits beside a careful latency benchmark for the same routers that separates connection setup from time to first token, which puts availability and speed for one set of routers in a single public repository.
What's good
Targeted reruns cannot overwrite the main dataset: a run limited to one provider writes to its own suffixed files and leaves the full-roster results intact. The catalogue pass sends no chat requests, so reproducing it burns no tokens. The companion latency suite documents its setup down to the prompt, a 200-token output cap, temperature 0 and a 45-second timeout, and it records receipt headers such as request IDs so a disputed number can be traced back to the provider's own logs.
Tradeoffs
Everything is measured from one place. The latency runs execute on Namespace runners in Northern Virginia, and the methodology says so, so routers whose edges sit far from there may look slower than they would for you. The project's own docs warn that results for different target LLMs are not comparable, even for the same router. Neon is both a listed sponsor and a benchmarked router, and Namespace sponsors the runs while also appearing as a benchmarked sandbox provider. The README states sponsors cannot influence results; that is a policy, not something the code enforces.
How to use it well
Treat it as a starting map when picking a router for a specific LLM: confirm the LLM is listed, then check that router's own pricing page before committing, since the table only reflects what each catalogue endpoint publishes. For speed, read the cold numbers if you run serverless functions or CLIs that open a fresh connection per request, and the warm numbers if you keep a connection pool. To check a claim yourself, clone the repository, add your own API keys and run the catalogue job for a single provider. It does not measure answer quality.
Technical notes+
benchmarks/ai-gateway/ai-gateway-model-index.bench.ts defines the job with defineBenchmarkConfig at iterations: 1 over the modelIndexProviders roster, with runModelIndexTask as the task; its onComplete hook collects each participant's record data and writes a payload (version 1.0, timestamp, node/platform/arch) to a dated file and a latest file, appending a provider suffix when --provider is passed. DAILY_BENCH_SLUG and DAILY_BENCH_NAME env vars rename the benchmark slug. benchmarks/ai-gateway/benchmark.ts holds the latency output path: per-iteration dnsMs, tcpMs, tlsMs, ttfbMs, ttftMs and coldE2eMs rounded to two decimals, median/p95/p99 summaries, and a fixed timeoutMs of 45000. AI_GATEWAYS.md documents four wire formats keyed by AIGatewayProviderConfig.wireFormat and per-connection ALPN negotiation of h2 or http/1.1. METHODOLOGY.md defines the composite as timingScore multiplied by successRate against a fixed 10-second ceiling, with 5% trimmed from each tail. package.json is a private ESM workspace package whose scripts run through tsx; LICENSE is MIT.
Observed
- License
- MIT
- Language
- TypeScript, run with tsx in a pnpm workspace
- Interface
- bench run CLI from the benchsdk runner, with a --provider filter
- Measurement method
- Queries each router's model-list endpoint; no chat probes are sent
- Output format
- Normalized JSON committed to the repository, written as a dated file plus a latest file
- Run environment
- GitHub Actions on Namespace runners, single vantage point in Northern Virginia
- Sponsor overlap
- Neon is a listed sponsor and a benchmarked router
Read from README.md, package.json, METHODOLOGY.md, AI_GATEWAYS.md, benchmarks/ai-gateway/benchmark.ts, benchmarks/ai-gateway/ai-gateway-model-index.bench.ts, pricing.json, LICENSE.
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