MCP server: One MCP server.

LLM Latency Tracker

@llmlatencyRun by its publisher: served from their own domain, namespace checked by the registry.MCP server · External

Measured latency, time to first token and uptime for ~45 AI inference APIs, by region.

Freeno charge per call

One address for any MCP client. No sign-in needed on this server.

What it does

2 read

Grouped by tool name. At most: only reads or looks up information.

What it costs

Free

Odel charges nothing per call. The publisher may charge for its own service.

Tools

2 · grouped by tool name · checked 12 Oct 2026

Read · 2Look things up

  • get_ai_api_latency
  • get_model_deprecations
Try it in the Inspector
README2 min read

Measure AI API latency and uptime across regions, and publish the results as open data.

🌐 Live rankings · JSON API · MCP endpoint

⚡ What it does

  • 🌍 Regional measurements — compare provider response times and availability from distributed probe nodes.
  • ⏱️ Two probe types — network time to first byte (TTFB) and optional inference time to first token (TTFT).
  • 📈 Open dataset — daily rankings, historical snapshots and regional p50 / p95 statistics.
  • 🤖 Agent access — JSON API, MCP, Markdown pages and an llms.txt index.
  • 🗓️ Model lifecycle — a deprecation calendar with links to provider announcements.

🔧 Built for measurement

  • Python standard-library core for edge probes; provider keys are needed for inference probes.
  • SQLite storage and regional aggregation, with remote nodes shipping measurements to a central node.
  • Static publishing for the website and JSON datasets.
  • CI checks for tests and linting; published snapshots include their measurement date.

Reading the results: edge TTFB measures network/API responsiveness, not model generation speed. Compare it separately from inference TTFT; results depend on region, probe type and time window.

🚀 Quick start

Use the public data without installing anything:

bash
curl https://llmlatency.dev/api/rankings.json

Or run edge probes locally with Python 3.12+:

bash
git clone https://github.com/mazamaka/llm-latency-tracker.git
cd llm-latency-tracker
REGION=local python3 run.py
python3 aggregate.py --region local

For a local stdio MCP server over the published API (stdlib only, no keys):

bash
python3 mcp_server.py

Tools: get_ai_api_latency (provider leaderboard per region), get_provider_latency (one provider across regions), get_model_deprecations (model shutdown calendar), get_coding_agent_health (Claude Code / Codex speed and accuracy drift). All read-only.

Inference probes, API access & site generation → · Deployment →

🧪 Contributing

Add a provider, bring a new probe region, or improve the measurement code. See CONTRIBUTING.md for setup and checks.


Python · SQLite · JSON API · MCP · Cloudflare Pages

Code: MIT · Data: CC-BY-4.0, with attribution to llmlatency.dev.

📈 Latest daily snapshot

Daily snapshot — 2026-10-10

Measured latency across 46 AI inference providers in 4 regions. Method: distributed edge (DNS→TCP→TLS→TTFB) + inference (TTFT) probes, last 24h. License: CC-BY-4.0.

RegionFastest provider (p50)p50p95Uptime
Asia (Tokyo)fireworks16 ms60 ms100%
Europe (Germany)fireworks98 ms199 ms100%
South America (São Paulo)openrouter62 ms104 ms100%
US (Central)fireworks28 ms77 ms100%

Snapshot generated 2026-10-10T07:22:13Z — this table is regenerated daily.

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