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LLM Latency Tracker

Registre officiel IA et agents

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

Code source Site web Documentation

Que pouvez-vous faire avec LLM Latency Tracker ?

  • 🌍 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.

Installation

Ajoutez ceci à la configuration MCP de votre client (Claude Desktop : claude_desktop_config.json ; Cursor : .cursor/mcp.json ; VS Code et les autres utilisent la même forme « mcpServers »).

Remote (streamable-http)
Claude Code 
claude mcp add --transport http llm-latency-tracker 'https://llmlatency.dev/mcp'
Configuration JSON (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "llm-latency-tracker": {
            "type": "http",
            "url": "https://llmlatency.dev/mcp"
        }
    }
}

Documentation

Tirée du README du projet sur GitHub, actualisée chaque semaine. Les liens ouvrent GitHub.

Sommaire
  1. 📊 LLM Latency Tracker
  2. ⚡ What it does
  3. 🔧 Built for measurement
  4. 🚀 Quick start
  5. 🧪 Contributing

📊 LLM Latency Tracker

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:

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

Or run edge probes locally with Python 3.12+:

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):

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 — 2026-10-26

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)fireworks24 ms99 ms100%
Europe (Germany)openrouter98 ms201 ms100%
South America (São Paulo)writer56 ms85 ms100%
US (Central)fireworks28 ms77 ms100%

Snapshot generated 2026-10-26T07:24:17Z — this table is regenerated daily.

Questions fréquentes

Qu'est-ce que LLM Latency Tracker ?

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

Comment installer LLM Latency Tracker ?

Il fonctionne comme serveur distant. Dans Claude Code : claude mcp add --transport http llm-latency-tracker 'https://llmlatency.dev/mcp'. Pour les autres clients, collez la configuration JSON de cette page dans leurs réglages MCP.

Faut-il une clé API pour LLM Latency Tracker ?

Son entrée au registre n'indique aucune clé ni secret obligatoire. Certaines fonctions peuvent tout de même demander un compte auprès du service auquel il se connecte.

LLM Latency Tracker est-il open source ?

Oui. Son code est sur GitHub sous licence MIT.

Qui maintient LLM Latency Tracker ?

Il est publié par mazamaka. AI.tn le référence sans le maintenir ni le recommander.

Issu du registre MCP officiel. Être listé n'est pas une recommandation : vérifiez le code et les permissions d'un serveur avant de lui donner accès à vos données.