LLM Latency Tracker MCP server

Add this server

The publisher's own URL. Not a PlugMCP endpoint.

Server URL

https://llmlatency.dev/mcp

Provider

Add in Claude

Claude opens with the name and URL filled in. You still confirm it there.

Claude Code

claude mcp add --transport http dev.llmlatency-llm-latency-tracker https://llmlatency.dev/mcp

Run in a terminal.

Claude Desktop

{
  "mcpServers": {
    "dev.llmlatency-llm-latency-tracker": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://llmlatency.dev/mcp"
      ]
    }
  }
}

claude_desktop_config.json

Step-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Questions

How do I install LLM Latency Tracker?

Copy the http config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does LLM Latency Tracker need an API key?

No required environment variable is listed on this record.

What transport does LLM Latency Tracker use?

LLM Latency Tracker is reached over HTTP.

When was LLM Latency Tracker last checked?

Last checked 2026-10-16. Measured latency, time to first token and uptime for ~45 AI inference APIs, by region.

📊 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.

Daily snapshot — 2026-10-21

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.

Region	Fastest provider (p50)	p50	p95	Uptime
Asia (Tokyo)	fireworks	18 ms	58 ms	100%
Europe (Germany)	fireworks	97 ms	199 ms	100%
South America (São Paulo)	openrouter	62 ms	100 ms	100%
US (Central)	fireworks	29 ms	75 ms	100%

Full dataset: data/rankings/2026-10-21.json (latest)
Citable archive (DOI): 10.5281/zenodo.23388429 — daily aggregates, CC-BY-4.0
Hugging Face dataset: https://huggingface.co/datasets/llmlatency/llm-latency-tracker
Kaggle dataset: https://www.kaggle.com/datasets/llmlatency/llm-latency-tracker
Archived in Software Heritage: swh:1:snp:2778cbabd72a70a629ee35fbd5ac536d1ccb7a9a
Python client: https://pypi.org/project/llmlatency/
Live rankings and methodology: https://llmlatency.dev
Machine-readable API: https://llmlatency.dev/api/rankings.json
Model deprecation calendar: https://llmlatency.dev/deprecations

Snapshot generated 2026-10-21T07:23:54Z — this table is regenerated daily.

📊 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

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.

Daily snapshot — 2026-10-21

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.

Region	Fastest provider (p50)	p50	p95	Uptime
Asia (Tokyo)	fireworks	18 ms	58 ms	100%
Europe (Germany)	fireworks	97 ms	199 ms	100%
South America (São Paulo)	openrouter	62 ms	100 ms	100%
US (Central)	fireworks	29 ms	75 ms	100%

Full dataset: data/rankings/2026-10-21.json (latest)
Citable archive (DOI): 10.5281/zenodo.23388429 — daily aggregates, CC-BY-4.0
Hugging Face dataset: https://huggingface.co/datasets/llmlatency/llm-latency-tracker
Kaggle dataset: https://www.kaggle.com/datasets/llmlatency/llm-latency-tracker
Archived in Software Heritage: swh:1:snp:2778cbabd72a70a629ee35fbd5ac536d1ccb7a9a
Python client: https://pypi.org/project/llmlatency/
Live rankings and methodology: https://llmlatency.dev
Machine-readable API: https://llmlatency.dev/api/rankings.json
Model deprecation calendar: https://llmlatency.dev/deprecations

Snapshot generated 2026-10-21T07:23:54Z — this table is regenerated daily.

LLM Latency Tracker

Add this server

Host a copy with our keys and logs.

Related MCP

Related skills

Questions

📊 LLM Latency Tracker

⚡ What it does

🔧 Built for measurement

🚀 Quick start

🧪 Contributing

Daily snapshot — 2026-10-21

LLM Latency Tracker

Add this server

Host a copy with our keys and logs.

Related MCP

Related skills

Questions

📊 LLM Latency Tracker

⚡ What it does

🔧 Built for measurement

🚀 Quick start

🧪 Contributing

Daily snapshot — 2026-10-21