📊 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.

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Python · SQLite · JSON API · MCP · Cloudflare Pages

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

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.

Region	Fastest provider (p50)	p50	p95	Uptime
Asia (Tokyo)	fireworks	16 ms	60 ms	100%
Europe (Germany)	fireworks	98 ms	199 ms	100%
South America (São Paulo)	openrouter	62 ms	104 ms	100%
US (Central)	fireworks	28 ms	77 ms	100%

• Full dataset: data/rankings/2026-10-10.json (latest)
• Citable archive (DOI): 10.5281/zenodo.22764636 — 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 2026-10-10T07:22:13Z — this table is regenerated daily.