LLM Latency & Speed Comparison
Compare response times across 33 models. Find the fastest AI API for your latency requirements — ranked by speed, cost, and quality.
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How to Reduce LLM Latency
Use Streaming (SSE)
Stream tokens as they arrive instead of waiting for the full response. Users perceive streaming as instant even if TTFT is 500ms+. All providers support streaming via Server-Sent Events.
Shorter Prompts
TTFT scales with input length. A 100-token prompt gets first token 2-3x faster than a 2000-token prompt. Trim context and use system prompts efficiently.
Model Routing
Route simple questions (FAQs, classification) to fast budget models like Gemini Flash (170 TPS). Save flagship models for complex reasoning. Reduces average latency by 60%+.
Connection Pooling
Reuse HTTP connections to eliminate TLS handshake overhead. Most SDKs handle this, but custom implementations should use keep-alive connections and connection pools.
Edge Caching
Cache identical requests at the edge. First request hits the API (500ms), cached responses return in <50ms. Works great for chatbot FAQs and repeated queries.
Batch for Non-Urgent
For async workloads (report generation, data processing), use Batch APIs. They're 50% cheaper and don't compete for real-time capacity, so your interactive requests stay fast.
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