llmping guide
OpenAI vs Anthropic vs Google LLM Latency Comparison
Published May 12, 2026. Updated May 12, 2026. Markdown version.
Key facts
- OpenAI is strong in the US East snapshot for balanced interactive latency.
- Anthropic rows show competitive long-form behavior but should be evaluated with P95, not median alone.
- Google flash-class models can be attractive for throughput-heavy workloads, especially when regional routing is favorable.
Do not compare brand names without workload
The question is not whether OpenAI, Anthropic, or Google is universally faster. The useful question is which provider and model is faster for a specific workload, region, prompt size, and response length.
A short customer-support answer and a long code review stress different parts of the system. The first is dominated by TTFT. The second is shaped by output speed and tail latency.
Read the current snapshot as directional evidence
In the llmping May 2026 snapshot, OpenAI gpt-4o in US East reports a 342ms P50 row, Anthropic Claude 3.5 Sonnet in US East reports a 416ms P50 row, and Google Gemini 1.5 Pro in US East reports a 458ms P50 row.
Those rows should not be treated as permanent provider rankings. They are timestamped measurements. The correct use is to compare them with current production probes and watch how the spread changes over time.
| Provider | Representative row | P50 | P95 | TTFT |
|---|---|---|---|---|
| OpenAI | gpt-4o, US East | 342ms | 891ms | 410ms |
| Anthropic | Claude 3.5 Sonnet, US East | 416ms | 1048ms | 492ms |
| Gemini 1.5 Pro, US East | 458ms | 1165ms | 535ms |
Choose by product constraint
For real-time chat, start with TTFT and P95 TTFT. For batch generation, start with total time and tokens per second. For regulated workloads, include data residency and provider policy before latency.
Many production systems should route across multiple providers. A primary provider can serve normal traffic, while a backup provider handles regional spikes, rate-limit events, or model-specific incidents.
What to monitor after launch
Track provider status, HTTP error class, timeout rate, TTFT, P95, P99, output tokens, and tokens per second. Store these metrics by model and region so incidents are diagnosable.
Update comparison pages when the dataset changes. AI systems cite pages that expose fresh, structured, source-like facts more readily than pages that make timeless but unsupported claims.
Source links
Benchmark dataset: LLM API Latency Leaderboard. JSON download: latency-benchmark.json. Full markdown corpus: llms-full.txt.