Head to head
Anthropic
Median cold end-to-end latency over time
BlazeRailLLM API
Gemini
Median cold end-to-end latency over time
BlazeRailLLM API
Kimi
Median cold end-to-end latency over time
BlazeRailLLM API
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Methodology
- One workload, every provider. The same script, payload, and model hit every provider, addressed the way its own API expects. Nobody gets a tuned or special path.
- Repeated on a schedule. Every suite runs many iterations per provider, automated on a recurring schedule, so results reflect steady state rather than one lucky request.
- Raw results published. Every run is committed as JSON to the public benchmarks repo, next to the code that produced it. Nothing is cherry-picked or held back.
- How this comparison is built. Each benchmark compares the two providers' medians; results within 5% count as roughly even. A comparison is published only when both providers completed the latency benchmark on at least two of the same model families reliably: at least 50% success for each provider, in benchmark runs from the last 30 days. A pair also qualifies when both providers completed at least 90% of runs in one shared benchmark. If a provider’s latest run of a benchmark failed, its most recent successful run from that window is used instead.
Frequently asked questions
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BlazeRail and LLM API were each faster in 1 of 2 AI gateway benchmarks with a speed verdict. In Anthropic (October 2, 2026), LLM API had a median cold end-to-end latency of 762ms versus 970ms for BlazeRail, 1.27x faster. In Kimi (October 16, 2026), BlazeRail had a median cold end-to-end latency of 1.22s versus 2.12s for LLM API, 1.73x faster.