mistral vs openai — AI API latency comparison

Last verified September 07, 2026

As of September 07, 2026, mistral and openai are evenly matched on edge latency — each is faster in 2 of 4 measured region(s). From Asia (Tokyo), openai leads at 204 ms vs 321 ms p50.

Is mistral or openai faster in each region?

Full details: mistral by region · openai by region.

Does the answer change by region?

The answer flips depending on where you measure from. In Asia (Tokyo) the gap is widest at 118 ms, while in South America (São Paulo) it narrows to 3 ms — and the faster provider is not the same one in every region.

This is why a single-location benchmark cannot answer “is mistral or openai faster” for you. Pick the row that matches where your requests originate.

Does mistral or openai have better uptime?

Over the last 24 hours mistral and openai are level on availability at 100% of probes succeeding across 4 region(s). Neither is separated from the other by uptime at this sample size.

Availability observed by our probes over the last 24 hours, not a contractual SLA. Providers publish their own SLA terms and their own status pages; this is an outside view of whether requests actually completed, which is the number a status page cannot give you about your own region. The worst region is shown next to the average because an average hides the one place where a share of your users sees a different service. Longer windows and all 4 regions: uptime comparison · confirmed outages: incidents.

What exactly is being compared?

Both providers are measured the same way, from the same probes, on the same schedule: a real connection is opened to each provider’s own API host and DNS, TCP, TLS and the first response byte are timed. Neither is measured through a gateway or aggregator, so the numbers reflect each provider’s own infrastructure. Figures are p50 and p95 over the last 24 hours; percentiles are nearest-rank, because a handful of outliers would distort an average.

What this comparison does not cover

This is edge latency — the time before the model starts working. It is the floor you pay on every request regardless of which model you call, and it is the part you cannot optimise away with a better prompt. It is not time-to-first-token, throughput, price or answer quality: those depend on the specific model, and comparing them across providers running different models would not be like-for-like. Measured time to first token (TTFT), with the model named next to every figure and the network share broken out, is on the time to first token page.

Method in full: methodology · Raw numbers under CC BY 4.0: JSON API · No provider pays for placement.