Celeris-1: API Specs, Pricing, and Latency Benchmarks

Celeris-1 is a low-latency language model served through Celeris's OpenAI-compatible API. This guide summarizes the provider's current model documentation and published latency evaluation, then explains the short-request use cases that fit its limits. The game's trivia-buzzer demonstration and unsupported broad model ranking from an earlier version have been removed.

Celeris documents a total 8,192-token request window and an OpenAI-compatible API at a model-specific base URL. Its request guide says explicit output limits must be positive multiples of 256; if omitted, the service defaults to 2,048 tokens. The latency guide says streaming responses do not arrive as progressive partial text, so clients should not assume stream: true will make tokens appear earlier.

The current pricing page lists $0.20 per million input tokens and $0.70 per million output tokens. Prices and workspace activation requirements may change; check the provider's console and pricing page before budgeting. The service requires a Celeris API key, which must stay on a trusted server or in a secret store rather than public browser code.

Celeris reports 75.9% on the full MMLU-Pro test set at a median server-reported latency of 158 ms with its fastest reasoning-off configuration. It reports accuracy up to 81.4% when reasoning is enabled. In its methodology post, the company describes a shared evaluation harness for several models and explains that timing is server-reported for some providers but end-to-end from a colocated client for Gemini endpoints. Those timings are not perfectly like-for-like across every system.

The benchmark page also documents dataset, few-shot setup, answer format, and model-specific configurations. It reports results from the provider's test, not an independent replication. Use those results to decide whether latency merits a trial, then measure end-to-end performance on the endpoint and region your application will actually call.

The short context and fast response profile may suit classification, extraction, query rewriting, routing, or brief agent steps where the input and expected output are bounded. The same limits make it a weaker default for long documents, large agent transcripts, and tasks that need a long answer. For those cases, route to a model with a larger context window or split the task carefully.

1. Set an explicit max_tokens value that is a positive multiple of 256 and leaves room for the prompt inside the 8,192-token total window.

2. Measure end-to-end latency from the same region and client your application will use; separate first-token time from full-answer time.

3. Test output formatting and task success on representative requests, especially if your caller expects strict JSON or a tool call.

4. Compare the cost per accepted result with a slower model; include retries, response length, and any routing logic.

5. Check the current route, API key handling, pricing, and data policy before sending user data.

Celeris-1's official benchmark suggests that some short tasks may become practical in a fast interaction loop. Whether it fits depends on the exact workload, the endpoint timing, and the quality threshold your application must meet.