Interactive tool
LLM Cost & Latency Estimator
Estimate monthly LLM spend, p50/p95 latency, cost per 1,000 requests, and anomaly exposure across GPT, Claude, Gemini, Llama, Mistral, and DeepSeek. All math runs in your browser — nothing is sent anywhere.
2026-10-03. Vendors change prices
often and offer batch/committed-use discounts not modeled here. Confirm
on the vendor's official pricing page (linked under the selected model).
Scenario inputs
Estimated outputs
Rough model: class base latency + output_tokens ÷ throughput. Real latency depends on provider load, region, and streaming.
Cost if one feature misbehaves for 7 days — a 10× request spike on this model with retries on. This is the number to alert on.
Pinned scenarios (side-by-side, in this URL)
| Model | In/Out tok | Req/day | Cache | Monthly $ | $ / 1k req | p50 / p95 | Anomaly $ | |
|---|---|---|---|---|---|---|---|---|
| No pinned scenarios yet. Configure inputs and click Pin this scenario to compare models side-by-side. | ||||||||
Methodology & assumptions
Billed input tokens = (avg input + RAG context) per request. With a cache hit-rate of h, a fraction h of those tokens is billed at the model's cached-input rate (where the vendor publishes one) and the rest at the standard input rate. Retries multiply total request volume by (1 + retry%). Monthly = daily × 30.4.
Cost is reported per 1,000 requests. Latency uses per-class base p50/p95 plus output-token throughput from the model's latency class. These are planning estimates, not SLAs.
Pricing is public list pricing collected by the LLMOps Report editorial team and is provided for planning estimates only. Vendors change prices frequently and offer batch, cached-input, and committed-use discounts not modeled here. Always verify against the vendor's official pricing page before relying on a number. Prices are USD per 1,000,000 tokens unless noted.