Budget routing
Cheapest LLM API for your workload
The cheapest model depends on cache ratio, output length, and traffic — not headline list prices. Start with a preset, then scan the ranked table for the lowest monthly estimate.
Related: topics/llm pricing for rag · compare/openai vs anthropic
Calculator
Step 1
Describe your workload
Start with a preset or dial in your own numbers.
1K messages / mo
1K tokens avg
Per message: 750 in · 250 out · 338 cached
Step 2
Estimated monthly spend
$67.50
Rank #63 of 63 · save $67.44/mo vs #1
High-quality reasoning agents and premium customer support flows.
Step 3
Compare all models
63 models priced for your workload
Best value
How costs are calculated
Prices from ai-provider-pricing-validated.json, validated Jun 2026. Confirm on official provider pages before billing decisions.
Model Cost Comparison · Built by Lazige · Methodology
How we calculate cost
Monthly estimate = (input tokens × input $/MTok) + (cached tokens × cached $/MTok when published) + (output tokens × output $/MTok), scaled to your message volume. See the methodology for validation sources and update cadence.
Use cases
Common workload patterns teams model here
Illustrative scenarios — not customer testimonials. Each card shows how a typical team shape (support bot, RAG, code assistant, or agent) maps to the calculator presets.
“A support-bot preset with 50k messages/month surfaced three budget models in one pass — faster than copying rates from five pricing pages.”
“Raising the cached-input slider made our RAG estimate realistic. We moved retrieval-heavy traffic to a cheaper model without changing reply quality.”
“PMs use the embed on internal docs to sanity-check model spend before vendor requests — everyone shares the same workload baseline.”
“Before scaling an agent workflow, comparing monthly cost across every provider for the same token mix avoided over-provisioning on day one.”
“Finance teams grasp token mix faster with one screenshot from the ranking table — useful when justifying a move off a default premium model.”
“Quarterly Bedrock vs Vertex vs direct API reviews start here — normalize the math before opening vendor spreadsheets.”
“The code-assistant preset was a realistic starting point for a copilot MVP; we adjusted tokens after a pilot week and stayed within 10% of the estimate.”
“Gemini Flash placed top three for our exact cache ratio on a high-volume FAQ bot — easy to miss in a static pricing table.”