Why Streaming vs Batch Pricing Matters in a Cost Comparison
Once you've narrowed a model shortlist on the main TokenRate calculator, the Compare Prices side-by-side view is where you stack them for a decision. Each row shows the provider, the model ID (the one you'd paste into your SDK), per-1M input and output costs, the context window, and the blended quality score. Most teams compare LLMs on per-token input price and call it a day. Streaming vs Batch Pricing is one of the under-attended dimensions that shows up in the Compare Prices grid — and ignoring it is where the surprise bills come from. Streaming doesn't add a per-token premium on any provider. Batch API (where supported) typically discounts 50% on input and output — a material multiplier on bill. See also: filter LLM models by tier, cost, quality, Value column vs tokens-per-dollar, and how to pick an LLM by quality score and cost.
How Streaming vs Batch Pricing Appears in the Grid
The Compare Prices grid shows the standard rate, not the batch rate. For batch-eligible workloads (offline embeddings, daily reports), mentally halve the column for impact estimation.
Reading the Spread
Streaming saves on time-to-first-token (user-perceived latency) but doesn't affect cost. The right Compare Prices framing for streaming workloads is the same as non-streaming: pick on input/output rate, context, quality.
Practical Implications
Batch API support: OpenAI yes (50% off), Anthropic yes (50% off), Google yes, others varies. The Compare Prices grid doesn't flag batch support — check the provider docs.