OpenLLMPrices says its June 8 dataset tracks official API prices, context tiers, source URLs, and historical snapshots for major LLM providers, aiming to make model pricing easier to compare and verify.
OpenLLMPrices has published a June 8 version of its Official LLM API Pricing Dataset, describing it as a source-backed record of API prices, context tiers, source URLs, and date-versioned snapshots for large language model providers.
According to OpenLLMPrices, the project tracks provider names, model names, token prices, cache prices, context tiers, official source URLs, and dated snapshots. The public OpenLLMPrices site describes the resource as an “Official LLM API Pricing Dataset,” while the associated GitHub repository, jayyao97/llm-pricing-tracker, says every listed model price requires an official source URL.
The June 8 dataset version, published at openllmprices.com/data/prices.json, says its notes include re-checks of pricing pages from OpenAI, Anthropic, Google, DeepSeek, xAI, Zhipu, Alibaba, ByteDance, Kimi, Xiaomi, and MiniMax. The dataset positions those checks as part of maintaining a dated record rather than a one-time price table.
For developers and technical buyers, that distinction matters. LLM pricing can vary by input tokens, output tokens, cached input, model family, and context window. A table that also records context tiers and links back to official pricing pages can help users verify whether a quoted price applies to the model and usage pattern they are evaluating.
The OpenLLMPrices website says the tracker displays date-versioned prices. The GitHub README similarly describes a static LLM API pricing tracker that reads from data/prices.json and displays historical versions. That design is useful because model providers frequently update product lines, retire older names, introduce lower-cost variants, or change cache and long-context pricing structures.
A date-stamped pricing dataset does not eliminate the need to check a provider’s current pricing page before making a purchasing decision. It does, however, create a clearer record of what was listed at a given time and where the listed price came from. For teams comparing model costs across months, that can help separate real price changes from documentation changes, model renames, or differences in context tiers.
The dataset’s emphasis on official URLs also makes it more auditable than informal comparison charts. OpenLLMPrices and the project README both state that the tracker relies on official source links for model prices. That approach gives readers a path to verify entries directly with the provider, including details that may not fit neatly into a single headline number.
The sources provided by OpenLLMPrices and GitHub describe the dataset’s structure and maintenance approach, but they do not claim that the dataset covers every LLM provider or every commercial contract. Public API prices may also differ from enterprise agreements, promotional credits, regional availability, or private deployment terms.
The dataset is therefore best understood as a public reference for official, published API pricing rather than a complete view of all possible LLM costs. Its value comes from combining model-level prices, context information, cache pricing fields, source URLs, and dated records in one machine-readable file.
As LLM providers continue to compete on price, context length, latency, and model capability, a transparent pricing dataset can give developers a more consistent way to compare public API offerings. OpenLLMPrices’ June 8 release shows an effort to make those comparisons easier to inspect and reproduce, while still pointing users back to the official pricing pages that remain the authoritative source for current terms.
A dataset focused on official pricing sources According to OpenLLMPrices, the project tracks provider names, model names, token prices, cache prices, context tiers, official source URLs, and dated snapshots.
The dataset positions those checks as part of maintaining a dated record rather than a one time price table.
For developers and technical buyers, that distinction matters.
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