AI Agent StoreOpenAI · Restricted research access
GPT-Rosalind Research
A specialized life-sciences reasoning model for approved research organizations. OpenAI lists October 5 as its billing start; this is a pricing milestone, not a new model launch. Comparable independent benchmarks are pending.
First reviewed 2026-10-05 · Checked 2026-10-15. Approved research access was announced September 8, 2026. The original model launch date is unverified.
We still need comparable independent tests. You can compare the confirmed features and limits below, but we cannot yet give this version an Agent Fit or Practical Value rating. Test it on your own tasks before switching; results for another version or thinking setting may not apply.
What you can use today
Token prices measure the small pieces of text the model reads or produces. Capacity is the amount the service accepts in a request; check the limits below for any fallback to another model. A dash means the rate is unverified.
Exact provider API ID: gpt-rosalind-research. OpenAI describes this as a life-sciences reasoning model. The reviewed sources do not establish its context window, output limit, input formats, tools or effort settings.
OpenAI’s September 8 access notice requires approval through its trusted-access program and limits use to approved internal research. A standard API account does not establish access.
Published standard rates per million tokens: $5 input, $0.50 cached input and $25 output. Billing begins October 5, 2026; an exact cutoff time and timezone are not published. OpenAI says cache-write pricing does not apply. Task costs and measured speed are unavailable. Your bill can also include tools, retries and charged thinking tokens. Check the offer used by your application.
Provider release and specifications ↗
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Test it before switching
Give each model the same examples, tools and definition of success. Record correct outcomes, response time, retries and total cost. A cheaper token price does not guarantee a cheaper completed task.
Compare caching and long-context costs ↗