Rate-Limit & Tier Planner
- Your workload needs
- 60 rpm / 480,000 tpm
- GPT-5.6 Sol, Tier 1 allows
- 500 rpm / 500,000 tpm
This workload fits inside Tier 1's published limits.
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GPT-5.6 Sol — published rate-limit table
| Free | Not supported |
|---|---|
| Tier 1 | 500 rpm · 500,000 tpm · 1,500,000 batch queue |
| Tier 2 | 5,000 rpm · 1,000,000 tpm · 3,000,000 batch queue |
| Tier 3 | 5,000 rpm · 2,000,000 tpm · 100,000,000 batch queue |
| Tier 4 | 10,000 rpm · 4,000,000 tpm · 200,000,000 batch queue |
| Tier 5 | 15,000 rpm · 40,000,000 tpm · 15,000,000,000 batch queue |
Tier 2 → Tier 3: what it actually raises, per model
- GPT-5.6 Sol: RPM does not increase from Tier 2 to Tier 3 (TPM does).
- GPT-5.6 Terra: RPM does not increase from Tier 2 to Tier 3 (TPM does).
- GPT-5.6 Luna: RPM does not increase from Tier 2 to Tier 3 (TPM does).
- GPT-5.5: RPM does not increase from Tier 2 to Tier 3 (TPM does).
- GPT-5.5 Pro: RPM does not increase from Tier 2 to Tier 3 (TPM does).
- GPT-5.4: RPM does not increase from Tier 2 to Tier 3 (TPM does).
- GPT-5.4 Mini: RPM does not increase from Tier 2 to Tier 3 (TPM does).
- GPT-5.4 Nano: RPM does not increase from Tier 2 to Tier 3 (TPM does).
- GPT-5.4 Pro: RPM does not increase from Tier 2 to Tier 3 (TPM does).
- GPT-5.3 Codex: RPM does not increase from Tier 2 to Tier 3 (TPM does).
The -pro models are throttled harder than their price suggests
- GPT-5.5 Pro: Tier 1 allows 50 rpm / 50,000 tpm — roughly 1/10 of the 500 rpm most other current models get at Tier 1. Its 1,050,000-token context window is 21.0× a whole minute's Tier 1 token budget.
- GPT-5.4 Pro: Tier 1 allows 500 rpm / 30,000 tpm. Its 1,050,000-token context window is 35.0× a whole minute's Tier 1 token budget.
A cost estimate answers whether you can afford to run a workload. This tool answers a different and often more urgent question: whether your account can actually push it through at the rate you need, before spend ever becomes the binding constraint.
Why this is a separate question from cost
Two workloads can cost the same and behave completely differently against a rate-limit ceiling, because RPM and TPM caps don't scale with price the way you might assume — one of the highest-priced models on this site's roster publishes a Tier-1 requests-per-minute limit roughly a tenth of what every standard-tier model gets, and a different high-context model's Tier-1 token ceiling is small enough that a single maximum-length prompt can burn through most of a full minute's budget on its own. Neither of those facts shows up in a per-token price. They only show up in the published rate-limit tables, which is exactly what this planner checks your workload against.
What you enter
A request shape — tokens in, tokens out, requests per minute you actually need to sustain — and the model and usage tier you're on or considering. The planner checks that shape against the real published RPM and TPM ceilings for that specific model and tier, not a generic number, since the tables genuinely differ across five distinct groups rather than scaling uniformly with price or capability.
What "next tier" actually buys you
This is the detail worth checking before assuming an upgrade solves a throughput problem: moving from the second usage tier to the third raises your monthly spend ceiling and, in most published limit groups, a meaningful jump in token-per-minute headroom and batch-queue size — but in several of those same groups, it does not raise your requests-per-minute ceiling at all. If your workload is bottlenecked specifically on RPM rather than TPM, paying to reach a higher tier can leave the actual constraint completely unchanged, and the planner flags this explicitly rather than assuming every tier upgrade helps uniformly.
Reading a red flag correctly
If your entered workload exceeds the ceiling for your current tier, the planner shows exactly which limit you'd hit first — RPM or TPM, whichever binds — and what the next tier that actually raises that specific limit would look like, rather than just naming "the next tier up" generically. That distinction matters given the RPM-plateau pattern above: the fix for a token-per-minute ceiling and the fix for a requests-per-minute ceiling aren't always the same upgrade.
Why account dashboards still win ties
Published tables are the honest default, and they're what this planner checks against — but limits can be raised for an individual account beyond the published defaults, and your own dashboard is the final authority on what you actually have access to right now. Treat a result here as what to expect before you've checked, and a genuine mismatch between this planner and your dashboard as a sign your account has a non-default limit, not a sign the planner is wrong.
Planning around a workload that's growing
A workload sized comfortably today against your current tier can outgrow it — more users, a busier CI schedule, a session that's started accumulating more context per call than it used to. Re-running this planner periodically as a workload's real shape changes, rather than checking it once at launch and assuming the answer stays true, is the cheap way to catch a throughput ceiling before it shows up as a wave of 429 responses in production instead.
Free tier and Tier 1 deserve a closer look than they usually get
It's tempting to treat the entry-level tiers as a formality you'll blow past quickly, but the free tier specifically isn't universally available — it depends on account geography — and the jump from nothing to Tier 1 requires a real, if modest, amount paid in. For a side project or an early prototype still deciding whether a workload is worth building out, checking whether it even fits inside the free tier's ceiling before writing a line of production code is worth the two minutes it takes, since discovering a throttling problem after launch is a worse time to learn the answer was already knowable.
A 429 doesn't always mean "upgrade your tier"
A rejected request due to rate limiting can also mean a single burst exceeded the window rather than the account genuinely needing more sustained throughput — smoothing request timing, adding a client-side retry with backoff, or batching several small calls into fewer larger ones can solve a bursty pattern without touching your account tier at all. This planner is built around sustained, steady-state throughput specifically; a workload that's occasionally spiky rather than consistently near its ceiling is a different problem, and upgrading a tier to fix a spike you could smooth out instead is spending money on the wrong lever.
Verified 2026-08-09 against https://developers.openai.com/api/docs/guides/rate-limits.
Could not confirm: Per-model tables are published per the models listed in src/data/facts/models.ts. An account's actual limits can be raised individually by OpenAI, so the reader's own dashboard is the final authority — these tables are the published defaults, and the tools on this site let the reader override them.
Checked: https://developers.openai.com/api/docs/guides/rate-limits · https://developers.openai.com/api/docs/models/gpt-5.6-sol · https://developers.openai.com/api/docs/models/gpt-5.3-codex