# LLM fast mode pricing, and what it excludes

> OpenAI renamed priority processing to Fast mode and bills it at double, except gpt-5.5 at 2.5x. Anthropic's is 2x on two models and blocks batching.

URL: https://getculpa.com/llm-fast-mode-pricing
Last reviewed: 2026-08-03
Rates effective: 2026-07-02

## Answer
Fast mode is a premium latency tier that bills the same tokens at a higher rate. OpenAI charges double on most models and 2.5 times on GPT-5.5, and Anthropic charges double on two Opus models. Culpa, a local-first LLM cost, margin, and forecast ledger, prices each call from a versioned price book, so a tier change shows up as a cost rather than a setting.

## Why this happens
Fast mode buys latency with money and the exchange rate isn't uniform. OpenAI renamed priority processing to Fast mode on 2026-07-30 and accepts either service_tier value, so old code and new documentation describe the same billing. Its multiplier is 2x on the GPT-5.6 family and on GPT-5.4, and 2.5x on GPT-5.5, which is the one worth knowing because it's the model many teams are still on. Anthropic's is a research preview covering Claude Opus 5 and Opus 4.8 only, at exactly double, and the restrictions around it matter more than the rate. It runs on the first-party Claude API only rather than on partner cloud platforms. It's unavailable with the Batch API, so the two biggest price modifiers in the catalogue can't be combined. And on Opus 4.6 a fast request runs at standard speed and bills at standard rates, so asking for it and silently not getting it counts as supported behaviour.

## What this usually looks like
- Latency improved after a config change and nobody priced what it cost.
- A service_tier value is set in code and no one remembers which model it applies to.
- You tried to batch a fast-mode workload and the request was refused.
- Your GPT-5.5 spend is higher per token than your GPT-5.6 spend, on the same tier.
- A fast-mode flag is set on a model that ignores it, so you pay standard and think you're paying more.

## Common mistakes
- Assuming the fast multiplier is the same on every model. Why it hurts: OpenAI charges 2x on most and 2.5x on GPT-5.5, so a per-model check is the only reliable one. Do instead: Read the fast-mode table per model rather than applying one multiplier across a family.
- Planning to batch a fast-mode workload. Why it hurts: Anthropic states fast mode isn't available with the Batch API, so the 50% discount and the premium tier are mutually exclusive. Do instead: Decide which one the workload actually needs, because you can't have both.
- Setting a fast flag globally. Why it hurts: Some models error, some silently run standard, and some bill double. All three look like working code. Do instead: Set it per call site, and check the reported cost rather than the config.
- Buying latency you can't measure the value of. Why it hurts: Doubling the rate is easy to justify in a meeting and hard to justify against a number nobody produced. Do instead: Price the premium on last month's real volume before enabling it, then compare against what the latency earned.

## Self-check
- Grep for service_tier and speed flags, and list which models each one reaches.
- Price last month's volume at the standard rate and at the fast rate, and look at the gap.
- Check whether any fast-flagged model silently ignores the flag.
- Confirm nothing in your batch pipeline also requests fast mode.

## The same volume on three tiers
100M input and 10M output tokens a month on GPT-5.5, priced from OpenAI's own pricing page read 2026-08-03. GPT-5.5 is chosen deliberately because it carries the highest published fast multiplier. Standard is $5.00 and $30.00 per million, fast mode is $12.50 and $75.00, and batch is half of standard. Token volume is modelled and every rate is published.
  standard: (100 x $5.00) + (10 x $30.00) = $500.00 + $300.00 = $800.00
  fast mode: (100 x $12.50) + (10 x $75.00) = $1,250.00 + $750.00 = $2,000.00
  batch: half of standard = $400.00
  fast is 2.5x standard and 5.0x batch, on identical tokens
The spread between the cheapest and dearest way to run the same tokens is five times, and none of that comes from a discount you negotiate. It's three settings. Knowing which one each workload runs on is worth more than most optimisation work.

## Cost figures
Every figure carries its confidence and its source. For each provider-reported figure below, its own source line names the published record it came from, the date that record was read, and any aggregation applied to it.
- $2,000.00 per month, modelled GPT-5.5 volume on fast mode, against $800.00 standard and $400.00 batched [calculated] Source: 100M input and 10M output tokens at OpenAI's published GPT-5.5 rates read 2026-08-03: fast mode $12.50 and $75.00 per million gives $2,000.00, standard $5.00 and $30.00 gives $800.00, batch at half of standard gives $400.00. Token volume is modelled and all three rate pairs are published.
- 2.5x, OpenAI's published fast-mode multiplier on GPT-5.5, against 2x on every other model listed [provider-reported] Source: developers.openai.com/api/docs/pricing, read 2026-08-03. GPT-5.5 standard is $5.00 and $30.00 per million and its fast-mode row is $12.50 and $75.00, which is 2.5x on both. The GPT-5.6 family and GPT-5.4 are 2x on both. Reported because a single multiplier applied across a family would be wrong on the model many teams are still running.

## FAQ
Q: What does fast mode cost?
A: On OpenAI, double the standard rate on the GPT-5.6 family and GPT-5.4, and 2.5 times on GPT-5.5. On Anthropic, double on Claude Opus 5 and Opus 4.8, which are the only two models it covers, at $10.00 and $50.00 per million against a standard $5.00 and $25.00. Read from both pricing pages on 2026-08-03.

Q: Was fast mode called something else?
A: Yes. OpenAI's pricing page states priority processing was renamed Fast mode on 2026-07-30, and that requests can use either service_tier value. So older code and newer documentation describe the same billing, which is worth knowing when auditing what a flag does.

Q: Can I combine fast mode with batching?
A: No. Anthropic states plainly that fast mode isn't available with the Batch API. Those are the two largest modifiers in the catalogue, one doubling the rate and the other halving it, and they can't be applied to the same request.

Q: What happens if I request fast mode on a model that doesn't support it?
A: It depends on the model, and both behaviours exist. Anthropic states Opus 4.7 returns an error for a fast request, while Opus 4.6 runs at standard speed and bills at standard rates. The second is the one to watch, because the flag is set, the code works, and nothing you asked for happened.

## Sources
- OpenAI API pricing: https://developers.openai.com/api/docs/pricing
- Anthropic pricing: https://platform.claude.com/docs/en/docs/about-claude/pricing

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