ChatGPT Pro API Credits vs Claude Max: What $500 Buys

On 28 October OpenAI started giving ChatGPT Pro and Business subscribers free API credits (API changelog): up to $500 a month, claimed from ChatGPT's billing settings. Anthropic has run an almost identical deal for Claude Max and Team since early October. The dollar amounts are easy to compare; what they buy is not, because each vendor counts tokens differently and prices its models differently. So on the day after the announcement we ran one office task, a messy 40-row sales sheet, on seven models the two credit programs pay for, and divided each month's credit by the measured cost per run.

Measured on 29 Oct 2026 through the OpenAI and Anthropic APIs from a Python script. We do not have a ChatGPT Pro subscription, so we have not claimed the OpenAI credits ourselves; the OpenAI terms below come from OpenAI's own documentation. Our Claude Max 20x credits we have claimed and use every day.

At a glance

Key results

• Same money, same tiers: OpenAI gives $100, $200 or $500 a month on its three Pro tiers; Claude gives $100 on Max 5x and $200 on Max 20x. Both give Business/Team $20 per Standard seat and $100 per Premium seat, pooled and capped at $500.
• The top individual allowance differs: $500 a month on ChatGPT Pro's $500 tier vs $200 on Claude Max 20x.
• A month of credit is a lot of office work. At our measured cost per run, $200 pays for about 32,700 runs of GPT-6.1 Terra or 7,000 runs of Claude Opus 5.5 on this task; even Claude Fable 5.1, the most expensive setup, gets about 2,000.
• Only two setups scored 10/10 in all three runs: GPT-6.1 Terra (high effort, $0.0061 a run) and GPT-6 Astra ($0.061). Claude Haiku 5.5 scored 10, 10, 9 at $0.0011 a run, a sixth of Terra's cost.
• Claude's tokenizer counted the same prompt as 1,645 tokens; OpenAI's counted 1,192. That is 38% more input tokens on Claude before any price difference.
• Both credits expire every billing cycle and neither covers the vendor's coding agent: Codex through API keys on OpenAI, Claude Code on Anthropic.

Hands-on test

Setup

• Task: the sales sheet from our GPT-6.1 Terra test: 40 invented rows, three branches, six planted problems (a duplicate order, the date 2026-09-31, a price written "1.2k", the branch written "SF", a quantity of −2). The model returns branch totals, the top branch and the problem rows as JSON.
• Scoring (code, not AI): 1 point per branch total correct to the dollar, 1 for the top branch, 1 per planted problem found, minus 1 per clean row flagged. Maximum 10. The prompt, data and answer key were committed on 27 Oct, before any model saw them.
• New runs (29 Oct): claude-haiku-5-5, claude-sonnet-5-5, claude-opus-5-5 at default settings; gpt-6-luna at its default effort (medium); gpt-6.1-terra at high. Three runs each, interleaved. The Claude Fable 5.1 and GPT-6 Astra rows are the 27 Oct runs on the identical prompt, re-read from saved logs.
• Cost: tokens × list price per million tokens, read on 29 Oct from OpenAI pricing and Claude pricing: Haiku 5.5 $0.10/$0.50, Sonnet 5.5 $2/$10, Opus 5.5 $4/$20, Fable 5.1 $10/$50; GPT-6 Luna $0.10/$0.50, GPT-6.1 Terra $1/$5, GPT-6 Astra $10/$50. Output includes reasoning tokens. The 15 new runs cost about $0.17.
• "Runs per credit" = monthly credit ÷ median cost per run. It ignores prompt caching and batch discounts, which would stretch the credit further on both sides.

What one month of credit buys

On Claude, $500 is only reachable on a Team plan; on OpenAI, on Pro's $500 tier or a Business workspace.

Image: RanThis · Source: ranthis.com

Nearly every lost point came from one cell. Row 18 lists a price as "1.2k", and $1,200 is far below the $3,200 every other filing cabinet sold for, so a model can read it as shorthand to normalize or as bad data to drop. Opus 5.5 and GPT-6 Luna dropped it in all three runs. The one other lost point: Luna's first run did not flag the "SF" row (it still counted it in San Francisco's total).

Claim, cover, expire: the two programs side by side

From OpenAI's API credits for ChatGPT subscribers and Anthropic's API credits for Max and Team plans, both read on 29 Oct 2026.

The structures are close enough that the practical advice carries over. When we claimed our Claude credits, the trap was the organization: we linked one Console organization while our app's key lived in another, so the $200 sat untouched while calls were still charged to a card (how we caught it). OpenAI's program has the same shape: credits go only to the linked organization, and changing it takes a support ticket. Before you confirm, check that the API key you actually use belongs to that organization.

What worked

What worked

• Every run on every model returned parseable JSON; no retries.
• All seven models caught the duplicate order, the impossible date and the negative quantity in every run.
• Re-using a task committed two days earlier kept the comparison fair: byte-identical input, scoring code nobody touched after seeing a result.

Where it fell short

What didn't work

• We could not claim or test the OpenAI credits themselves; we have no ChatGPT Pro plan. Our OpenAI runs were billed to an ordinary paid API key, which uses the same models and list prices.
• One task with three runs per model cannot rank these models in general. It shows the order of magnitude of what a credit buys, not which model is best.
• Token counts and costs depend on the language. On a Vietnamese version of the same sheet (run the same day for our Vietnamese sister site), the median cost per run rose 20–76% for six of the seven models (GPT-6.1 Terra stayed flat), and only GPT-6 Astra scored 10/10 in all three runs.

Our verdict

If you already pay for ChatGPT Pro or a Business workspace, claim the credits: it takes a few clicks and costs nothing extra. Do not buy Pro for the credits alone unless you will actually call the API, because for office tasks like this one even $100 a month is thousands of runs, more than most people use. The decision that matters more than the dollar amount is the model: on our sheet, GPT-6.1 Terra on high effort was the cheapest setup that scored 10/10 every time, while Claude Haiku 5.5 came close at a sixth of the price. And whichever credit you claim, link the organization that holds the key you actually use.

How this was made: an AI (Claude) helped write this article; the RanThis editor ran the test script, checked every number against the raw results and checked facts against the linked OpenAI and Anthropic documentation.