SemiAnalysis measures it directly -- Anthropic's $200 plan beats OpenAI's by ~5x
Controlled token-metering experiments, not marketing claims, put a number on the gap between Claude and ChatGPT subscription value for agentic work -- and OpenAI's newer $500 tier barely moves the needle against it.
SemiAnalysis published a direct, methodology-first comparison on October 5 of what a Claude subscription and a ChatGPT subscription actually buy, rather than what either company claims. The approach: isolate each token type (input, cache write, cache read, output) in controlled requests, watch each provider's own usage meters drain, convert consumption rates to token counts per time window, and price it out at current API rates. Under that methodology, for agentic workloads, "Anthropic is an overwhelmingly better deal, offering ~5x the API-equivalent value across the board" comparing Opus 5.5 to GPT 6.1 Sol.
The actual numbers
Anthropic's $200/month plan works out to roughly $2,485 worth of Fable 5.1 API usage before the subscriber's quota meaningfully tightens -- SemiAnalysis's write-up puts subscribers at "50% left after consuming $2,485 worth of Fable 5.1," versus OpenAI's equivalent $200 plan, which the analysis says is fully exhausted around $2,897 of Astra usage at a worse effective rate. The comparison gets sharper on OpenAI's side of a recent change: OpenAI cut the value of its own $200 plan by half, and the newer $500 tier OpenAI introduced to compensate only delivers 21% more usable Astra than the old $200 plan did.
A number this site's own thesis depends on being real
This is a rare case of someone actually measuring the thing Merit AC exists to measure -- not spend, but value per dollar -- at the subscription-plan level instead of the per-seat level. If SemiAnalysis's methodology holds up, a company defaulting every engineer onto ChatGPT instead of Claude without re-checking the math is leaving a real, quantified amount of usable capacity on the table per seat, every month. The obvious caveat: this is one outlet's controlled experiment, not an audited benchmark, and "agentic workloads" is a specific usage pattern that won't match every team's actual mix of tasks -- worth treating as a strong data point to re-check against your own usage, not a universal multiplier.