SemiAnalysis finds a large Claude subscription advantage, with workload-dependent limits

SemiAnalysis reports that Anthropic subscriptions deliver about five times the API-equivalent value of OpenAI's plans in its comparison of Opus 5.5 and GPT 6.1 Sol. The result comes from usage-limit experiments, with a specific workload assumption, rather than a universal assessment of which assistant is best for a subscriber.

The October 5 analysis, by Andrew Megalaa, Max Kan and Dylan Patel, measures how different tokens consume subscription meters. Its central caution is useful even for readers uninterested in the ranking: a plan's value depends on the selected model and the work being done. A monthly price or a message count cannot capture all of that.

Measuring consumption through the usage meter

SemiAnalysis distinguishes fresh input, cache writes, cache reads and generated output. These token types can consume an allowance at different rates. Its experiments isolate one type as far as possible, record billed tokens and observe how the provider's usage meter changes.

A request may not move a meter enough to register, so the researchers measure completed steps between meter increases. They discard partial steps at the start and end of a run. They also account for unavoidable tokens of other types, such as required instructions, using the rates measured in separate experiments.

The publication says it continues measuring until its rate range is within plus or minus 5%. That addresses uncertainty from observing the meter once per request. It does not remove every limitation of translating subscription access into a monetary comparison. The researchers also assume cache reads are free when the meter has not moved after 500 million such tokens, which is a stated measurement convention rather than a contractual guarantee.

The value depends on the model and workload

To calculate API-equivalent value, SemiAnalysis combines measured limits with a workload's token proportions and the models' API prices. For its agentic workload, it uses its own September usage ratios. A different pattern of input, cached context and output could produce a different comparison.

The publication reports roughly five times the API-equivalent value for Anthropic in the Opus 5.5 versus GPT 6.1 Sol comparison. It says the gap remains large when comparing raw token amounts, while acknowledging that the models have different per-token API prices. Those prices are part of the calculation, so a model's API price reduction can lower its measured equivalent value without reducing the subscriber's token allowance.

SemiAnalysis also describes a different result for the more expensive GPT-6 Astra and Fable 5.1 tiers: their limits are closer, and Fable has a separate allowance constraint. That qualification prevents the mid-tier result from becoming a claim that every Anthropic model offers five times as much use as every OpenAI model.

A snapshot can change without a new plan name

SemiAnalysis reports a reduction in the API-equivalent value of OpenAI's $200 plan and a new $500 tier. It says plans bought before the cut retain the previous higher limits until October 29, while new purchases start with the lower allowance. These are the publication's observations at the time of its report, not a current entitlement verified by Franklin in a subscriber account.

The authors also found one of three otherwise equivalent subscriptions with about 20% lower limits. They say the provider confirmed it was part of a small A/B test. That example explains why one account's observed limit should not automatically become a provider-wide promise.

OpenAI's Pro plans lack a five-hour limit, according to the analysis, which can make more of a weekly allowance usable in a concentrated session. That practical difference may matter to someone whose work arrives in bursts, even when another plan offers a larger total token budget.

A sensible purchasing comparison therefore starts with the model and the actual work: how much context is reused, how much output is generated, and whether a short-window cap interrupts a session. SemiAnalysis's measurements provide a way to compare those constraints. They do not establish task success, model quality or guaranteed savings for every user.