A subscription price is simple. Comparing what it buys is not. The useful starting point is to separate how much usage a plan permits from the API prices used to express that usage in dollars.
What the dollar value measures
In SemiAnalysis’s October 5 comparison, the $200 plans yield an estimated 28.6 billion tokens for Claude Opus 5.5 and 10.2 billion for GPT 6.1 Sol under its agentic workload. Valued at the corresponding API prices, those allowances become $11,726 and $2,084.
Our calculation separates the approximately 5.63× dollar ratio into 2.80× token volume and 2.01× implied blended API price. A higher API price contributes to a larger API equivalent value. It does not establish better answers, more completed work or cash savings.
Same fee. Different measures.
Estimated monthly token allowance · $200 plans
Tokens include cache reads. They are not all newly generated answers.
2.80× token volume × 2.01× implied API price ≈ 5.63× API equivalent value.
Calculated from rounded source totals. Higher API pricing increases this value measure without proving better results.
SemiAnalysis · October 5, 2026. Estimated full use of the source’s agentic workload. Historical allowances, not current plan guidance.
Exact values and calculation
| Model | Billions of tokens |
|---|---|
| Claude Opus 5.5 | 28.6B |
| GPT 6.1 Sol | 10.2B |
Implied blended dollars per million tokens = API equivalent dollars ÷ (billions of tokens × 1,000). The implied price is a calculation from rounded totals, not a separately measured current API rate.
Changing the model changes the comparison.
The same source reports a different API equivalent allowance for its flagship pair. These estimates use the same agentic workload; they do not compare answer quality.
| Monthly fee | Claude Fable 5.1 | GPT 6 Astra |
|---|---|---|
| $20 | Not included | $162 |
| $100 | $1,273 | $1,322 |
| $200 | $2,485 | $2,897 |
Fable’s model specific cap matters. The source limits Fable to half the plan allowance; the remaining allowance can be used with other models. Doubling Fable’s figure would incorrectly assume additional Fable capacity. “Not included” is not a zero performance result.
Source: SemiAnalysis . No flagship token comparison is shown because that dataset was not verified.
Most counted tokens are cache reads.
SemiAnalysis’s September agentic workload, used for its October 5 estimates.
- Fresh input
- 0.4%
- Cache writes
- 2.6%
- Cache reads
- 96.6%
- Output
- 0.3%
Shares are drawn to scale. Rounded source percentages total 99.9%. Source: SemiAnalysis . This is the source’s workload, not an Institute enterprise survey.
The source measures changes in provider usage meters while isolating token types. Where 500 million cache read tokens do not move a meter, it assumes those reads are free. The available methodology does not identify every plan to which that assumption applies. Token totals should therefore not be treated as a uniform measure of paid processing across providers.
The monthly estimates also assume the allowance is consumed within the relevant reset windows. A theoretical ceiling is different from the capacity a particular team can actually use.
Apply the distinction to enterprise purchasing.
A seat plan, committed API spend or platform bundle quotes capacity. Capacity valued at list price is not completed work. Compare options on the cost of an accepted result for your workload, alongside quality, review effort and operating requirements.
Test the work you need done.
Give each plan the same representative tasks and judge the results against the same criteria. Record accepted outputs, review time, errors and usage limits. Then compare the cost of work your team can actually use.
Check limits for the model your workflow needs. An allowance for another model does not increase that model’s capacity.
These results describe the source’s October 5 snapshot. The article itself reports plan changes around publication. This edition does not claim that the same allowances remain available today.
Sources and limits
SemiAnalysis: Anthropic Subscriptions Offer 5x+ More Value Than OpenAI , October 5, 2026, Andrew Megalaa, Max Kan, and Dylan Patel. Allowance figures and workload shares were checked against the original supplied article and chart images. Charts here are redrawn from those figures; original chart images are not republished.
The ratios and implied prices are Institute calculations from rounded source totals. They are not independent measurements. No model quality, current entitlement or task success comparison is claimed. The Institute has not conducted new provider limit testing for this article.
Source analysis prepared October 17, 2026. Read the methodology.