Ep. 036 - $200 Buys $12,000 of Opus Tokens, We Bought Every Plan (Tokenomics)
Summary
Anthropic’s $200 subscription can deliver roughly $12,000 of usage at API prices without necessarily losing money. Max cites a consensus forecast of more than 90% API gross margins; if the average subscriber consumes only about 10% of the maximum allowance, subscriptions could still earn 50–60% gross margins. The sharper conclusion is that API pricing may be “punitive, exploitative, speculative pricing,” not that every subscription token is subsidized.
A subscription’s value cannot be reduced to one dollar figure because each model and token type consumes plan credits differently. Fresh input, cache writes, cache reads, and output carry different weights, so the same $200 plan can produce radically different API-equivalent values depending on the workload. The defensible formulation is always: “a certain workload on a certain model” yields a particular value.
Caching is the decisive economic lever in agentic inference. The tested agent workload was roughly 96% cache reads; those reads can cost 0.1x or less than fresh input because they retrieve an existing KV cache instead of repeating model computation. Maintaining high cache-hit rates across deployments, memory tiers, and pauses is difficult systems work—and “exactly what distinguishes good inference providers from worthless ones.”
Anthropic offered materially more usable tokens than OpenAI in the speakers’ like-for-like $200 comparison. They measured about 1.9 billion Astra tokens versus 3.1 billion Fable 5.1 tokens, with Fable consuming only 50% of the Anthropic allowance and leaving the other half for Opus. Although critics cited an Artificial Analysis comparison of roughly $5 for a maximum-cost Opus 5.5 task versus $0.72 for a GPT-5.1-Codex task, Andrew Megalaa argues that matching benchmark settings narrow the efficiency gap to 2–3x while Anthropic supplies about 5x the API-equivalent value.
The industry advantage accrues to model owners, while wrappers inherit structurally worse economics. Anthropic can monetize API traffic at greater than 90% gross margin, whereas services such as Perplexity or Cognition may have only around 20% of the API cost available to cover their own economics and consequently offer much lower limits on Anthropic models. The episode’s investor framing is blunt: in AI, possessing a model “that everyone wants to use” is a huge advantage, while being a “wrapper” is especially unattractive.
Realized subscription margins depend on ordinary users consuming nowhere near the published maximum. Max believes Anthropic utilization “cannot be much higher than 10%” if leaked financial figures are to reconcile, implying approximately 40–70% subscription gross margins. OpenAI’s announced 50% limit reduction had not yet hit users who bought plans before the announcement because of a one-month grace period, potentially postponing backlash until the limits become tangible.
Open models can win large volumes of adequate work without invalidating frontier-model economics. Businesses can rationally assign simpler, margin-sensitive tasks to open models such as GLM-5.3; Jordan Nanos did so when frontier-model cybersecurity refusals blocked his workflow, then reused the same context for a trivial ten-line PR at roughly one-tenth the tokens. The unresolved frontier thesis is whether the economy can profitably absorb the equivalent of “100 million super-smart PhDs” faster than routine work migrates to cheaper models.
Deep dive
1. The $12,000 headline reflects API pricing, not proof of an $11,800 subscription loss
Max’s framing: paying $200 and receiving $12,000 of API-priced Claude tokens is unquestionably “a good deal,” but it does not prove negative subscription gross margins. Anthropic’s API margin is estimated above 90%, while most subscribers leave the overwhelming majority of their allowance unused.
At roughly 10% utilization, Max’s tokenomics model puts Anthropic subscription gross margins around 50–60%, versus more than 90% for API sales. His provocative alternative explanation is that API tokens carry “punitive, exploitative, speculative pricing”; Jordan’s rejoinder was simpler: willing buyers are paying the market price.
The team rejects viral analyses assigning one universal API-equivalent value to a plan. Each plan is effectively a bundle of internal credits, and every model-token combination consumes those credits differently: “The $200 Anthropic plan” is worth a specific amount only for a specified model and workload.
2. Four token types turn one subscription into many different products
Andrew’s taxonomy starts with fresh input at roughly 1x cost, followed by cache writes near 1.25x and cache reads around 0.1x or less. Output—the model’s decoding work—is usually dearest: the example was $50 per million output tokens against $10 per million input tokens.
Cache reads retrieve an existing KV cache rather than reprocessing the whole conversation. Since an autoregressive chat repeatedly carries its history forward, cached context can dominate total volume; when most of the workload is cached, the cache-read price can almost completely determine the cost.
The physical analogy matters: cache reading is largely memory movement, while output creation is sequential decoding on the accelerator. That makes workload mix—not merely total token count—the load-bearing variable in any comparison.
3. The limit tests isolated every token price experimentally
Andrew’s team bought each plan and imitated the calls made by popular coding CLIs. For input and cache experiments, they placed a small tag before a token-heavy passage from War and Peace, then requested a trivial answer of roughly four tokens so output would not distort the result.
Randomizing the tag and omitting the cache header isolated fresh input; setting the header produced a cache write; repeating the identical cached request isolated reads. For output, they demanded a very long technical essay—up to 16,000 generated tokens—and measured how much of the weekly allowance disappeared.
Repetition produced curves from which the internal weights could be solved. Their observed agent workload was about 96% cache reads; the illustrative chat mix was stated, in rounded terms, as roughly 75% cached input, 10% uncached input, and 13% output tokens.
4. Cache-hit rate is an infrastructure moat hiding inside token prices
A production provider must preserve conversational state while serving thousands, tens of thousands, or millions of intervening queries: write tokens somewhere, keep hot state in HBM, demote it after a pause, then return the next request to the correct model deployment without leaving processing units overloaded or idle.
Time-to-live policy creates a direct user penalty. A cache might remain at the highest tier for five minutes or perhaps an hour; once an old conversation expires, restoring it through a full input or cache write can cost ten times as much—or more—than reading the live cache.
Max says early Anthropic ratios were roughly 1 for cache reads, 10 for input, and 50 for output, implying that Anthropic may have captured about 99% of the profit from cache reads. Competitive cuts have therefore targeted the highest-margin, highest-impact component: Astra still charged $1 per million cache-read tokens against $0.25 for Fable.
OpenRouter’s “effective price” is useful precisely because it incorporates an assumed cache-hit mix. The episode’s operational conclusion: high hit rates are not accounting trivia but difficult routing and memory engineering.
5. Anthropic wins the measured plans, though model quality remains disputed
On the $200 tiers, the tests yielded about 1.9 billion Astra tokens versus 3.1 billion Fable 5.1 tokens. Fable was also about 40% cheaper in API pricing in this comparison because its cache reads were 75% cheaper. Fable used only half of Anthropic’s allowance, leaving another 50% available for billions of Opus tokens—so the headline dollar chart understated Anthropic’s advantage.
Critics invoked Artificial Analysis figures of approximately $5 for a maximum-cost Opus 5.5 task versus $0.72 for a GPT-5.1-Codex task. Andrew’s pushback: the benchmark is imperfect, Opus 5.5 at medium roughly matches 6.1 Soul at X high, and that matched-quality cost gap is closer to 2–3x; against about 5x subscription value, Opus still delivers roughly twice the value.
The disagreement remains visible. Andrew calls Opus 5.5 a class above 6.1 Soul, while Max and Jordan stress that model preference is task-specific and personal. Andrew’s SemiAnalysis repository also automatically rejects pull requests unless the specified model is Claude Opus 4.5 or Claude Sonnet 4.5.
6. Model ownership, utilization, and task quality decide the durable economics
Moonshot appeared weakest among the cited Chinese plans at roughly 7x API-equivalent value, while some peers landed around 12–15x, near OpenAI. None offered such overwhelming value that users should abandon OpenAI or Anthropic for a weaker model; Meta’s $50 plan was highlighted as yielding about $2,500 of Spark usage, potentially 13 billion monthly tokens.
Max believes Anthropic utilization cannot be materially above 10% if leaked financials are to reconcile, leaving subscription margins around 40–70%. Power users with multiple accounts dominate online discussion, but corporate seats that consume only a fraction of their apparent API value can still satisfy both buyer and vendor.
OpenAI’s 50% plan-limit cut was announced around 9 p.m. on the eve of DevDay, surrounded by positive releases and softened by a one-month grace period. The speakers expect the response may change once grandfathered users feel the lower caps; they also emphasize that every estimate is point-in-time and could shift sharply with new models or pricing.
Token efficiency itself resists benchmarking. Andrew says a model such as Astra may complete the immediate task faster, while Opus or Fable may perform refinements immediately, producing a cleaner long-run codebase; the leaderboard cannot cleanly separate model quality from operator skill or quantify “how much of anything you do.”
The open-versus-frontier split ultimately concerns task demand. Simpler work can rationally move to cheaper open models, while frontier economics require new high-intelligence opportunities to grow faster; Andrew’s thought experiment asks whether the economy could absorb “100 million super-smart PhDs” at high returns.
Jordan’s cybersecurity experience shows both sides: refusals pushed him to GLM-5.3, whose existing context then handled an ordinary dashboard PR with far fewer tokens. Jordan’s conversations with well-known hackers suggested unlocked 5.6 Soul, Astra 6 Cyber, or Mythos 5.1 could be far stronger; Andrew separately said frontier-lab researchers may be genuinely concerned about cyber capabilities, while the exchange also raised—and muddied—the “regulatory grab” interpretation.