Claude Opus 5.5 vs GPT-6.1 Sol: The 5x Subscription-Value Gap, Measured

Two hundred dollars a month buys either a Claude Max 20x seat or a ChatGPT Pro 200 seat. On the same agentic workload, one of them returns $11,726 a month in first-party API-equivalent tokens and the other returns $2,084 — a 5.6x gap between Claude Opus 5.5 and GPT-6.1 Sol. Neither of those models is what makes this news, and neither is new: Claude Opus 5.5 shipped on September 22, 2026 and GPT-6.1 Sol on September 29, 2026, so the models themselves are two weeks and one week old respectively. What is new is that the subscription economics of running them now have a measured number attached, published by SemiAnalysis on October 5, 2026 — one day before this piece — after the firm built a method for pricing limits the vendors do not publish.

The headline figure has been circulating on X since the evening of October 5, usually stripped of its caveats. So the caveats go up front, because the number is worthless without them. Every value in the chart below is one research publication's model of somebody else's private meters, measured over a defined workload shape and reported as a range, not an audited benchmark. The plan prices are the vendors' own published list prices. The capability scores referenced later are Artificial Analysis's, in named configurations. And the whole comparison is about subscriptions — a billing mode with its own rules — not about what either model costs per token, which is a different question with a different answer.

How you price a plan you cannot see inside

Anthropic and OpenAI both expose subscription usage as a percentage meter, not a price. There is a five-hour window and a weekly limit, and no per-request invoice. The vendors differentiate tiers by multipliers relative to their own other plans — Claude's Max 5x and Max 20x are literally named for their position against Pro, and ChatGPT's tiers were advertised the same way, until OpenAI removed the relative usage language from its pricing page.

The same plan therefore has different value depending on which model you spend it on, because a credit consumed by a cached input token and a credit consumed by an output token are not priced in the same ratio the vendors charge at the API. That is the whole thesis of the exercise: you cannot say a plan is worth $X on its own. You need the plan, the model and the workload together.

SemiAnalysis's method is worth describing because it determines how much weight the numbers can carry. The firm runs repeated calls designed to isolate one token type at a time — a portion of War and Peace with a random tag per call for uncached input, the same prompt marked for caching for cache writes, a fixed tag for cache reads, a long technical essay to force output. Each call records tokens billed by the vendor and the position of the usage meter. Because a single request rarely moves a meter at all, they measure in steps: the token totals between two meter movements give the cost of one meter unit, partial steps at each end are discarded, and steps accumulate until the range is within ±5%. Cache reads that do not move the meter after 500 million tokens are treated as free. The workload mix then comes from the firm's own September usage ratios, and the value is the resulting token capacity multiplied by the vendor's published API price for each token type.

The most interesting finding in the methodology section is not about either lab's generosity. While validating, SemiAnalysis found that one of three accounts it tested on the same plan had limits about 20% lower than the other two — an older account. The provider confirmed it was an "extremely tiny" A/B test on limits and emphasised that it had not cut limits wholesale. Both halves of that matter: it proves subscription limits can be moved silently at any time, and it proves the method is sensitive enough to detect a 20% move when it happens.

What each tier returns, at $200, $100 and $20

Three tiers, both labs, agentic workload, API-equivalent value per month at first-party list rates:

• $200 a month — Claude Opus 5.5 on Max 20x returns $11,726 — 58.6 times the fee in API-equivalent tokens; GPT-6.1 Sol on ChatGPT Pro 200 returns $2,084 — 10.4 times the fee. Ratio: 5.6x.

• $100 a month — Claude Opus 5.5 on Max 5x returns $5,725 (57.3 times the fee); GPT-6.1 Sol on ChatGPT Pro 100 returns $1,055 (10.6 times the fee). Ratio: 5.4x.

• $20 a month — Claude Opus 5.5 on Claude Pro returns $1,178 (58.9 times the fee); GPT-6.1 Sol on ChatGPT Plus returns $211 (10.6 times the fee). Ratio: 5.6x.

That the three tiers land between 5.4x and 5.6x is the finding, not the individual dollars. The comparison is being run on the mid-tier of each lab's catalogue — the model both vendors position as the everyday driver rather than the flagship — and it is being run across the whole price ladder, so a reader on a $20 plan gets the same answer as a reader on a $200 one. SemiAnalysis's own summary is blunt: at this tier, Anthropic is "an overwhelmingly better deal, offering ~5x the API-equivalent value across the board."

The workload shape behind all three rows is labelled, and it is worth reading twice: agentic, 0.4% uncached input, 96.6% cached input, 2.6% cache writes, 0.3% output. That is what an agent loop looks like — a small amount of fresh instruction, a huge amount of re-read context, and a sliver of generated text. It is also, as the next section shows, the mix that flatters Anthropic most.

Opus 5.5 costs twice as much per token, and still wins by 5.6x

Here is the thing the circulating version of the chart leaves out. Claude Opus 5.5 is not the cheaper model. It is the more expensive one, by a factor of two.

Both rate cards are public. Claude Opus 5.5 lists at $4.00 per million input tokens, $0.20 per million cache reads, $5.00 per million cache writes and $20.00 per million output tokens. GPT-6.1 Sol lists at $2.00, $0.10, $2.50 and $10.00 for the same four lines. Weight those by the agentic mix above and the blended rates come out at roughly $0.399 per million tokens for Claude Opus 5.5 against $0.200 for GPT-6.1 Sol — a ratio of 2.00x. That arithmetic is ours, from the two vendors' published cards, but it is not controversial: it follows directly from the published prices and the labelled mix.

Divide the 5.6x value gap by the 2.0x price gap and you get roughly 2.8x more tokens per dollar of plan on the Claude side. The same division at the $20 tier gives 2.79x — the gap is one meter being far more generous, almost exactly uniformly across the ladder, not one model being cheaper to run. Which means the 5x headline is a statement about Anthropic's credit pricing, and it holds only for workloads that look like the one measured.

Change the mix and the number moves. The mix is 96.6% cached reads, where Opus 5.5 charges $0.20 against Sol's $0.10. A cache-light workload — long fresh documents, little reuse — leans on the input line and the same 2x shows up there. Output-heavy work, like long generations rather than long agent runs, hits the line where Opus is $20.00 against $10.00. And SemiAnalysis is explicit that its own preferred metric has a failure mode: API-equivalent value "will of course be misleading" if a model is a particularly good or bad deal at API prices, which is precisely the situation here — the more expensive-per-token model is the one with the better subscription. The firm also declines to lean on token-efficiency charts, saying it does not believe typical agentic benchmark tasks are representative of real work, and that the industry lacks reliable efficiency data. Take the 5x as a measured statement about one workload shape from one publication, not as a permanent property of either model.

What makes the gap an actual problem rather than a neutral trade is that the subscription-and-cheaper side is also the stronger side on the independent board. Artificial Analysis puts Claude Opus 5.5 at 58 on its Intelligence Index in the configuration its model page titles "max with fallback", listed in the variant selector as "Claude Opus 5.5 (Max, Default Fallback)" — the highest it had measured by several points. GPT-6.1 Sol reads 51.8 at maximum effort on the same index, index version v4.3.2, with GPT-6 Astra at 52.7. So a buyer weighing the two $200 plans is being asked to pay 5.6x more per dollar of usable capacity for the lower-scoring model. The scores are third-party and independent; the value figures are SemiAnalysis's model. Both are labelled, and neither is a vendor number.

What OpenAI changed on September 29

None of this was static. The reason the gap is being discussed this week rather than last is that OpenAI moved its own meters eight days before the measurement landed.

On September 29, 2026, OpenAI reopened $200 Pro subscriptions to new subscribers and changed how usage is calculated alongside the reopening. In its own words that day, the change would "net out at half the dollar in API spend compared to the old Pro $200 plan," and a second post the same day described halving the API-equivalent value of the tier. That is a vendor statement about its own plan, dated, and it is the event that made a subscription-value measurement worth publishing.

Three consequences, as SemiAnalysis measured them:

• Tokens per tier were halved, and Sol-class value fell further than that. OpenAI cut cached input pricing for GPT-6.1 Sol at the same time — to $0.10 per million, which OpenAI's developer account described on September 29 as 95% below standard input pricing and 50% below GPT-6 Sol's cached rate — so the measured API-equivalent value of Sol-class models on the plan dropped by more than 50% rather than the 50% the token cut alone implies.

• A $500 tier arrived, and it does not restore the value. The new Pro 500 plan offers 21% more GPT-6 Astra than the old $200 plan did; because of the Sol price cut, Sol-class API-equivalent value on the $500 tier actually went down. Ultrafast mode — the 300-tokens-per-second service tier — is the real addition there, and SemiAnalysis says its limits are still being tested.

• Existing subscribers are grandfathered until October 29, 2026. A $200 plan held before the cut keeps the older, larger allowance until that date and then moves to the lower one at an unchanged price. For anyone who bought in before September 29, the comparison in this article applies to their account from the end of the month, not today.

The rebalancing is the subtle part. Before the cut, OpenAI's per-dollar value was lopsided in its own favour at the top: Pro 200 offered roughly twice the per-dollar value of Pro 100 on Astra, which itself offered roughly twice Plus. After the cut, Pro 100, Pro 200 and Pro 500 all give the same tokens per dollar for every model, and Plus is comparable on Sol though still relatively worse on Astra. OpenAI made its ladder internally fair by halving everything to the bottom rung. Anthropic, by contrast, already gave the same per-dollar value at every tier — which is why the same 5.6x shows up whether you look at a $20 plan or a $200 one. If you are on Plus and were counting on Astra access, the cut removed the value that used to make the highest OpenAI tier worth reaching for.

OpenAI does retain one real structural advantage, and SemiAnalysis grants it: none of the Pro tiers carry a five-hour window, which makes it easier to consume a high share of the monthly limit in a single long session. The firm's judgement is that it does not offset a gap of roughly 4x to 5x on API-equivalent value.

Two economies, one model name

There is a trap in reading any of this as a statement about the models. If you pay per token — an API key, a self-hosted stack, an agent platform you build — the 5.6x is not your number and never was. Your number is the blended 2.00x, and on that basis GPT-6.1 Sol is the cheaper model for the same agentic loop. The subscription comparison answers "which seat should I buy." The API comparison answers "which model should I call." They point in opposite directions for these two models, and conflating them is the single most common error in the commentary around the chart.

The round of price cuts that fed the subscription measurement is already in the API rate cards. Anthropic cut Opus 5.5's input and output pricing 20% against Opus 5 and cache reads 60% when the model shipped, on September 25. OpenAI cut GPT-6.1 Sol's cached input in half at launch on September 29. Those are the two list cards used throughout this article, and they apply whether you buy a seat or a token. Both models sit on the OrcaRouter catalogue at the vendor's list price — Claude Opus 5.5 at provider list price with 0% markup, and GPT-6.1 Sol on the same key behind one endpoint — so a vendor cut is live at the same time it is published rather than waiting for a routing-table refresh.

One thing worth being plain about: OrcaRouter does not sell subscriptions, so this measurement does not change what an OrcaRouter user pays. It changes which model they should be paying for, and it prices the alternative. The plan side of the comparison is a wall you do not control — two labs, both of which have demonstrated they will move limits, silently and at short notice, in the same week they cut prices. The API equivalent of that risk is being able to send the same request somewhere else without a new contract, which is what automatic failover across providers is for. If you are running an agent loop at 96.6% cached reads, the thing you actually want is the cheapest route that will accept the request, and the ability to change your mind when a vendor changes its mind.

Who should move, and who should not

If you are paying for a Claude subscription and running agentic coding or long tool loops, the case for staying is now measured rather than anecdotal: 58.6x your fee comes back as API-equivalent capacity on the $200 tier, against 10.4x on the comparable ChatGPT plan, and it holds at every price point. If you are on a ChatGPT Pro 200 plan purchased before September 29, you have the older allowance until October 29, 2026 and the lower one after — check the date before deciding anything, because for the next three weeks you are comparing against a plan that no longer exists for new buyers.

If you are an API builder, ignore the 5.6x. Route on the 2.00x blended rate, price it per task rather than per token, and re-check it after every vendor price change, because this quarter produced four of them — GPT-6 Sol, Fable 5.1, Opus 5.5 and GPT-6.1 Sol in a little over a month. The measurement that matters for a subscription is a meter nobody outside the lab can see. The measurement that matters for an API is a rate card, and that one is public.

What to watch next: whether Anthropic's Opus limits hold through the quarter, given that its own competitor spent the last week cutting and that both labs retain the ability to move meters quietly; whether OpenAI restores Sol-class value on the $500 tier by raising limits rather than by cutting prices again; and whether the $200-tier grandfathering actually ends on October 29 as published. SemiAnalysis maintains the underlying numbers as a live dashboard rather than a fixed study, so the figures quoted here are the October 5, 2026 state of a dataset that is designed to move.