Claude plans give about 5x the value of OpenAI’s: how to pick a model and spend fewer tokens

The hardest question when you pick an AI coding subscription is “how much can I actually use?” Claude and ChatGPT both show a 0-to-100% meter and never say how many tokens that is. On October 5, 2026, the research firm SemiAnalysis published a limit test that converts those meters into dollars: on the models each company pushes as its daily driver, Claude plans give about 5x the API-equivalent value of OpenAI’s. This piece turns that result into three things a developer can use: how to choose a model, how to spend fewer tokens, and what the test’s own method teaches. It also says why we default to Claude, and what that choice depends on.

Key points

• The gap is in the mid tier: on the $200 plans, GPT-6 Astra is worth about $2,897 at full use and Claude Fable 5.1 about $2,485, which is close. But for Opus 5.5 against GPT-6.1 Sol, Claude plans come out around 5x ahead (SemiAnalysis, October 5, 2026).
• Limits are not constants: OpenAI halved the tokens on its $200 tier around its September 29 DevDay, and SemiAnalysis caught an A/B test that left one of three identical accounts about 20% short.
• Cache reads dominate agent usage: coding agents keep resending old context, so whether the cache hits matters more to your meter than rewording a prompt.

How the test works

SemiAnalysis’s method is worth understanding first, because it decides how far the result transfers to your usage. Subscription plans publish no token counts, only percentage meters over a 5-hour window and a 7-day window. The team measured one token type at a time: send the same prompt template repeatedly, record how many tokens the provider billed and where the meter landed, then work out how many tokens it takes to move the meter one notch.

There are four token types: fresh input, cache writes, cache reads and output. Input runs put a random tag on every call so nothing caches. Cache-write runs mark the prompt for caching. Cache-read runs reuse a fixed tag, so the first call writes and every repeat reads. Output runs use a technical essay to force long answers, because models refuse mechanical requests like “repeat this word 100,000 times”. After dropping the partial first and last steps, they keep adding steps until the error band is within ±5%. Finally they blend the per-type rates using their own September agent usage mix and multiply by API list prices to get the “API-equivalent value”.

Two lessons follow for developers. First, a plan’s value depends on the (plan, model, workload) tuple; the report says a plan can’t be priced in isolation. Second, the workload is agentic coding with heavy cache reads. If you mostly chat in a web window, your experience will differ.

Choosing a model

Per Anthropic’s pricing docs (checked October 8, 2026), the Claude tiers cost the following. The GPT-6.1 Sol row comes from OpenAI’s launch page, covered in our GPT-6.1 Sol report:

Model	Input	Output	Cache hit
Claude Fable 5.1	$10 / MTok	$50 / MTok	$0.25 / MTok
Claude Opus 5.5	$4 / MTok	$20 / MTok	$0.20 / MTok
Claude Sonnet 5.5	$2 / MTok	$10 / MTok	$0.10 / MTok
GPT-6.1 Sol	$2 / MTok	$10 / MTok	$0.10 / MTok

Inside a subscription the rules differ from this table. SemiAnalysis found that Fable can use at most half of a Claude plan’s limit, leaving the other half for other models, and that Sonnet 5.5 and Opus 5.5 are worth about the same while Fable 5.1 is clearly less. That suggests a simple split:

• Opus 5.5 for daily coding: Anthropic positions it for long-running agentic work, and it is the best value inside a plan. Its launch cut API prices and raised subscription limits.
• Fable 5.1 for the hard problems: architecture calls and stubborn bugs, where one wrong turn costs hours. Don’t spend that scarcer allowance on routine tasks.
• Sonnet 5.5 for bulk, simple work: it is close to Opus in plan value but half the API price, so scripted calls are cheaper.

On pay-per-use API billing the logic is plain unit price times volume. Sol and Sonnet 5.5 list at the same price, and on Terminal-Bench Science OpenAI reports a per-task cost of $5.47 for Sol against $23.21 for Opus 5.5 (OpenAI’s own figures). So “Claude plans are the better deal” is about plan allowance, not about every task being cheaper.

Why we still default to Claude

First, the boundary: what follows is the editorial team’s day-to-day experience, not a SemiAnalysis finding, and we ran no controlled comparison. Our impression is that Claude needs fewer rewrites on code and gets multi-step tasks right more often, so one requirement takes fewer rounds. A higher unit price doesn’t necessarily mean more total spend, and with a roughly 5x plan-value gap on top, the choice isn’t hard.

The judgment has two gaps, and we’d rather name them. One: SemiAnalysis says plainly that the industry lacks reliable token-efficiency data, and it doesn’t consider the commonly cited Artificial Analysis index representative of real work. “Which model uses fewer tokens for the same job” therefore rests on your own experience for now. Two: none of OpenAI’s Pro plans has a 5-hour limit, so it’s easier to use a larger share of the monthly allowance. SemiAnalysis doesn’t think that offsets Opus 5.5’s roughly 4x higher value. If your work is overnight batch jobs, run that math yourself.

There is also a shift in reputation. OpenAI changed its plans at the end of September; the start of that change is in our Pro tier report. Indie developers used to praise OpenAI’s generous limits, and by SemiAnalysis’s measurements that is no longer true.

Spending fewer tokens

Everything below follows from one fact: cache reads make up a large share of agent usage, and the price gap is wide. For Opus 5.5, fresh input costs $4 per million tokens and a cache hit costs $0.20, a 20x difference.

1. Make the cache hit. Anthropic’s caching docs list what to check: tool_choice, images, the thinking configuration and output_config.effort must stay the same between calls, and calls must land within the cache lifetime (5 minutes by default; a 1-hour cache costs 2x the base input price to write). Opus 5.5’s minimum cacheable prompt is 512 tokens. Put stable material (system prompt, tool definitions, project rules) first and volatile material last.
2. Match thinking effort to the task. Output is the most expensive token type. Our Opus 5.5 report notes that the default effort is medium and that developers flagged high output use at max effort. Try a small batch before running a large one.
3. One session per task. A long session re-reads its history every turn; cached reads are cheap but not free. Write the conclusions into a project doc and start fresh.
4. Plan where it doesn’t draw down the allowance. ChatGPT’s desktop Chat mode doesn’t consume Codex quota; the details are in spend your Codex quota where it counts. We haven’t verified an equivalent free mode on the Claude side, so we won’t claim one.
5. Watch both windows. The 5-hour window resets many times a week and the month is capped by the weekly limits. Start heavy jobs right after a reset, not at the tail of a window.

Limits change without notice

The most interesting passage in the SemiAnalysis report is the A/B test it caught. Of three identical accounts, one had about 20% lower limits, and it was also the oldest. The provider told them it was an “extremely tiny” test of how to balance the moment people hit limits, and stressed it hadn’t cut limits wholesale. Two things follow: providers can change limits silently, and careful measurement can see it.

List changes are just as frequent. OpenAI cut its $200 tier from 20x Plus to 10x and added a $500 Ultrafast tier; accounts bought earlier keep the old limits until October 29. After the change, SemiAnalysis found OpenAI’s $100, $200 and $500 plans give the same tokens per dollar, while Anthropic’s tiers always did.

The two companies are reducing subsidies differently. SemiAnalysis estimates subscriptions are about 10% of Anthropic’s revenue but over 40% of its inference compute. Anthropic gives less on its pricier models; OpenAI lowered every model together. The practical reading for developers: today’s best deal may not be tomorrow’s, and the value of an Opus 5.5 plan actually slipped a little after the API price cut.

Advice for developers

• Skip long prepayment: providers change limits whenever they like, so keep monthly billing and an exit.
• Keep your own ledger: you don’t need SemiAnalysis’s rigor, but record weekly what percentage a fixed task burns, so a silent change shows up quickly.
• Read plans as tuples: judge (plan, model, workload), not the multiplier on the pricing page. OpenAI just removed those multipliers altogether.
• Don’t plan around resets: Tibo, who leads Codex at OpenAI, promised that over the next 28 days it will either ship an improvement or reset limits for everyone each day. Good news, but not a resource to schedule against.
• Route models by task: Opus for daily work, Fable for the hard problems, Sonnet for bulk jobs, then calibrate with your own experience instead of leaderboards.