Plans & pricing

A single AI model can have blind spots and quirks. Consensus is the answer to that: instead of one model, a panel of models from different AI makers each analyses the article independently, and an editor model then merges their findings into one result. The rule is strict: a finding only counts as agreed when at least two models independently support it, and this is enforced automatically, not left to the AI's judgement. What a single model raised is not thrown away — it is shown separately, under its own heading, with that model named.

• What you get: the findings the panel agrees on, each labelled with how much of the panel backed it ("3 of 4 models"), followed by a clearly separated set of observations raised by one model — models read different parts of an article closely, so one voice there is a narrower reading, not a discredited one. A note at the bottom names the exact models the judgement rests on. If the models agree on nothing, an empty agreed set is the honest outcome: the panel found no issues in common.
• What a "group" is: the maker of the model — Anthropic, OpenAI, Google, DeepSeek, Z.ai (GLM), Moonshot AI (Kimi), xAI — and the panel takes one seat per maker. Two models from the same maker share training data and habits, so counting them as two opinions would overstate the agreement; grouping by maker is what makes "two models agreed" mean two independent readings.
• Who is on the panel: a run seats three makers. Analyses of the article already cached from a maker's model fill that seat free — and every covered maker joins, so a well-analysed article can seat more than three at no extra cost, while a run never buys a fourth. Empty seats are filled by the fixed representatives Claude Haiku 4.5, GPT-5.4 Mini, Gemini 3 Flash and DeepSeek V3.2, in that order; DeepSeek V4 Pro performs the merge and never sits on the panel it judges. This is the current line-up and will change as models change; every result names the models that actually sat on its panel.
• Where to find it: press Consensus beside the model picker. It is a run mode, not a model: pressing it opens a setup card showing the panel seats and what the run costs, and nothing is charged until you run it. It's offered on the analyses whose findings come from a fixed catalogue — Bias, Persuasion, Legal, Moral, Scientific — because counting agreement needs findings the code can match across models. Omissions and Critique produce free-form claims and suggestions, so there is nothing to match; interpretive modes like Intent, Background, Parallels, Rewrite and Roast have no agree/disagree concept.
• What it costs: you're charged for the models that actually run. Panel members whose results are already cached for the page are re-used free, so a consensus on a previously analysed article is often much cheaper — and when three makers are already covered, the only thing left to pay for is the merge. The setup card itemises the seats and the price before you run. Members that do run become available to everyone afterwards as ordinary single-model results.

A consensus never ships on fewer than three models. If a panel member fails on an article whose cache covered more than three makers, the remaining models still give you a consensus, clearly flagged as a partial panel; on a three-seat run there is nothing to fall back on, so the analysis reports the failure and you can run it again.

A cached consensus is never silently rewritten. If the article later gains analyses that would add a maker the panel never had — or a stronger model in one it did — the result offers a Strengthen button naming exactly what it would add and what it costs. In practice that is just the merge pass — everything a rebuild seats is already cached, so nothing new has to be run. Consensus results are a strong default when you care about reliability; a single-model run remains the right choice when you want a specific model's take or the fastest, cheapest answer.

Every analysis of a web article is shared with the community — except text you paste in directly, which is stored privately under your account (the page URL is never sent), so neither the text nor its results are ever added to the community feed. Cached results are free for every signed-in user, on every plan, and don't count against your monthly analyses — so when you open the extension on a page someone has already checked, you get the analysis instantly, at no cost. The more people use BiasChecker, the more of the pages you read are already covered.

Every analysis is saved against the article and the model that ran it, so re-opening one that already exists is free — it doesn't use an analysis. Running something genuinely new — a different article, a different analysis type, or the same analysis on a different model — still counts as normal.

In the browser extension you can turn on Prefer cached results (Settings → AI Model Preference). With it on, opening or switching an analysis loads an existing cached result for the page instead of running a fresh, paid one — a simple way to make a trial or a credit pack last longer.

• If the model you've selected already has a cached result for the page, that one loads.
• If it doesn't, the extension loads the best result that is cached (most capable model first), rather than nothing — so your default-model choice never hides analyses that other models have already produced.
• Only when nothing is cached for that analysis does it run a fresh one, using the model you selected.

So you don't need to keep changing the model dropdown to browse what already exists — change it only when you specifically want a fresh run on a particular model. Anyone who has used up their allowance or credits keeps free access to cached and community results — on every plan, with no time limit.

And it's not only your own analyses: when someone else has already run an article, that cached result is shared with you too. With Prefer cached results on, simply opening the article you want is usually enough to pick one up — wherever it's published — so you don't need to go looking for it. The extension's Community Analyses page is a handy window into some of what's already out there, but it's a curated, partial list, not everything that has been analysed.

Upgrading takes effect immediately — your new, larger monthly allowance is available as soon as the payment goes through, and your renewal date stays the same.

You only pay the difference, not a full new month. We credit the unused part of your current plan and charge the new plan for the rest of the current billing period, so the amount taken today is prorated — usually small. From your next renewal onward you simply pay the new plan's normal monthly price.

Whatever you've already analysed this month carries over: your bigger allowance applies to your existing usage rather than starting a fresh one on top of it. For example, if you've nearly used up a Lite allowance and upgrade to Pro, you get Pro's larger allowance with what you've already used this month counted against it — leaving the remainder for the rest of the period. The full new-plan allowance refreshes on your normal renewal date.

Moving to a lower tier works the other way around — see the next question.

You downgrade right on the pricing page — sign in, pick the lower tier and confirm (the plan buttons only appear as "Switch" once you're signed in). The change is scheduled for your next billing date, not applied immediately:

• Until your next billing date, nothing changes: you keep your current plan, its full monthly allowance, and all its features. You already paid for this period, so you keep what you paid for.
• Nothing is charged today, and no refund is issued for the current period.
• From the next billing date, you're billed the lower plan's price and get its allowance. Your renewal date doesn't change.
• Changed your mind? You can cancel the scheduled change any time before it takes effect — pick your current plan again on the pricing page (or use the "Keep my current plan" link in the banner). Reverting is free.

While a downgrade is scheduled, your dashboard shows "Changes to [plan] on [date]" so there are no surprises. Any unused credit packs are unaffected — they stay yours on any plan.

Downgrading is different from cancelling: a cancellation ends the subscription entirely at the period end, while a downgrade keeps you subscribed on the smaller plan with no gap and nothing to remember.

Yes, in one specific case. If you use up your trial allowance before the 14-day trial window ends, you'll see a one-time offer in the extension and on your dashboard: subscribe before your trial ends and get 15% extra allowance in your first month.

• The bonus is 15% of whichever plan you pick (for example, Lite's 3M-token allowance earns a 450K-token bonus), granted automatically as bonus credits when your subscription starts — no code needed.
• It appears on your dashboard as a "First-month bonus" alongside any credit packs, and is used automatically after your monthly allowance runs out.
• It's first-month only: whatever is left of the bonus expires at your first renewal.

The offer ends when your trial does — if the 14 days pass, or you never used up the trial allowance, the normal plans apply.

In tokens — the small chunks of text a model reads and writes (roughly ¾ of a word each). A standard analysis reads the article and writes up its findings, which comes to about 5,000 tokens for an average-length article on a 1× model — that's where “~5,000 tokens per analysis” comes from (a longer article reads more, so it costs more). We scale the actual tokens by the model's tier (1× standard up to 10× frontier) — we call that your “tokens of work” — so a frontier model costs ~10× as many tokens for the same article.

The cost comes out of your monthly allowance first, then your credit packs (the Power pack is 5,000,000 tokens — about 1,000 standard-model analyses, or ~100 on a top tier). Cached community results are always free, on every plan — you only ever pay for a fresh analysis. See the model tiers page for multipliers.

An analysis's real cost depends on the article's length and the model you choose, so we charge the actual tokens used instead of a flat per-analysis fee. A flat fee would either be set low (and lose money on long, premium analyses) or set high (and overcharge you on short ones) — charging real usage is fairer, and a short article on a fast model can cost a small fraction of a heavy one.

The analysis counts we quote are honest estimates from typical usage; your token balance is always exact. Because you only pay for the tokens each analysis actually uses, the same allowance or pack can stretch to more or fewer analyses than the number shown — and the estimate gets more accurate the longer you use BiasChecker, as it learns from your own reading. Sign in and the estimates on the plan and pack cards update to your own usage.

Lite, Pro, and Premium plans reset monthly on your billing date. The trial expires after 14 days.

Your allowance refills at the start of each billing cycle. If you run out mid-month, a credit pack (from $15 for ~500 analyses / 2.5M tokens) tops you up instantly — credits are only consumed after your plan allowance. And articles the community has already analysed stay free on every plan, so popular news often costs you nothing at all.

Yes — buy a credit pack and use it at your own pace, no subscription required. Packs are the same price for everyone, but subscribers get more for it: an active subscription adds bonus credits to every pack (Lite and Pro +25%, Premium +50%). As a pay-as-you-go user you buy packs at the base amount, your credits fund each fresh analysis, and you don't get a monthly quota. Cached community results are free for everyone. For regular use, a Lite subscription ($8/mo) adds ~600 monthly analyses and the pack bonus. View credit packs →

You can get your money back within 14 days of any purchase, no reason needed, wherever you live. Because access starts immediately, the refund may be reduced by the value of what you have already used, priced at what you paid for those credits. Bonus credits are a gift rather than a purchase, so they carry no cash value and are not refunded. One window covers everything, subscriptions and credit packs alike. Separately, if an analysis comes back unusable we put the tokens back rather than have you pay for it, so just tell us. Refunds are processed by Stripe (Link), the merchant of record; cancelling a subscription stops future charges. See our refund policy for details.