Estimate AI cost per blog post, website page, documentation article, PRD, or shipped feature without thinking in tokens first.
Most AI pricing tools assume you already know your prompt and response token counts. Most teams do not. They want to know what it costs to write 20 blog posts, publish 50 SEO pages, draft 12 help-center articles, or support 6 product features this month.
This AI cost calculator turns real workloads into estimated input and output tokens using words, context, formatting, and revision assumptions. Extra revision rounds are treated as partial rewrites that also resend part of the existing draft back to the model, and reasoning-capable models can bill additional hidden thinking tokens on top of visible output.
If you already know your token counts, use the advanced token calculator. If you want the fastest path to a budgeting answer, stay on this page.
8 kickoff/alignment turns per month
6 sources reviewed per post at ~1200 words each
3 research turns and ~550 note words per turn
250 planning words and 200 formatting/meta output words per post
20% cache savings assumed on repeated source/context reuse
35% extra billed output assumed for reasoning-model thinking tokens
These workflow assumptions are baked into the preset for this use case so the front end stays simple while the estimate still includes kickoff back-and-forth, research, formatting, context reuse, and model thinking overhead.
Built-in web search and grounding tool-call fees are not yet added to the estimate. If your workflow uses provider search tools heavily, actual cost can still be higher.
Monthly input tokens
382,980
Visible output tokens
48,360
Estimated billed output
48,360 - 65,286
Derived from
8 posts x 1500 final words, plus 8 project setup turns, 3 research turns, 6 sources, formatting overhead, and 1 revision rounds
This workload uses about 294,600 prompt words and 37,200 visible response words per month before converting to tokens. Billed output can be higher for reasoning models because provider docs now count hidden thinking tokens too.
$0.24 per post · $2.5/M input · $15/M output
Billed output tokens: 65,286 (includes thinking overhead)
DeepSeek V3 is the cheapest option for this workload at $0.16/mo. Compared with GPT-5.4, that saves $1.78/mo.
Pricing note: MiniMax M2.5: Official MiniMax pay-as-you-go rate for MiniMax-M2.5. Kimi K2.5: Estimated from recent public Moonshot references and third-party listings; verify against Moonshot billing before budgeting. Gemini 3 Flash: Official Google Gemini API rate for Gemini 3 Flash.
This page is designed for people who think in outputs like blog posts, pages, docs, and features. If you already know your token counts, use the advanced token-first tool.
Open the token-based LLM cost calculatorMost people do not budget AI work in tokens. They budget in outputs that matter to the business: how many blog posts need to go live, how many new landing pages the marketing team wants to publish, how many documentation articles support needs this quarter, or how many features the product team expects to ship. That is the gap this AI cost calculator is meant to close.
Instead of starting with input tokens per request and output tokens per request, this page starts with deliverables. You can model the cost of AI writing, AI-assisted product work, and AI content operations in units that mean something to a founder, marketer, operator, or product lead. Then the calculator translates those workloads into token estimates behind the scenes so the math still stays grounded in provider pricing.
That approach also makes planning conversations easier. When someone asks how much AI will cost to publish 20 blog posts per month or create 50 new SEO pages, you can answer in a monthly budget range instead of explaining why one million tokens is not actually a lot of work.
A useful AI content cost calculator should not stop at word count. A 1,500-word blog post is not just 1,500 words of output. There is also the content brief, the SERP context, the competitor outline, internal linking instructions, editing rounds, and final revision prompts. That is why blog post cost is usually higher than a naive word-count estimate suggests.
If your team publishes frequently, the right question is cost per blog post multiplied by monthly volume. A model that is only a few cents more expensive per post can become a meaningful line item across dozens or hundreds of articles. On the other hand, a slightly pricier model can still be a bargain if it reduces editing overhead or produces better first drafts.
Use the blog-post preset when you want to estimate AI writing cost for editorial calendars, SEO programs, content agencies, or founder-led publishing. It is especially useful when comparing whether a budget model is good enough, or whether a higher-end model saves enough human review time to justify the premium.
Website page production often looks simple from the outside, but page creation usually includes more context than people expect. Messaging frameworks, brand voice, product differentiators, conversion goals, SEO targets, and revision feedback all add prompt volume. That is why a realistic cost to create website pages with AI is not just based on final page length.
For SEO teams, this page is useful as a website page cost calculator because it models publishing in batches. If you are planning 10, 25, or 100 pages in a month, you can estimate the monthly AI budget before you choose a model family. This is much easier to explain internally than talking about per-token billing.
The website-page preset is a good fit for landing pages, feature pages, industry pages, comparison pages, and template-driven SEO page programs. If your workflow includes heavier research or stricter brand review, increase the context and revision assumptions to reflect the real process.
Documentation and help-center content usually carry more context than marketing pages because accuracy matters. The model needs product behavior, release notes, setup instructions, troubleshooting context, and edge cases. Even when the final article is not especially long, the input side can be substantial.
This makes a documentation cost calculator particularly valuable for support-heavy products. The marginal cost of using a stronger model may be acceptable if it reduces inaccuracies, back-and-forth, or the need to rewrite technical steps manually. If your docs team is scaling, those tradeoffs matter more than raw per-token rates.
The docs preset is designed for API docs, knowledge-base articles, onboarding help, release notes, and support workflows. It is also a good approximation for internal enablement content when you want to price AI assistance at the team level.
PRDs, product briefs, technical specs, and research summaries usually need more context and more iteration than content marketing assets. Teams often paste customer feedback, bug reports, analytics notes, and engineering constraints into the prompt, then revise multiple times as decisions sharpen.
That means the right planning unit is cost per spec or cost per PRD, not just output length. A 2,000-word spec may still be cheap if the prompt is simple. A shorter document can be more expensive when it requires many rounds of structured reasoning, prioritization, and rewrite instructions.
The PRD/spec preset is useful for founders, product managers, and operators who want a fast sense of AI planning cost across a month or quarter. It can also help teams decide whether premium reasoning-heavy models belong in the planning workflow or only in narrower high-value moments.
People increasingly ask a new kind of budgeting question: what does AI cost per shipped feature? That is not a token question and it is not a total engineering-cost question either. It is a workflow question. How many AI sessions does a team use while planning, coding, debugging, testing, and polishing one feature?
This calculator treats feature work as AI assistance spread across many sessions. Smaller features may need a handful of coding and debugging turns. Larger features may involve specification work, implementation help, test generation, bug fixing, migration advice, and release note drafting. The complexity presets give you a starting point without pretending the answer is universal.
If you run an AI-enabled engineering team, this framing is useful for forecasting budget, comparing model mixes, or explaining tooling cost to leadership. It is especially valuable when the user does not care about tokens at all and only wants to know how much AI support adds to the monthly software budget.
Providers bill in tokens because that reflects how language models process text, but most planning discussions start in words. A practical planning assumption is that one English word is about 1.3 tokens. That ratio is not perfect for every language, formatting style, or code-heavy workflow, but it is a solid default for estimation.
The important part is visibility. This page shows the derived input and output tokens so you can see the math instead of treating the estimate like a black box. If you later want more precision, you can take the derived totals and jump straight into the advanced token-based calculator.
That makes this page useful both as an AI writing cost calculator for non-technical users and as a bridge into more advanced token planning for operators who eventually want tighter budget controls.
There is no single best model for every workload. Cheap models usually win when the job is repetitive, high-volume, and easy to review, like publishing large numbers of SEO pages or drafting straightforward support articles. Premium models often make more sense when mistakes are expensive, reasoning depth matters, or the human review loop is costly.
That is why this page compares multiple models side by side. It lets you see the spread between low-cost options like Gemini 3.1 Flash-Lite, Gemini 3 Flash, DeepSeek V3, MiniMax M2.5, and Kimi K2.5 versus more premium options like GPT-5.4, Claude Sonnet 4.6, and Gemini 3.1 Pro. For some workloads the gap is small enough that quality should decide. For others the cheapest model can save thousands of dollars per month.
Use the current estimate as a scenario tool. If one model is 5x more expensive, ask whether it also removes enough human effort to be worth it. If not, the cheaper model may be the smarter operational choice even if it is not the benchmark leader.
These example scenarios show how a human-friendly AI cost calculator becomes more useful than a token-only tool. Instead of explaining token budgets to every stakeholder, you can anchor the conversation in real monthly output.
| Workload | Derived Tokens | Cheapest In Table | GPT-5.4 |
|---|---|---|---|
| 20 blog posts / month | 927,810 in / 107,900 out | Gemini 3.1 Flash-Lite — $0.14/mo | $4.5/mo |
| 50 website pages / month | 635,326 in / 118,755 out | Gemini 3.1 Flash-Lite — $0.11/mo | $3.73/mo |
| 12 docs articles / month | 261,994 in / 36,881 out | Gemini 3.1 Flash-Lite — $0.04/mo | $1.29/mo |
| 6 medium features / month | 107,640 in / 58,760 out | Gemini 3.1 Flash-Lite — $0.03/mo | $1.5/mo |
Start with the workload you actually care about: blog posts, website pages, documentation articles, PRDs, or product features. This calculator converts those deliverables into estimated prompt and response tokens using words, revisions, and context assumptions, then applies model pricing.
AI cost per blog post depends on draft length, revision rounds, and model choice. Lower-cost models like Gemini 3.1 Flash-Lite or Gemini 3 Flash can be dramatically cheaper than frontier models for high-volume publishing, while GPT-5.4 or Claude Sonnet 4.6 may be worth the premium for harder briefs or more editorial polish.
Website page costs usually come from shorter output than blog posts but still include briefing, brand context, messaging constraints, and revision prompts. The right estimate is not cost per token in isolation, but cost per page at your real monthly publishing volume.
Documentation often uses more input context than marketing copy because the model must ingest product behavior, source notes, release details, and support edge cases. That raises input token volume even if the published article is not especially long.
Yes. PRDs and specs usually have higher context requirements and more revision loops than lightweight content. This page treats them as deliverables with larger briefs, larger context windows, and multiple rounds of refinement so you can budget for planning work, not just output length.
On this page, AI cost per feature means AI-assistance cost, not total engineering cost. It estimates how much model usage is involved in planning, coding, debugging, testing, and polishing a feature across many AI sessions.
A practical rule of thumb is that one English word is roughly 1.3 tokens. This varies by language, formatting, and code, but it is a useful planning ratio for high-level budgeting. The calculator shows the derived token counts so you can inspect the assumptions.
For raw cost efficiency, Gemini 3.1 Flash-Lite, Gemini 3 Flash, DeepSeek V3, and MiniMax M2.5 are typically among the cheapest options in this calculator. They are often the best starting point when your workflow is high-volume and quality demands are moderate.
For complex product work, many teams still evaluate premium models like GPT-5.4, Claude Sonnet 4.6, and Gemini 3.1 Pro because the output quality, reasoning, and iteration speed can offset a higher token bill. The best fit depends on how expensive mistakes are in your workflow.
Use this page when the business question is about outcomes. Use the token calculator when the operations question is about prompts, completions, or request volume. For model selection, pair both with the quiz and the pricing directory.
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