For most content teams, Claude Sonnet 5 is the best value for article writing in 2026, at $0.0203 for a 1,500-word draft — high enough quality to edit rather than rewrite, without flagship pricing. If volume matters more than polish, GPT-5 mini drops that to $0.00400. Prices are list rates verified 2026-09-24.
Choose a model by the cost of a useful editorial draft—not token price alone. We weigh API cost against quality fit, latency and the human review each workflow still needs.
Read the same brief across five models before choosing. These are individual experiments, not measured editing-time rankings.
It is our best overall value for teams that want a polished first draft without paying flagship-model prices. Use a smaller model when volume matters more; move up to Opus only when the article is complex enough to justify it.
Costs use one 1,500-word draft, a 250-word brief and 20% prompt overhead.
The strongest balance of draft quality and mid-tier API cost for a normal editorial workflow.
Inspect this model’s unedited writing: How-to guide sample · Comparison piece sample · Technical explainer sample · Book chapter (fiction) sample.
Price this model →Low enough for volume while retaining more editorial headroom than economy models.
No published writing sample for this model yet. This pick is an editorial hypothesis, not a measured sample winner.
Price this model →A practical choice for briefs, outlines and first drafts that receive human editing.
Inspect this model’s unedited writing: How-to guide sample · Comparison piece sample · Technical explainer sample · Book chapter (fiction) sample.
Price this model →For complex, high-stakes pieces where draft quality matters more than token cost.
Inspect this model’s unedited writing: How-to guide sample · Comparison piece sample · Technical explainer sample · Book chapter (fiction) sample.
Price this model →Very low batch cost for scheduled pipelines that do not need an immediate response.
No published writing sample for this model yet. This pick is an editorial hypothesis, not a measured sample winner.
Price this model →Batch appears only where the provider publishes an eligible batch tier.
| Model | Best for | Quality fit | Latency | Standard | Batch |
|---|---|---|---|---|---|
| Claude Sonnet 5 | Balanced | High | Moderate | $0.0203 | $0.0101 |
| GPT-5.4 mini | Agency throughput | Good | Fast | $0.00907 | $0.00453 |
| Gemini 2.5 Flash | Rapid drafts | Good | Fast | $0.00499 | $0.00250 |
| Claude Opus 5 | Premium editorial | Very high | Slower | $0.0507 | $0.0253 |
| GPT-5 mini | Batch publishing | Good | Fast | $0.00400 | $0.00200 |
“Quality fit” and “latency” are transparent editorial assessments, not laboratory benchmark scores or provider service-level guarantees.
Value means more than finding the smallest token bill.
Published input and output rates applied to one consistent article workflow, re-checked weekly.
A qualitative workflow judgement—Good, High or Very high—not an independent factuality benchmark.
Fast, Moderate and Slower are relative expectations. Actual speed changes with length, reasoning, load and region.
A cheap draft can become expensive if it needs repeated generations or extensive human repair.
Open any scenario in the calculator to change its assumptions.
Four 1,500-word posts a month, one draft each.
A cost-conscious 100-post monthly production run.
Twelve 2,000-word pieces, three iterative drafts and a 500-word brief.
One hundred scheduled posts using the provider batch discount.
Token cost is the smallest line in the budget. Here is what happens when you add the only line that is actually large.
| Model | API | Editing | Editing cost | Total per article |
|---|---|---|---|---|
| Gemini 2.5 Flash | $0.00499 | 45 min | $37.50 | $37.50 |
| GPT-5.4 mini | $0.00907 | 35 min | $29.17 | $29.18 |
| Claude Sonnet 5 | $0.0203 | 20 min | $16.67 | $16.69 |
| Claude Opus 5 | $0.0507 | 15 min | $12.50 | $12.55 |
The editing times are illustrative, not measured. They are the assumption the table exists to expose — plug in your own team's figures and the ranking may change. What does not change is the shape: editing minutes dominate token cents at every realistic rate.
The model with the highest token cost finishes cheapest per finished article, because it is assumed to need thirty fewer minutes of work. Across 100 articles a month that is a difference of $2,495 — money that never appears on an API invoice and therefore never gets attributed to the model choice that caused it.
Every comparison page on the internet rates models. Most do not say where the rating came from. Here is ours.
Each provider's own positioning of a model within its lineup — which tier it sits in, what it is documented as being for, and how it is priced relative to its siblings.
Drafting long-form prose from a brief specifically. A model that is excellent at code or extraction may not be the same model at 1,500 words of continuous argument.
No rating here says a model is more accurate. Every model produces confident wrong sentences, and verifying them is human work at every price point.
There is no test suite behind it and no number you could re-derive. If you need that, run your own evaluation on your own briefs — it is the only assessment that reflects your house style.
Every figure on this page is computed from rates transcribed by hand from the five providers' own pricing pages and re-checked weekly. The judgements about quality and latency are opinion. We keep them clearly separated so you can take one without the other, and the methodology shows exactly how the priced half is derived.
Most advice on this topic points one way. These are the cases where it should not.
If the draft publishes largely as generated, the model is doing quality control it was not priced to do. Buy the better draft — it is still fractions of a cent.
Structural errors in a technical argument take longer to find and fix than surface errors in a general one. Repair time rises faster than the price gap.
At four articles a month the entire spread between cheapest and dearest is a rounding error. Optimising it is time you could spend on the brief, which affects output far more.
Cheaper models lean harder on a good brief. If the input is vague, the cheap draft degrades faster and the saving disappears into rewriting.
The rule of thumb: economy tiers are for workflows that already have an editor in the loop. They are a way to spend the budget on editing instead of tokens — not a way to skip the editing.
Every pick above is a per-token rental, so the bill rises with every article forever. Past a few thousand drafts a month that arithmetic starts losing to a machine you already have and an open-weight model running on it, where the marginal draft costs electricity. The catch is that the constraint stops being price and becomes VRAM — whether the weights and the KV cache fit on the card at your context length. RunMyLLM profiles that per GPU; price the API side here first, because the crossover is much further out than most people expect.
The picks above are five models out of 55. Each provider prices differently, and the differences decide more than the headline rates do.
Output averages 5.0Ă— input. Eligible Batch token charges are 50% off. Cheapest full draft: Claude Haiku 4.5.
Anthropic pricing calculator →Output averages 6.0× input. Eligible Batch token charges are 50% off. Cheapest full draft: GPT-5 nano.
OpenAI pricing calculator →Output averages 6.2× input. Eligible Batch token charges are 50% off. Cheapest full draft: Gemini 2.5 Flash-Lite.
Google pricing calculator →Output averages 2.4× input. Grok 4.3 and Grok 4.20 Batch is 20% off; other tracked Grok models do not support Batch. Cheapest full draft: Grok Build 0.1.
xAI pricing calculator →Output averages 3.5× input. No Batch tier; DeepSeek offers separate time-based off-peak pricing. Cheapest full draft: DeepSeek V4.1 Flash.
DeepSeek pricing calculator →Every rate for all 55 models is on the full pricing table.
The questions people actually ask before committing to one.
Neither wins outright; they win at different points on the price curve. OpenAI has the widest range, including both the cheapest tracked model and the most expensive. Anthropic has the most consistent structure — output is exactly five times input on every model — which makes budgeting predictable. For a draft an editor will lightly touch, a mid-tier Claude model is the common choice; for high-volume work an OpenAI mini model usually costs less.
The economy tiers — GPT-5 nano, Gemini Flash-Lite — produce text cheaply but need real editing, so they suit workflows with an editor already in the loop. If nobody is editing, a mid-tier model costs a fraction of a cent more per article and arrives far closer to publishable. The cheapest model is only cheapest when you ignore what happens after generation.
Usually it produces a draft that needs less structural repair, particularly on complex or technical pieces. It does not make the draft factually reliable — every model can produce confident, wrong sentences, and verifying those is human work regardless of model tier.
Consider Batch when your publishing schedule can wait for asynchronous results. Eligible Claude, OpenAI and Gemini charges are 50% off; Grok 4.3 and Grok 4.20 are 20% off. Other tracked Grok models do not support Batch. Compare supported model-specific rates, turnaround and retry requirements.
Less than people expect. Caching discounts input only, and an article workflow is overwhelmingly output — a 1,500-word draft is around 1,950 output tokens against a few hundred of input. Caching pays off when a large fixed system prompt or style guide is reused across many requests, not because you generate many articles.
That is an editorial policy question rather than a pricing one. The relevant test is whether your review process catches what the model gets wrong. If a cheap draft goes through the same verification a human draft would, the model tier matters less; if it is published largely as generated, the tier is doing quality control it was not priced to do.
One generated draft plus human editing is the most common working pattern. Multiple iterative drafts cost more than a simple multiple, because with iterative revision each redraft re-sends the previous draft as input — the calculator models this, and the methodology page shows the arithmetic.
The figures do, automatically — every number on this page is computed from the same price data the calculator uses, so a re-verification updates the prose. The editorial judgements about quality fit and latency are reviewed separately and are not recomputed.
API generation is only one line in the budget. Research, source verification, editing, subject-matter review, images and publishing remain separate costs. So does being found: a draft only earns back its cost if search engines and AI assistants are allowed to read it, and a robots.txt that admits Googlebot, GPTBot and ClaudeBot costs nothing but is easy to get wrong.