The cheapest AI model for writing, ranked by price

The cheapest AI model for writing can be named cleanly with one well-chosen measure. That measure is the OUTPUT price, the price of the tokens the model writes. On today's official price lists two champions share the top spot. Claude Haiku 5.5 and GPT-6 Luna both charge $0.50 per million tokens produced, an exact tie. A 1,000-word article comes to less than a tenth of a cent on either one, brief included.

What follows is the full ranking from cheapest to most expensive, with the typical article costed on each model. The quality question gets an honest answer, including the specific case of writing in French. Then comes the setup that reconciles price and quality, with the floor model for drafts and the tier above for the polish. Official prices are dated, every calculation can be redone on a calculator, no score is invented and the limits of floor models are stated plainly.

01Key takeaways

• For writing, the measure that counts is the output price. The text produced drives the bill far more than the brief does.
• Today's champions are Claude Haiku 5.5 and GPT-6 Luna ($0.50/M output), just ahead of Mistral Small 4 ($0.60/M), as the official price lists show.
• A 1,000-word article costs from 0.09 cent on the floor models to 2.7 cents on a high-end mid-tier model.
• Cheaper doesn't mean worse on routine work. A blind test on your own texts settles it in one evening.
• The winning setup routes drafts to the floor model and keeps a higher tier for the published polish.

02Quick answer, today's champions

The raw champions are Claude Haiku 5.5 and GPT-6 Luna, both at $0.10 per million input tokens and $0.50 for output. Anthropic applies that rate to prompts of up to 100,000 tokens, which covers any article brief. Both also bill cached instructions at one cent per million tokens. Right behind them sits Mistral Small 4 at $0.15 and $0.60, which adds a European base and a solid reputation in French, a legacy of the company's roots. At these prices the gap between the three is a rounding error. Test them on your texts, keep your favorite and note another as a backup.

Behind them sits the "almost floor" group. DeepSeek V4.1-Flash charges $1.20 for output at peak hours and half that off-peak, GPT-5.4-nano $1.25 and Mistral Large 3 $1.50. All of them write the typical article for less than a third of a cent, room for iterations included. That's the budget tier defended in the AI without subscription hub, where raw material is sold at the price of raw material. No middleman resells it by the pound inside a flat plan.

03The deciding measure for writing is output price

Ranking by output makes sense because writing is the most lopsided use there is. A 400-token brief produces a 1,700-token article, so the text produced makes up most of the bill. Models that show tiny input prices next to high output prices fool the writer's eye. Always sort by the output column first, then look at input only to break a tie between two close models.

The full asymmetry, with its ratios by provider and its money-saving habits, is covered in input and output pricing explained. The sorting rule is the part to remember here. It also explains why the cache, which only applies to input, matters less for pure writing than for document work, where you send a lot and receive little back.

04The ranking, from cheapest to most expensive

Price per million tokens and cost of a 1,000-word article (400 input tokens, 1,700 output tokens). Official price lists of October 16, 2026

Model	Input	Output	1,000-word article
Claude Haiku 5.5	$0.10	$0.50	~0.09 cent
GPT-6 Luna	$0.10	$0.50	~0.09 cent
Mistral Small 4	$0.15	$0.60	~0.11 cent
DeepSeek V4.1-Flash (peak hours)	$0.30	$1.20	~0.22 cent
GPT-5.4-nano	$0.20	$1.25	~0.22 cent
Mistral Large 3	$0.50	$1.50	~0.28 cent
DeepSeek V4-Pro (peak hours)	$1.32	$3.96	~0.73 cent
GPT-5.4-mini	$0.75	$4.50	~0.8 cent
Claude Haiku 4.5	$1	$5	~0.9 cent
Claude Sonnet 4.6	$3	$15	~2.7 cents

Ten models show a factor of thirty between the two ends of the table, yet no row goes above three cents per article. That's the real state of per-token writing today. The general table for all uses, flagships and options included, stays in the full price list comparison. This one is cut for writing alone.

05A year of blogging on each champion, in figures

The yearly scale assumes twelve months of ten 1,000-word articles and one iteration each. On Claude Haiku 5.5 the whole year costs about 21 cents. On GPT-6 Luna it's also about 21 cents. On GPT-5.4-nano it's half a dollar. Even on the mid-tier Haiku 4.5, the year comes to a little over two dollars, which is less than the coffee of a single morning.

These amounts look unreal, but they're plain arithmetic. You multiply the yearly token volume by the price list and nothing else. The practical consequence deserves to be stated. At floor level, further optimization is pointless, because your time is worth more than chasing a tenth of a cent. Useful optimization lies elsewhere, in the quality routing described below and in templates that get the writing right the first time. Both of those pay you back in time rather than cents.

06Where these floor prices come from, in brief

Three forces explain them. The first is efficiency gains, since generating a token takes less and less computing with each new architecture and providers pass that on to win market share. Competition comes next, fierce in the budget tier where customers compare to the cent. Open-weight models finish the job, served by several competing hosts, which set a public reference price that everyone else has to match or justify.

For writers the lesson is reassuring. These floor prices aren't loss-leading promotions but a market structure. Prices have mostly fallen as new models arrive, though not always, since DeepSeek and Mistral Small 4 both raised their rates between June and October 2026. The ranking above will go out of date, most often on the downside. The official price lists cited in the sources are the reference on the day you decide, so check them before you commit.

07Cheap models and the quality of the text

The fair objection is to ask what the text is worth at these prices. The honest answer comes down to three observations from use. On routine writing such as product sheets, drafts, variations and standard content, today's floor models hold their own very well. Users writing in French praise Mistral in particular, since French is home ground for the Paris company. Tokenization also favors models trained widely on that language. On demanding registers such as nuance, humor and signed texts, the higher tiers keep the edge, which is no surprise. Nobody asks a floor model to write the brand manifesto. You ask it to get through routine work cleanly. It does that.

The third observation is the most useful one. Nobody can decide for you. The blind test protocol uses three pieces of content, two models and a read with no labels. It settles the matter in one evening for a few cents. It's laid out in the verdict across all uses. Many people find that an honest floor model covers 70% of their output without anyone noticing.

08The traps of going all floor, stated honestly

The first trap is sameness. A whole site written on a floor model without demanding editing ends up sounding the same everywhere, correct but interchangeable. Readers don't name the problem. They just drift away. The fix lies in personal templates covering voice, structures and house bans, then in a polish at a higher tier for whatever goes in the shop window. A site's personality comes from its instructions more than from its model.

Complex instructions are another trap. Briefs with many constraints, such as a precise tone plus an imposed structure plus banned vocabulary, wear floor models out faster. The fixes during editing then eat up the savings. When a piece of content needs three rounds of corrections, move up a tier without a second thought. The time calculation beats the cents calculation from the first minute of rework you avoid.

Factual trust is the last trap. No price tier lets you skip fact-checking, but floor models call for extra care with figures and dated claims. The rule holds for all AI-assisted writing. The model proposes and your editing decides, because your editing is what signs the piece and what your readers judge in the end.

09Three writer profiles, three setups

The volume publisher puts out fifty pieces of content a month or more. The setup is a floor model for mass production, a mid-tier model to review the important pages and carefully built templates made once and reused. The bill stays under two dollars a month. The quality lever is the brief rather than the model, since a well-directed floor model matches a badly briefed mid-tier one for this kind of production.

The regular blogger writes six to ten careful articles a month. The two-tier setup described below serves that profile better than a single champion, with drafts on the floor model and the polish on the mid-tier one. It takes one settings decision and costs less per month than a postage stamp, while the quality of what gets published stays intact.

Last comes the demanding writer who publishes polished texts under their own name. For that profile the floor model works as a quick draft tool and research assistant, never as the final writer. The savings come from the volume behind the scenes, not from the signed text. That's already a lot, because most of the tokens in a finished text are used before the final version, in research, outlines and variants nobody will ever read.

10Free or paid floor model for writing

To be fair to the free options first, the tiers that need no card, led by Google AI Studio, write quite well and stay unbeatable for learning. The fully free options compare all of them. Their limits show up once you write regularly, with daily quotas, queues at peak hours and data use terms specific to free plans.

The paid floor model takes over at exactly that point. At about a tenth of a cent per article, five dollars of credit covers years of volume that no free tier allows, with no queue and the data terms of paid offers. For a regular writer, the choice between free and floor settles itself at the first quota you hit. The two also sit side by side in the same key ring without conflict, the free tier as a sandbox for learning and the floor model as the production line.

11The winning setup, floor model for drafts and a higher tier for polish

The real champion isn't a model but a routing. The floor model writes the first drafts, the variants and anything that gets edited anyway. A mid-tier or high-end model then takes the chosen version for the published polish. A finished article typically costs half a cent of drafts plus three cents of polish, so you get top quality at a price that barely registers. The setup also makes iteration painless. Three opening angles tested on the floor model cost less than a single sentence at the higher tier, so you test them and the final text improves.

That routing habit, adjustable task by task in good tools, has its own full guide. Routing between AI models sets out its rules and traps. Routing is what turns a price ranking into a writing strategy. The table supplies the parts, the routing builds the machine and your editing signs off what it produces.

12The double test, made for writing

Before you adopt a floor model, two tests are better than one. The first is the classic writing test, with three typical pieces of content read blind against your current model to get a verdict on taste. Then comes the editing test, specific to writing. Time how long it takes to clean up each version, corrections included, because that's where the real cost of a model hides.

A floor model that needs ten minutes of fixes per article, against two for the mid-tier model, "costs" eight minutes. That's far more than the two cents saved as soon as your time has any value. If the fixes take the same time, the floor model wins without debate. This double measure, taste plus stopwatch, builds the personal ranking that every public comparison lacks. It's your own. You can recalibrate it for free whenever a new model catches your interest.

13Getting started in twenty minutes

On the key side, the floor models open quickly. Creating a Claude key takes five minutes with a few dollars of credit and the OpenAI console works the same way. Creating a Mistral key takes about as long, with a free trial plan as a bonus so you can try before you top up. Every API key step by step covers the rest of the market if your choice lands elsewhere, DeepSeek included.

On the practical side, the full chain from brief to publication, templates and images included, is set out in writing and publishing without a subscription. Used through OpenTahr, the cheapest AI on the market stays exactly at its list price and the account is free. It's your key and your list price with no margin on your generations, so the volume works for you article after article.

14Beyond the text, the side jobs of a thrifty writer

Writing comes with side tasks that follow the same floor logic. Meta descriptions, alternative titles and tags can be generated in batches on the cheapest model, because their short format forgives almost anything. Translating your own content works too, since today's floor models handle it very well between closely related languages. Read it over like everything else before you publish.

Featured images fall under a separate price list billed per image. Their own floor prices go below one cent at standard quality. A fully illustrated article, text plus visuals, therefore stays under five cents from start to finish on a budget setup. The content line of a whole site stops being a budget item and becomes a rounding figure at the bottom of the statement.

15Frequently asked questions

What is the cheapest AI for writing articles?

On today's official price lists it's a tie between Claude Haiku 5.5 and GPT-6 Luna at $0.50 per million output tokens, just ahead of Mistral Small 4 at $0.60. A 1,000-word article costs under a tenth of a cent on either. Price lists change, so the official pages cited in the sources are the reference on the day you read this.

Is DeepSeek really cheaper than GPT?

Against the GPT-5.4 range it clearly is. DeepSeek V4.1-Flash charges $1.20 for output at peak hours and $0.60 off-peak, against $4.50 for GPT-5.4-mini and $15 for GPT-5.4, a gap you can check column against column in the sources. The newer GPT-6 Luna reverses the picture at $0.50, below DeepSeek even off-peak. The useful comparison is therefore made tier by tier and generation by generation.

Is Mistral cheaper than ChatGPT for writing?

Through the API, Mistral Small 4 costs about seven times less on output than GPT-5.4-mini ($0.60 against $4.50). OpenAI's newer GPT-6 Luna sits slightly below it at $0.50, so the answer now depends on the generation you compare. In consumer apps, the subscriptions of the two companies sit in the same price range, which is why the API angle is the one used here.

Is a free AI enough to write articles?

For learning and for occasional writing it is, with the no-card tiers first in line. For regular output the free quotas run out fast and paid floor models take over for a few cents. Many writers keep both, with the free tier as a sandbox and the floor model for production.

Is the quality of the cheapest models improving?

That's the trend observed across the market. Today's floor models write better than the mid-range models of two years ago, at a fraction of the price. Hence the rule of running the blind test again at each major release. The price ranking is dated by nature. The quality ranking goes stale even faster.

Disclosure. Prices were recorded on October 16, 2026 from the official price lists cited in the sources. Article costs are recalculated from those lists with the assumptions shown in the table. You can check them on a calculator. Quality assessments rely on observed use, with no invented scores. None of the providers mentioned pays us.

16Sources

1. DeepSeek, official API price list
2. Mistral, official API price list
3. OpenAI, official API price list
4. Anthropic, official Claude API price list (Haiku 5.5)
5. Mistral, API price list per model (Small 4, Large 3)