GPT 4o mini
8105°
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OpenAI | 128,000 token context | $0.15/M input, $0.60/M output. GPT-4o mini is OpenAI's newest model after GPT-4 Omni, supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than GPT-3.5 Turbo. It maintains SOTA intelligence, while being significantly more cost-effective. GPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferences common leaderboardsOpens in new tab. Check out the launch announcementOpens in new tab to learn more. #multimodal
Guides for this model
The 10 cheapest ChatGPT alternatives in 2026: free local models, free tiers and flat-rate plans
How to choose an AI model in October 2026: the closed source news, then the open weights worth downloading
Open weights vs open source vs API-only: what you are actually allowed to do
Captain's notes
Notes from 2026-10-13
GPT-4o mini is OpenAI's small, fast model that accepts text and image inputs and returns text. At $0.15 per million input tokens it sits well below most frontier models in price, yet OpenAI positions it above GPT-3.5 Turbo in capability. It suits developers and builders who need reliable intelligence at scale without a large API bill.
Known for
- Accepts image inputs alongside text, not just plain text prompts.
- 128,000-token context window — generous for a budget-tier model.
- Priced more than 60% below GPT-3.5 Turbo on input tokens.
- Scores 82% on MMLU, competitive with larger models.
- Ranks above GPT-4 on chat preference leaderboards, per OpenAI.
Watch out for
- Closed weights — no local deployment, no self-hosting, API only.
- Output tokens cost four times the input rate; watch that ratio in long conversations.
- Not an o-series reasoning model; complex multi-step logic is better handled elsewhere.
Prompts to try
Classify and extract at scale
You have a batch of customer support tickets. Here are three examples: 'My order never arrived after two weeks', 'The app crashes on login every time', 'I was charged twice for one order'. For each ticket, output a JSON object with fields: category (billing, shipping, or technical), urgency (high, medium, low), and a one-sentence suggested reply.
Describe and tag an image
Look at the image I've attached. Write a two-sentence factual description of what is shown. Then list up to five concise tags that would help someone find this image in a search engine. Finally, note anything in the image that might be ambiguous or hard to identify with confidence.
Summarise a long document
Here is a lengthy report. Summarise it in three parts: a one-paragraph executive summary for a non-specialist, a bullet list of the five most important findings, and a single sentence naming the most urgent action the reader should take. Keep the total response under 300 words.
Compare with
- GPT-6 LunaAPI-only model1,050,000 token context · $0.10/M input, $0.50/M output
- GPT-5.6 LunaAPI-only model1,050,000 token context · $0.20/M input, $1.20/M output
- GPT-4o (2024-11-20)API-only model128,000 token context · $2.50/M input, $10.00/M output
- GPT-3.5 Turbo (older v0613)API-only model4,095 token context · $1.00/M input, $2.00/M output
More text models from openai
Questions people ask
- Can I run GPT-4o mini locally?
- No — the weights are not public, so it is API access only through OpenAI.
- What does GPT-4o mini cost and how long is its context?
- It costs $0.15 per million input tokens and $0.60 per million output tokens, with a 128,000-token context window.
- How does GPT-4o mini differ from full GPT-4o?
- GPT-4o mini is the smaller, cheaper sibling — significantly lower cost per token but with less headroom for the most demanding reasoning or instruction-following tasks.
Captain's verdictThe sensible default when you need OpenAI quality at volume and the bill actually matters.
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
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Also ranked: #71 Hottest Text / LLM text model
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GPT 4o mini Popularity Trend
Heat score, last 12 months
Heat score
8,105°
This week
▲
0.3%
8,104° then, 8,105° now
This month
▲
1.1%
8,100° then, 8,105° now
Past year
▲
25.0%
8,029° then, 8,105° now
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+1,105°
OpenRouter tokens
48,101,380,955 tokens this model processed through OpenRouter in the last 30 days, prompts and answers together. Every 10,000,000 tokens count as one download in the download term, 4,810 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 8,105°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
- 1Start at the floor.Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.7,000°running total
- 2Add the downloads.4,810 download-equivalents in all, from OpenRouter tokens. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.+1,105°8,105°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.8,105°
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
GPT 4o mini is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
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