Mistral Nemo
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Mistral | 131,072 token context | $0.03/M input, $0.03/M output. A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi. It supports function calling and is released under the Apache 2.0 license.
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Captain's notes
Notes from 2026-10-13
Mistral Nemo is a 12B parameter model built jointly by Mistral AI and NVIDIA, with a 128k token context window and genuine multilingual coverage across eleven languages. It sits in the practical middle ground: small enough to run locally on consumer hardware, cheap enough via API to leave in production without watching the meter, and capable enough to replace Mistral 7B in most pipelines. Apache 2.0 weights mean you own the deployment.
Known for
- Apache 2.0 licence: use it commercially, modify it, run it anywhere.
- 128k context window at 12B parameters is unusually generous for this size class.
- Eleven-language support with dedicated multilingual training data, not just English fine-tuning.
- Function calling built in, making it practical for tool-use and agent pipelines.
- Designed as a drop-in upgrade from Mistral 7B with the same integration patterns.
Watch out for
- Multilingual MMLU scores cluster in the high-50s to low-60s; strong for the size, but not frontier-level.
- At 12B it will need quantisation to run comfortably on GPUs under 16 GB VRAM.
Prompts to try
Multilingual document summary
The following paragraph is in French. Summarise it in English in three bullet points, then translate those bullets into Spanish and Japanese.
"La transition énergétique représente l'un des défis les plus complexes du XXIe siècle. Elle implique non seulement des changements technologiques profonds, mais aussi des transformations économiques et sociales majeures qui touchent l'ensemble des secteurs d'activité."
Function-calling schema design
Design a JSON function schema for a tool called get_weather. It should accept a city name, an optional country code, and a unit parameter that accepts either celsius or fahrenheit with celsius as the default. Return the schema in the format expected by a function-calling API, then show an example call for Tokyo in celsius.
Long-context contract review
You are reviewing a software licence agreement. Identify any clauses that restrict sublicensing, impose audit rights on the licensor, or automatically renew the contract. For each clause found, quote the relevant sentence and explain in plain English what obligation it creates for the licensee. If none are found, say so explicitly.
Compare with
- Mistral Large 2407API-only model131,072 token context · $2.00/M input, $6.00/M output
- Mistral Large 4API-only model1,048,576 token context · $0.68/M input, $2.09/M output
- Ministral 3 3B 2512Open-weights model131,072 token context · $0.10/M input, $0.10/M output
- Voxtral Small 24B 2507Open-weights model32,768 token context · $0.10/M input, $0.30/M output
More text models from mistralai
Questions people ask
- Can I run Mistral Nemo locally?
- Yes. The weights are open under Apache 2.0 and available at mistralai/Mistral-Nemo-Instruct-2407 on Hugging Face. Mistral AI supports running it via mistral_inference, Hugging Face Transformers, or NVIDIA NeMo.
- What does Mistral Nemo cost via API, and what is the context limit?
- The API price is $0.03 per million tokens for both input and output, and the context window is 128k tokens.
- How does Mistral Nemo differ from Mistral 7B?
- Nemo is larger at 12B parameters, carries a much bigger 128k context window versus 7B's shorter window, adds broader multilingual training, and is positioned by Mistral as a direct drop-in replacement for the 7B in existing pipelines.
Captain's verdictA sensible, genuinely open 12B workhorse: cheap to run via API, free to deploy locally, and one of the more honest multilingual options at this weight class.
Model card on Hugging Face
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: #63 Hottest Text / LLM model · #63 Hottest Text / LLM text model
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Mistral Nemo Popularity Trend
Heat score, last 12 months
Heat score
8,319°
This week
▲
0.3%
8,317° then, 8,319° now
This month
▲
1.1%
8,314° then, 8,319° now
Past year
▲
25.2%
8,242° then, 8,319° 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.
+1,319°
OpenRouter tokens
248,311,320,782 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, 24,831 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 8,319°, 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.24,831 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.+1,319°8,319°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.8,319°
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.
Mistral Nemo 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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