Can I run Mistral Nemo Instruct 2407 locally?

Mistral has not published open weights for this model in the listing, so local self-hosting is not confirmed; access appears to be via API.

What does it cost and how long is the context?

The API is priced at $0.02 per million input tokens and $0.03 per million output tokens, with a 131,072-token context window.

How does Mistral Nemo compare to other Mistral models?

Nemo is a 12B model co-developed with NVIDIA and positioned as outperforming models of similar or smaller size; it sits below Mistral's larger or more specialised models in raw capability but well above them in cost efficiency.

Mistral Nemo Instruct 2407

1214°

#llm #mistralai #chat #long context #budget #multilingual

Open DeepInfra

mistralai | 131,072 token context | $0.02/M input, $0.03/M output. 12B model trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.

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Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Mistral Nemo Instruct 2407 is a 12B instruction-tuned model built jointly by Mistral AI and NVIDIA, with a 128K token context window at a price that barely registers. It punches above its weight class against models of similar or smaller size, making it a practical choice for developers who want capable general-purpose text generation without paying frontier-model rates.

Known for

128K context window, unusually large for a 12B model
Jointly developed by Mistral AI and NVIDIA
Strong performance relative to models at or below its size
Very low API cost: $0.02 per million input tokens

Watch out for

Weights are not listed as open; local deployment options are unclear from what Mistral has published.
At 12B parameters it will trail larger frontier models on complex reasoning and coding tasks.

Best for

Prompts to try

Summarise a long document

Here is a long article. Read it carefully, then write a structured summary with three sections: key claims, supporting evidence, and what is still uncertain. Keep each section to three bullet points. [paste your article text here]

Draft a reply email

I received this email from a client asking why their project is delayed. Write a professional, honest reply that acknowledges the delay, gives one concrete reason, and proposes a revised delivery date of two weeks from today. Tone: direct but not defensive. Client email: 'We were expecting the report last Friday. What is going on?'

Explain a concept simply

Explain how transformer attention mechanisms work to someone who understands basic matrix multiplication but has never studied machine learning. Use a concrete analogy, keep it under 200 words, and flag one thing that analogy gets wrong.

Compare with

Mistral Large
API-only model
128,000 token context · $2.00/M input, $6.00/M output
Mistral Nemo
Open-weights model
131,072 token context · $0.03/M input, $0.03/M output
Saba
API-only model
32,768 token context · $0.20/M input, $0.60/M output
Mistral Large 2407
API-only model
131,072 token context · $2.00/M input, $6.00/M output

More text models from mistralai

Questions people ask

Can I run Mistral Nemo Instruct 2407 locally?: Mistral has not published open weights for this model in the listing, so local self-hosting is not confirmed; access appears to be via API.
What does it cost and how long is the context?: The API is priced at $0.02 per million input tokens and $0.03 per million output tokens, with a 131,072-token context window.
How does Mistral Nemo compare to other Mistral models?: Nemo is a 12B model co-developed with NVIDIA and positioned as outperforming models of similar or smaller size; it sits below Mistral's larger or more specialised models in raw capability but well above them in cost efficiency.

Captain's verdict
A sensible workhorse for long-context tasks on a tight budget; do not expect it to match models three times its size.

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.

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Updated Oct 22, 2026

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How this score adds up

This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.

Glossary: the terms used here

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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.

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Mistral Nemo Instruct 2407 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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