Can I run Pareto 26.10 Preview locally?

unbiased | 1,048,576 token context | $0.80/M input, $3.20/M output. Pareto is a multimodal composite model built for research, coding, and agentic workflows, while delivering frontier-level performance across a broad range of general-purpose tasks. This is a preview of the next Pareto version and may change without notice; use pareto-26.9 for stable behaviour.

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Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Pareto 26.10 Preview is a closed-weights multimodal model from Unbiased, built around research, coding, and agentic workflows while covering general-purpose tasks. Its one-million-token context window is the headline practical feature. This is a preview release and may change without notice, so production work should stay on pareto-26.9.

Known for

One-million-token context window for very long documents or codebases.
Multimodal: accepts images alongside text.
Designed explicitly for agentic and multi-step workflows.
Broad general-purpose coverage beyond its research and coding focus.

Watch out for

Preview release: behaviour may change without notice; not suitable for stable production use.
Closed weights, API-only — no local deployment option.
Output pricing at $3.20/M tokens adds up quickly in long agentic runs.

Best for

Prompts to try

Audit a large codebase

I'm pasting the full contents of a Python repository below. Review the architecture, identify any security issues, flag functions with high cyclomatic complexity, and suggest the three highest-value refactors. Be specific about file names and line ranges.

Multi-step research plan

I need to understand the competitive landscape for solid-state batteries in 2026. Break the research into stages: key players, recent patent activity, manufacturing bottlenecks, and likely commercialisation timelines. For each stage, list the sources you would consult and what question each one answers.

Analyse a diagram or screenshot

Attach a system architecture diagram or screenshot. Describe what the diagram shows, identify any single points of failure or bottlenecks, and suggest two concrete improvements with a brief justification for each.

Compare with

Pareto
API-only model
262,144 token context · $2.50/M input, $7.50/M output
DeepSeek V4.1 Flash
Open-weights model
1,048,576 token context · $0.04/M input, $0.68/M output
GLM 5.3 Flash
Open-weights model
1,048,576 token context · $0.02/M input, $0.72/M output
Hy4 preview
Open-weights model
1,048,576 token context · $0.83/M input, $2.50/M output

More text models from unbiased

Questions people ask

Can I run Pareto 26.10 Preview locally?: No. Unbiased has not released open weights for this model; it is API-only.
What does it cost and how long is the context?: Pricing is $0.80 per million input tokens and $3.20 per million output tokens, with a context window of 1,048,576 tokens.
How does this differ from pareto-26.9?: This is a preview of the next version and may change without notice; Unbiased recommends pareto-26.9 if you need stable, reproducible behaviour.

Captain's verdict
A capable long-context multimodal model worth testing for research and agent work, but keep it off anything that needs stable, predictable outputs.

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

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.

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Pareto 26.10 Preview 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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