Pareto

Unbiased's blended model that runs several frontier and open-source models per request and returns the best synthesized answer.

Unbiased · Frontier/foundation models · model

Published in Feed

Overview

Pareto is Unbiased's composite model, built by the Circuit & Chisel team, that runs multiple frontier and open-source LLMs in parallel on each request and dynamically synthesizes or selects the best result rather than relying on one set of weights. It launched publicly on September 17, 2026 after a roughly 33-hour free stealth preview on OpenRouter under the codename 'Union Alpha' starting September 16. The model accepts a 262,144-token context window, can output up to 131,072 tokens, supports text and image input with tool calling and JSON output, and is priced at $2.50 per million input tokens ($0.25 cached) and $7.50 per million output tokens.

This is a sourced directory entry. Features and capabilities are reported in the linked sources; this entry does not document independent Aymorphic testing.

Key features

  • Blended architecture: runs several frontier/open-source models per request and keeps the best answer
  • Locks to one model for the rest of a conversation to preserve prompt-caching and consistency
  • 262,144-token input context window, up to 131,072-token output
  • Text and image input, tool calling, and JSON output support
  • Scored 74 on DeepSWE, 51 on Terminal-Bench 4.0, 78 on MMMU-Pro, 88 on ArXivMath per vendor benchmarks

Use cases

  • Coding and agentic workflows needing frontier-level reasoning without picking a single vendor's model
  • Research and math-heavy tasks (per ArXivMath benchmark)
  • General-purpose tasks where teams want automatic best-of-N model selection

Pricing

$2.50 per million input tokens ($0.25 cached), $7.50 per million output tokens; no published task-cost figure or ongoing free tier beyond the initial stealth-preview window.

Pricing reflects the source record and may change. Confirm current terms with the provider.

Dates and sources

Released
17 Sept 2026
Record updated
22 Sept 2026
Record last checked
22 Sept 2026

Explore the Feed

Other entries filed under frontier/foundation models; inclusion does not imply equivalent features.

  • Muse Wonder

    Meta's most capable Muse models, with active memory and long-goal focus for agents, 1M context and $4/$20 per million tokens.

  • Rho-1

    Reka's 19B omni-reasoning model that handles text, images, video and robot actions in one network, in research preview.

  • Amazon Nova 3

    Amazon's third-generation Nova frontier model family, unveiled at an AWS keynote with a 1M-token context window and a dedicated agentic tool-use variant.

Browse the Feed