What frontier models show about reasoning over time

The leaders

Each score is the share of the possible reward that a model earns, averaged over the environments of a family, with its 95% interval. These are the leaders in version 7 of the leaderboard, from 2 November 2026.

The three families test different kinds of work. On personal agents, an assistant runs on a person's phone while new data arrive and people reply late; the score is for the hard level. On industry and on science and compute, the agent works through months of records from a real system with code, finds what happened and why, and submits its answer with the evidence.

• Industry: Claude Opus 5.5 leads with 43.1% (±5.4). Gemini 4 Argon follows with 35.0%, and Claude Sonnet 5.5 with 34.1%.
• Science and compute: Claude Opus 5.5 leads with 29.7% (±14.6). GPT-6 Astra follows with 22.2%, and GPT-6.1 Sol with 16.7%.
• Personal agents, at the hard level: Gemini 4 Argon leads with 31.6% (±5.4). Claude Opus 5.5 follows with 27.0%, and Claude Sonnet 5.5 with 17.4%.

What a score means

A score is not the share of tasks that a model solves. It is the share of the possible reward that the model earns. A model can earn part of the reward on a task that it does not finish, and on a person's phone it can lose reward on a task that it answers, if it answers late or raises a false alarm on the way.

That makes the scores comparable across families, and it makes them useful for training. A model that moves from 30% to 40% has learned something that the board can see, even if it still finishes few tasks in full.

No family is close to solved

The best model earns less than half of the possible reward in every family. These are not trick questions. Each answer key is checked against the records, and every task has a reference answer that earns the full score.

The tasks are hard because the work is hard. The histories are long, the data keep arriving, and the evidence is spread over many sources. A model must choose where to look, keep track of what it knew at each moment, and resist the first explanation that fits.

That leaves room at the top. A benchmark that the best model almost passes stops telling models apart, and it stops being useful for training too. On every family, the leader is still further from the full score than it is from the models below it.

The same conditions for every model

Every model runs on the same environments with the same tools. On industry and on science and compute, every model also gets the same harness and the same budget.

The board also changes as new models arrive. Versions 3 to 7 added Claude Haiku 5.5, DeepSeek-V4.1-Flash, DeepSeek-V4-Pro, Qwen3.8-Flash-Next, MAI-Thinking-1 and Nemotron 3 Ultra. None of them entered the top three of any family, so the leaders above are the same as they were in version 3.

Different labs lead different families

Gemini 4 Argon leads on a person's phone, where data arrive while the agent works and people reply late. Claude Opus 5.5 leads on industrial and scientific systems, where the agent works through months of records with code.

A single benchmark number would hide this. The two kinds of work ask for different strengths. On a phone, the agent must decide when the evidence is enough and whom to tell. On a recorded history, it must search a long record with code and build an answer that the evidence supports. A model that is strong on one kind of time is not always strong on the other.

For a team that chooses a model, the lesson is practical. Measure the model on the kind of time your product has. A model that tops a general board may not top yours.

Read the intervals

Every mean on the board comes with its 95% interval, because a single number hides how much a score moves between runs. Evaluations of language models are statistical experiments, and their results need error bars before anyone compares them. ²

Some gaps are larger than their intervals and some are not. On industry, Gemini 4 Argon at 35.0% and Claude Sonnet 5.5 at 34.1% sit well inside each other's intervals, so the board does not separate them. The gap from Claude Opus 5.5 at 43.1% to the next model is larger than the interval of either one. On science and compute, the intervals are wider than the gaps, so even the order at the top is less settled than on the other two families.

A high score is hard to fake

On industrial and scientific systems, wrong answers, including answers that take a shortcut, score 10% or less. Numeric checks need no judge. Two judges from different labs grade the rest against keys checked against the records, because a language model that grades answers can favour answers that look like its own. ³

On a person's phone, late messages and false alarms lose points. A model earns its score by finding what happened and saying it in time, not by sounding confident.

Run it yourself

Each environment is a Harbor task with its own grader, so you can run your own model under the same conditions and compare it with the board. The methodology page explains how we run and score each model. ¹ To get started, request samples or talk to a researcher.

The board shows averages with their intervals, not scores for single environments. That keeps the measure honest: no one can tune a model to one task and climb the board with it.