Knowledge Work

SnorkelWorkplace

A frontier benchmark for evaluating AI agents on economically valuable professional work and verifiable workplace deliverables.

overview

WorkplaceAgents evaluates whether an agent can complete real-world professional tasks that require evidence synthesis, domain judgment, and a finished work product. Each task provides expert-authored instructions and relevant source materials, with success determined by the correctness and usefulness of the resulting deliverable.

Across its occupational coverage, the benchmark spans analytical, operational, technical, financial, scientific, administrative, healthcare, legal, and creative workflows. Tasks require multi-step reasoning and can produce documents, spreadsheets, presentations, code, and structured analyses.

At a glance

200

frontier tasks

19

sectors

96

occupations

19

resource formats

Leaderboard

Rank Model Pass@1 Pass@5 Cost / Trial Cost / Task
1 Muse Spark 1.3
17.7%
30.3%
$10.4 $51.99
2 Grok 4.6
17.1%
28.6%
$7.84 $39.19
3 Fable 5.1
15.8%
30.8%
$18.65 $93.27
4 Qwen 3.8 Max
14.9%
30.3%
$26.54 $132.7
5 Claude Opus 5
14.9%
34.7%
$15.96 $79.79
6 GPT-6 Astra
14.4%
23.7%
$22.6 $113.01
7 Kimi K3
13%
28.2%
$7.79 $38.96
8 GLM 5.3
12.8%
23.6%
$14.33 $71.64
9 Gemini Flash 3.8
11.2%
23.7%
$3.63 $18.15
10 DeepSeek V4 Pro
8.6%
35.2%
$1.61 $8.04
11 Nemotron 3 Ultra 550B
6.6%
18.4%
$0.59 $2.95
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Frontier performance

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Methodology

Evaluator

Harbor evaluates completed work products using deterministic checks and rubric-based judging. Each rubric criterion maps to a substantive, independently verifiable requirement.
timeout
A run fails if the agent times out before producing a complete answer or required artifact. Each task is evaluated within a bounded execution environment.
integration

Instructions, reference files, supporting materials, and required output formats are packaged with each task. Agents work through the tools available in the task environment.

scoring note

Scoring prioritizes substantive correctness, reasoning, and work-product quality. Formatting criteria are constrained so they cannot dominate the evaluation.

Behind the benchmark

Professional work rarely ends with a short answer. It requires finding relevant evidence, applying domain judgment, resolving ambiguity, and producing an artifact that another person can use.

WorkplaceAgents evaluates that end-to-end process. The benchmark measures whether an agent can produce a correct, useful, and professionally defensible work product across diverse forms of knowledge work.

More benchmarks

Agentic Coding

Agentic Coding 2.0

Evaluating whether coding agents can plan, execute, verify, and recover across complex terminal-native engineering tasks.

By pass@1
1
Image
GPT-6 Astra
47.6%
2
Image
Fable 5.1
39.6%
3
Image
Opus 5
38.9%
Software Engineering

SWE-bench CLI

Tests whether an AI coding agent can diagnose and deliver a validated, multi-file change in a real open-source repository, navigating code, tests, dependencies, and tooling through the command line.

By pass@1
1
Image
Fable 5.1
14.5%
2
Image
Opus 5
14.0%
3
Image
GPT-6 Astra
12.4%
Enterprise Environments

SnorkelUnderwrite 2.0

Measures an agent’s ability to turn incomplete, distributed insurance evidence into an auditable underwriting decision while respecting authority and policy constraints.

By pass@1
1
Image
GLM 5.3
30.4%
2
Image
DeepSeek V4 Pro
28.5%
3
Image
Kimi K3
27.9%
Enterprise Environments

SnorkelManufacturing

Measures how well AI agents can turn fragmented plant-floor, engineering, and supplier evidence into safe, technically defensible decisions and actions.

By pass@1
1
Image
Grok 4.6
16.4%
2
Image
GLM 5.3
11.9%
3
Image
Fable 5.1
11.3%
Enterprise Environments

SnorkelRevOps

Tests whether AI agents can reconcile revenue systems, enforce hard commercial controls, and carry a decision through to the correct business state.

By pass@1
1
Image
Grok 4.6
15.8%
2
Image
Fable 5.1
14.0%
3
Image
Kimi K3
13.1%
Enterprise Environments

SnorkelFinance 2.0

Scores how agents gather evidence, perform financial analysis, follow compliance constraints, and complete required state updates in a simulated environment.

By pass@1
1
Image
Grok 4.6
25.1%
2
Image
GLM 5.3
21.2%
3
Image
Fable 5.1
19.6%
Enterprise Environments

SnorkelLegal

Evaluates AI agents on whether they can advance a legal matter while preserving the chain from controlling evidence to procedure, authority, and action.

By pass@1
1
Image
Grok 4.6
40.7%
2
Image
GLM 5.3
37.8%
3
Image
Muse Spark 1.3
36.5%
Agentic Coding

Terminal-Bench 4.0

A benchmark to measure and evolve with the frontier of agent work: real terminal environments, real software engineering tasks, and a rolling task set that is revised as agents catch up to it.

By Resolution Rate
1
Image
GPT-6 Astra
58.2%
2
Image
Fable 5.1
57.9%
3
Image
Opus 5
53.9%
Scientific & Research Workflows

Terminal-Bench-Science

A benchmark for evaluating AI agents on workflows from researchers’ own work. Scientists, not model developers or vendors, set the bar for scientific capability in AI.

By Resolution Rate
1
Image
Fable 5.1
40.0%
2
Image
Opus 5
30.0%
3
Image
GPT-5.6 Sol
22.4%
Agentic Coding

Terminal-Bench 3.0

The next frontier benchmark for agent work. A harder, more domain-diverse successor to Terminal-Bench 2.1 — built in the open, task by task, under continuous adversarial review.

By Resolution Rate
1
Image
Opus 5
42.7%
2
Image
GPT-5.6 Sol
34.6%
3
Image
Fable 5
34.1%
Computer Use

OSWorld 2.0

Long-horizon professional workflows with verifiable outcomes across 55 sub-industries. 147 public tasks of a 1,500+ task corpus, sourced and validated by 300+ industry experts.

By binary accuracy (500 steps)
1
Image
Opus 5 · max
44.33%
2
Image
Opus 5 · xhigh
36.89%
3
Image
Opus 5 · high
33.33%
Software Engineering

Senior SWE-Bench

Evaluating coding agents on senior-level engineering work.

Tasteful Solve Rate
1
Image
Fable 5.1
34.7%
2
Image
Fable 5
34.7%
3
Image
Opus 5
34.7%
Knowledge Work

Agents’ Last Exam

Evaluating AI agents on long-horizon, economically valuable professional workflows with verifiable outcomes.

By Binary Accuracy
1
Image
GPT-6 Astra
34.2
2
Image
Muse Spark 1.3
32.2%
3
Image
Opus 5
31.6%
Software Engineering

SlopCode Bench

Measures code quality degradation in AI-assisted codebases. Tracks checkpoint solve rates, erosion (code bloat), and verbosity under realistic repo conditions.

Top Models by Iso Solve
1
Image
GPT-5.5
28.06%
2
Image
GPT-5.3-Codex
26.02%
3
Image
GPT-5.4
23.47%
Capability/Efficiency

Continual Learning Bench

Evaluates whether AI systems improve from prior experience across sequential, stateful tasks, measuring real in-context learning, not just raw capability.

Top Systems (Agg. Reward)
1
Image
Sonnet 4.6 · ICL
+0.196
2
Image
GPT-5.4 · ICL
+0.189
3
Image
Sonnet 4.6 · Claude Code
+0.185
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