productivity benchmark
Job Bench Leaderboard
Job Bench evaluates AI agents on realistic professional tasks that require multi-step planning, research, and production of work artifacts.
Updated Aug 17, 2026
Models4
Model coverage4
MetricScore
EvidenceB
Job Bench Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelME | Score54.7% | Percentile100.0% | Participants4 | EvidenceC | Evaluated |
| Rank02 | ModelAC | Score53.4% | Percentile66.7% | Participants4 | EvidenceC | Evaluated |
| Rank03 | ModelMA | Score52.9% | Percentile33.3% | Participants4 | EvidenceC | Evaluated |
| Rank04 | ModelAC | Score33.4% | Percentile0.0% | Participants4 | EvidenceC | Evaluated |
Job Bench Highlights
The leading models and scores on this benchmark.
Job Bench Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for Job Bench
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis job bench AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01MEMuse Spark 1.1Meta
- Score
- 54.7%
- Price
- $1.3 input / $4.3 output per 1M tokens
- Speed
- Up to 6.2 tok/s via Meta Model API
Strengths
- Ranks #1 of 4 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures Job Bench, not total model capability
- 02ACQwen3.8 MaxAlibaba Cloud / Qwen Team
- Score
- 53.4%
- Price
- $2.0 input / $6.0 output per 1M tokens
Strengths
- Ranks #2 of 4 compared models
- 67th percentile on this benchmark
- C evidence result
Considerations
- This result measures Job Bench, not total model capability
- 03MAKimi K3Moonshot AI
- Score
- 52.9%
- Price
- $3.0 input / $15 output per 1M tokens
- Speed
- Up to 26 tok/s via Fireworks
Strengths
- Ranks #3 of 4 compared models
- 33th percentile on this benchmark
- C evidence result
Considerations
- This result measures Job Bench, not total model capability
- 04ACQwen3.8-27BAlibaba Cloud / Qwen Team
- Score
- 33.4%
Strengths
- Ranks #4 of 4 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures Job Bench, not total model capability
Selection summary
Best AI Models for Job Bench
Muse Spark 1.1 currently leads Job Bench with 54.7%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is Job Bench?
What Job Bench measures and how its scores work.
Job Bench evaluates AI agents on realistic professional tasks that require multi-step planning, research, and production of work artifacts.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Job Bench
- Modality
- text
- Primary category
- productivity
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- job-bench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Job Bench.
Which model scores highest on Job Bench?
Muse Spark 1.1 is currently ranked first with 54.7%.
What does Job Bench measure?
Job Bench evaluates AI agents on realistic professional tasks that require multi-step planning, research, and production of work artifacts.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
4 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.