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reasoning benchmark

Workspace Bench Leaderboard

Workspace Bench evaluates AI agents on high-economic-value workplace tasks that span multi-step planning, file processing, and tool use across realistic office and productivity workflows.

Updated Aug 17, 2026

Models3
Model coverage3
MetricScore
EvidenceB

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Workspace Bench Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

Show columns

Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelACQwen3.8 MaxAlibaba Cloud / Qwen TeamScore67.7%Percentile100.0%Participants3EvidenceCEvaluatedAug 17, 2026
Rank02ModelBYSeed 2.1 TurboByteDanceScore54.7%Percentile50.0%Participants3EvidenceCEvaluatedAug 17, 2026
Rank03ModelBYSeed 2.1 ProByteDanceScore53.0%Percentile0.0%Participants3EvidenceCEvaluatedAug 17, 2026

Workspace Bench Highlights

The leading models and scores on this benchmark.

Rank #1Qwen3.8 Max67.7%Rank #2Seed 2.1 Turbo54.7%Rank #3Seed 2.1 Pro53.0%

Workspace Bench Score Distribution

A closer view of the leading scores on this benchmark.

Workspace Bench

The Top AI Models for Workspace Bench

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

Ranking basisThis workspace 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.

  1. 01
    AC
    Qwen3.8 MaxAlibaba Cloud / Qwen Team
    Score
    67.7%
    Price
    $2.0 input / $6.0 output per 1M tokens

    Strengths

    • Ranks #1 of 3 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Workspace Bench, not total model capability
  2. 02
    BY
    Seed 2.1 TurboByteDance
    Score
    54.7%

    Strengths

    • Ranks #2 of 3 compared models
    • 50th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Workspace Bench, not total model capability
  3. 03
    BY
    Seed 2.1 ProByteDance
    Score
    53.0%

    Strengths

    • Ranks #3 of 3 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Workspace Bench, not total model capability

Selection summary

Best AI Models for Workspace Bench

Qwen3.8 Max currently leads Workspace Bench with 67.7%. 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.

Benchmark rank #1Qwen3.8 Max67.7% · $2.0 input / $6.0 output per 1M tokensBenchmark rank #2Seed 2.1 Turbo54.7%Benchmark rank #3Seed 2.1 Pro53.0%

What is Workspace Bench?

What Workspace Bench measures and how its scores work.

Workspace Bench evaluates AI agents on high-economic-value workplace tasks that span multi-step planning, file processing, and tool use across realistic office and productivity workflows.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
Workspace Bench
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
workspace-bench|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about Workspace Bench.

Which model scores highest on Workspace Bench?

Qwen3.8 Max is currently ranked first with 67.7%.

What does Workspace Bench measure?

Workspace Bench evaluates AI agents on high-economic-value workplace tasks that span multi-step planning, file processing, and tool use across realistic office and productivity workflows.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

3 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.