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reasoning benchmark

LMArena Text Leaderboard Leaderboard

LMArena Text Leaderboard is a blind human preference evaluation benchmark that ranks models based on pairwise comparisons in real-world conversations. The leaderboard uses Elo ratings computed from user preferences in head-to-head model battles, providing a comprehensive measure of overall model capability and style.

Updated Aug 17, 2026

Models2
Model coverage2
MetricScore
EvidenceB

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LMArena Text Leaderboard Ranking

Higher score ranks better on this benchmark.

2 rows
Columns

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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelXAGrok-4.1 ThinkingxAIScore1,483 pointsPercentile100.0%Participants2EvidenceCEvaluatedAug 17, 2026
Rank02ModelXAGrok-4.1xAIScore1,465 pointsPercentile0.0%Participants2EvidenceCEvaluatedAug 17, 2026

LMArena Text Leaderboard Highlights

The leading models and scores on this benchmark.

Rank #1Grok-4.1 Thinking1,483 pointsRank #2Grok-4.11,465 points

LMArena Text Leaderboard Score Distribution

A closer view of the leading scores on this benchmark.

LMArena Text Leaderboard

The Top AI Models for LMArena Text Leaderboard

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

Ranking basisThis lmarena text leaderboard 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
    XA
    Grok-4.1 ThinkingxAI
    Score
    1,483 points
    Speed
    Up to 80 tok/s via xAI

    Strengths

    • Ranks #1 of 2 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures LMArena Text Leaderboard, not total model capability
  2. 02
    XA
    Grok-4.1xAI
    Score
    1,465 points
    Speed
    Up to 100 tok/s via xAI

    Strengths

    • Ranks #2 of 2 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures LMArena Text Leaderboard, not total model capability

Selection summary

Best AI Models for LMArena Text Leaderboard

Grok-4.1 Thinking currently leads LMArena Text Leaderboard with 1,483 points. 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 #1Grok-4.1 Thinking1,483 points · Up to 80 tok/s via xAIBenchmark rank #2Grok-4.11,465 points · Up to 100 tok/s via xAI

What is LMArena Text Leaderboard?

What LMArena Text Leaderboard measures and how its scores work.

LMArena Text Leaderboard is a blind human preference evaluation benchmark that ranks models based on pairwise comparisons in real-world conversations. The leaderboard uses Elo ratings computed from user preferences in head-to-head model battles, providing a comprehensive measure of overall model capability and style.

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

Family
LMArena Text Leaderboard
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
No
Evaluation key
lmarena-text|llm-stats-current

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

FAQ

Common questions about LMArena Text Leaderboard.

Which model scores highest on LMArena Text Leaderboard?

Grok-4.1 Thinking is currently ranked first with 1,483 points.

What does LMArena Text Leaderboard measure?

LMArena Text Leaderboard is a blind human preference evaluation benchmark that ranks models based on pairwise comparisons in real-world conversations. The leaderboard uses Elo ratings computed from user preferences in head-to-head model battles, providing a comprehensive measure of overall model capability and style.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

2 model results are currently shown.

Does this benchmark affect the overall score?

No. This benchmark is shown for reference but does not contribute to the overall score.