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multimodal benchmark

Vibe-Eval Leaderboard

Vibe-Eval is a hard multimodal evaluation suite with 269 visual understanding prompts and expert-authored gold-standard responses.

Updated Sep 5, 2026

Models8
Model coverage8
MetricScore
EvidenceB

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  • FAQ

Vibe-Eval Ranking

Higher score ranks better on this benchmark.

8 rows
Columns

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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelGOGemini 2.5 Pro Preview 06-05GoogleScore67.20%Percentile100.00%Participants8EvidenceCEvaluatedSep 8, 2026
Rank02ModelGOGemini 2.5 ProGoogleScore65.60%Percentile85.71%Participants8EvidenceCEvaluatedSep 8, 2026
Rank03ModelGOGemini 2.5 FlashGoogleScore65.40%Percentile71.43%Participants8EvidenceCEvaluatedSep 8, 2026
Rank04ModelGOGemini 2.0 FlashGoogleScore56.30%Percentile57.14%Participants8EvidenceCEvaluatedSep 8, 2026
Rank05ModelGOGemini 1.5 ProGoogleScore53.90%Percentile42.86%Participants8EvidenceCEvaluatedSep 8, 2026
Rank06ModelGOGemini 2.5 Flash-LiteGoogleScore51.30%Percentile28.57%Participants8EvidenceCEvaluatedSep 8, 2026
Rank07ModelGOGemini 1.5 FlashGoogleScore48.90%Percentile14.29%Participants8EvidenceCEvaluatedSep 8, 2026
Rank08ModelGOGemini 1.5 Flash 8BGoogleScore40.90%Percentile0.00%Participants8EvidenceCEvaluatedSep 8, 2026

Vibe-Eval Highlights

The leading models and scores on this benchmark.

Vibe-Eval Score Distribution

A closer view of the leading scores on this benchmark.

Vibe-Eval

The Top AI Models for Vibe-Eval

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

What is Vibe-Eval?

What Vibe-Eval measures and how its scores work.

Vibe-Eval is a benchmark for measuring the progress of multimodal language models, with objectives covering day-to-day multimodal chat tasks and rigorous testing of frontier models.

It measures performance on visual understanding prompts using a Score reported as a ratio; its hard set contains more than 50% questions that all frontier models answer incorrectly.

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

Family
Vibe-Eval
Modality
multimodal
Primary category
multimodal
Score direction
higher
LLMBoard eligible
No
Evaluation key
overall

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

FAQ

Common questions about Vibe-Eval.

Which model scores highest on Vibe-Eval?

Gemini 2.5 Pro Preview 06-05 is currently ranked first with 67.20%.

What are the top three models on Vibe-Eval?

The current leaders are Gemini 2.5 Pro Preview 06-05 (67.20%), Gemini 2.5 Pro (65.60%), and Gemini 2.5 Flash (65.40%).

Which Vibe-Eval model has the lowest official input price?

Gemini 2.5 Flash-Lite has the lowest matched official input price at $0.10 input / $0.40 output per 1M tokens.

Which models are fastest among Vibe-Eval results?

The fastest matched records are Gemini 2.0 Flash (183.00 tok/s via Google), Gemini 1.5 Flash (150.00 tok/s via Google), and Gemini 1.5 Flash 8B (150.00 tok/s via Google).

Does the highest score result prove overall model quality?

No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.

What does Vibe-Eval measure?

It measures performance on visual understanding prompts using a Score reported as a ratio; its hard set contains more than 50% questions that all frontier models answer incorrectly.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

8 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.

Rank #1Gemini 2.5 Pro Preview 06-0567.20%
Rank #2Gemini 2.5 Pro65.60%
Rank #3Gemini 2.5 Flash65.40%
Rank #4Gemini 2.0 Flash56.30%

Ranking basisThis vibe-eval AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    GO
    Gemini 2.5 Pro Preview 06-05Google
    Score
    67.20%
    Price
    $1.3 input / $10 output per 1M tokens
    Speed
    Up to 85.00 tok/s via Google

    Strengths

    • Ranks #1 of 8 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Vibe-Eval, not total model capability
  2. 02
    GO
    Gemini 2.5 ProGoogle
    Score
    65.60%
    Price
    $1.3 input / $10 output per 1M tokens
    Speed
    Up to 18.89 tok/s via Google

    Strengths

    • Ranks #2 of 8 compared models
    • 86th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Vibe-Eval, not total model capability
  3. 03
    GO
    Google
    Score
    65.40%
    Price
    $0.30 input / $2.5 output per 1M tokens
    Speed
    Up to 85.00 tok/s via Google

    Strengths

    • Ranks #3 of 8 compared models
    • 71th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Vibe-Eval, not total model capability
  4. 04
    GO
    Google
    Score
    56.30%
    Speed
    Up to 183.00 tok/s via Google

    Strengths

    • Ranks #4 of 8 compared models
    • 57th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Vibe-Eval, not total model capability
  5. 05
    GO
    Google
    Score
    53.90%
    Speed
    Up to 85.00 tok/s via Google

    Strengths

    • Ranks #5 of 8 compared models
    • 43th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Vibe-Eval, not total model capability

Selection summary

Best AI Models for Vibe-Eval

Gemini 2.5 Pro Preview 06-05 currently leads Vibe-Eval with 67.20%. 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.

Gemini 2.5 Flash
Gemini 2.0 Flash
Gemini 1.5 Pro
Benchmark rank #1Gemini 2.5 Pro Preview 06-0567.20% · $1.3 input / $10 output per 1M tokens
Benchmark rank #2Gemini 2.5 Pro65.60% · $1.3 input / $10 output per 1M tokens
Benchmark rank #3Gemini 2.5 Flash65.40% · $0.30 input / $2.5 output per 1M tokens