llmboard.aiAI model intelligence
Home

Model Rankings

OverallOpen ModelsAgentCodingReasoningMathKnowledgeInstruction FollowingTextVision
Image GenerationImage Editing
Video GenerationImage to VideoVideo Editing
Text to SpeechSpeech to Text
Embeddings

Efficiency

Chat Token PricingImage PricingVideo PricingAudio Pricing
Chat Speed & LatencyProvider Reliability

Benchmarks

GPQAMMLU-ProAIME 2025SWE-Bench VerifiedMMLUHumanity's Last ExamLiveCodeBenchMATHHumanEvalMMMU-Pro
All Benchmarks

Tools

Model DirectoryCompare Models

Scoring & Data

Scoring & Data
393 models668 benchmarks

Leaderboard Center

Overall RankingCodingCore BenchmarksPrice & ValueRuntime Performance

Modalities

All ModelsImage GenerationImage EditingVideo GenerationImage-to-VideoVideo EditingText-to-SpeechSpeech-to-TextEmbeddings

Data & Methods

Scoring MethodAll BenchmarksReasoningMath

Vendors

All VendorsOpenAIAnthropicGoogle
llmboard.aiCopyright 2026 llmboard.ai

reasoning benchmark

Natural Questions Leaderboard

Natural Questions is a question answering dataset featuring real anonymized queries issued to Google search engine. It contains 307,373 training examples where annotators provide long answers (passages) and short answers (entities) from Wikipedia pages, or mark them as unanswerable.

Updated Aug 17, 2026

Models7
Model coverage7
MetricScore
EvidenceB

On this page

  • Ranking
  • Highlights
  • Distribution
  • Top models
  • About
  • FAQ

Natural Questions Ranking

Higher score ranks better on this benchmark.

7 rows
Columns

Show columns

Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelGOGemma 2 27BGoogleScore34.5%Percentile100.0%Participants7EvidenceCEvaluatedAug 17, 2026
Rank02ModelMAMistral NeMo InstructMistral AIScore31.2%Percentile83.3%Participants7EvidenceCEvaluatedAug 17, 2026
Rank03ModelGOGemma 2 9BGoogleScore29.2%Percentile66.7%Participants7EvidenceCEvaluatedAug 17, 2026
Rank04ModelGOGemma 3n E4BGoogleScore20.9%Percentile50.0%Participants7EvidenceCEvaluatedAug 17, 2026
Rank05ModelGOGemma 3n E4B Instructed LiteRT PreviewGoogleScore20.9%Percentile33.3%Participants7EvidenceCEvaluatedAug 17, 2026
Rank06ModelGOGemma 3n E2BGoogleScore15.5%Percentile16.7%Participants7EvidenceCEvaluatedAug 17, 2026
Rank07ModelGOGemma 3n E2B Instructed LiteRT (Preview)GoogleScore15.5%Percentile0.0%Participants7EvidenceCEvaluatedAug 17, 2026

Natural Questions Highlights

The leading models and scores on this benchmark.

Rank #1Gemma 2 27B34.5%Rank #2Mistral NeMo Instruct31.2%Rank #3Gemma 2 9B29.2%Rank #4Gemma 3n E4B20.9%

Natural Questions Score Distribution

A closer view of the leading scores on this benchmark.

Natural Questions

The Top AI Models for Natural Questions

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

Ranking basisThis natural questions 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
    GO
    Gemma 2 27BGoogle
    Score
    34.5%

    Strengths

    • Ranks #1 of 7 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Natural Questions, not total model capability
  2. 02
    MA
    Mistral NeMo InstructMistral AI
    Score
    31.2%
    Price
    $0.15 input / $0.15 output per 1M tokens
    Speed
    Up to 42 tok/s via Google

    Strengths

    • Ranks #2 of 7 compared models
    • 83th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Natural Questions, not total model capability
  3. 03
    GO
    Gemma 2 9BGoogle
    Score
    29.2%

    Strengths

    • Ranks #3 of 7 compared models
    • 67th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Natural Questions, not total model capability
  4. 04
    GO
    Gemma 3n E4BGoogle
    Score
    20.9%
    Speed
    Up to 42 tok/s via Together

    Strengths

    • Ranks #4 of 7 compared models
    • 50th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Natural Questions, not total model capability
  5. 05
    GO
    Gemma 3n E4B Instructed LiteRT PreviewGoogle
    Score
    20.9%

    Strengths

    • Ranks #5 of 7 compared models
    • 33th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures Natural Questions, not total model capability

Selection summary

Best AI Models for Natural Questions

Gemma 2 27B currently leads Natural Questions with 34.5%. 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 #1Gemma 2 27B34.5%Benchmark rank #2Mistral NeMo Instruct31.2% · $0.15 input / $0.15 output per 1M tokensBenchmark rank #3Gemma 2 9B29.2%

What is Natural Questions?

What Natural Questions measures and how its scores work.

Natural Questions is a question answering dataset featuring real anonymized queries issued to Google search engine. It contains 307,373 training examples where annotators provide long answers (passages) and short answers (entities) from Wikipedia pages, or mark them as unanswerable.

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

Family
Natural Questions
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
natural-questions|llm-stats-current

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

FAQ

Common questions about Natural Questions.

Which model scores highest on Natural Questions?

Gemma 2 27B is currently ranked first with 34.5%.

What does Natural Questions measure?

Natural Questions is a question answering dataset featuring real anonymized queries issued to Google search engine. It contains 307,373 training examples where annotators provide long answers (passages) and short answers (entities) from Wikipedia pages, or mark them as unanswerable.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

7 model results are currently shown.

Does this benchmark affect the overall score?

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