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chat benchmark

IFEval Leaderboard

IFEval is an Instruction-Following Evaluation benchmark for large language models that uses around 500 prompts with one or more verifiable constraints across 25 types of instructions.

Updated Oct 23, 2026

Models70
Model coverage70
MetricScore
EvidenceB

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IFEval Ranking

Higher score ranks better on this benchmark.

30 of 70 rows
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Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelACQwen3.5-27BAlibaba Cloud / Qwen TeamScore95.00%Percentile100.00%Participants70EvidenceCEvaluatedOct 22, 2026
Rank02ModelACQwen3.7-PlusAlibaba Cloud / Qwen TeamScore94.60%Percentile98.55%Participants70EvidenceCEvaluatedOct 22, 2026
Rank03ModelACQwen3.7 MaxAlibaba Cloud / Qwen TeamScore94.30%Percentile97.10%Participants70EvidenceCEvaluatedOct 22, 2026
Rank04ModelACQwen3.6 PlusAlibaba Cloud / Qwen TeamScore94.30%Percentile95.65%Participants70EvidenceCEvaluatedOct 22, 2026
Rank05ModelOPo3-miniOpenAIScore93.90%Percentile94.20%Participants70EvidenceCEvaluatedOct 22, 2026
Rank06ModelACQwen3.5-122B-A10BAlibaba Cloud / Qwen TeamScore93.40%Percentile92.75%Participants70EvidenceCEvaluatedOct 22, 2026
Rank07ModelACQwen3 Max ThinkingAlibaba Cloud / Qwen TeamScore93.40%Percentile91.30%Participants70EvidenceCEvaluatedOct 22, 2026
Rank08ModelANClaude 3.7 SonnetAnthropicScore93.20%Percentile89.86%Participants70EvidenceCEvaluatedOct 22, 2026
Rank09ModelACQwen3.5-397B-A17BAlibaba Cloud / Qwen TeamScore92.60%Percentile88.41%Participants70EvidenceCEvaluatedOct 22, 2026
Rank10ModelMELlama 3.3 70B InstructMetaScore92.10%Percentile86.96%Participants70EvidenceCEvaluatedOct 22, 2026
Rank11ModelAMNova ProAmazonScore92.10%Percentile85.51%Participants70EvidenceCEvaluatedOct 22, 2026
Rank12ModelACQwen3.5-35B-A3BAlibaba Cloud / Qwen TeamScore91.90%Percentile84.06%Participants70EvidenceCEvaluatedOct 22, 2026
Rank13ModelACQwen3.5-9BAlibaba Cloud / Qwen TeamScore91.50%Percentile82.61%Participants70EvidenceCEvaluatedOct 22, 2026
Rank14ModelGOGemma 3 27BGoogleScore90.40%Percentile81.16%Participants70EvidenceCEvaluatedOct 22, 2026
Rank15ModelNVNemotron Nano 9B v2NVIDIAScore90.30%Percentile79.71%Participants70EvidenceCEvaluatedOct 22, 2026
Rank16ModelGOGemma 3 4BGoogleScore90.20%Percentile78.26%Participants70EvidenceCEvaluatedOct 22, 2026
Rank17ModelMAKimi K2 InstructMoonshot AIScore89.80%Percentile76.81%Participants70EvidenceCEvaluatedOct 22, 2026
Rank18ModelMAKimi K2-Instruct-0905Moonshot AIScore89.80%Percentile75.36%Participants70EvidenceCEvaluatedOct 22, 2026
Rank19ModelACQwen3.5-4BAlibaba Cloud / Qwen TeamScore89.80%Percentile73.91%Participants70EvidenceCEvaluatedOct 22, 2026
Rank20ModelAMNova LiteAmazonScore89.70%Percentile72.46%Participants70EvidenceCEvaluatedOct 22, 2026
Rank21ModelMELongCat-Flash-ChatMeituanScore89.65%Percentile71.01%Participants70EvidenceCEvaluatedOct 22, 2026
Rank22ModelLAEXAONE 4.5 33BLG AI ResearchScore89.60%Percentile69.57%Participants70EvidenceCEvaluatedOct 22, 2026
Rank23ModelNVLlama 3.1 Nemotron Ultra 253B v1NVIDIAScore89.45%Percentile68.12%Participants70EvidenceCEvaluatedOct 22, 2026
Rank24ModelGOGemma 3 12BGoogleScore88.90%Percentile66.67%Participants70EvidenceCEvaluatedOct 22, 2026
Rank25ModelACQwen3-Next-80B-A3B-ThinkingAlibaba Cloud / Qwen TeamScore88.90%Percentile65.22%Participants70EvidenceCEvaluatedOct 22, 2026
Rank26ModelACQwen3-235B-A22B-Instruct-2507Alibaba Cloud / Qwen TeamScore88.70%Percentile63.77%Participants70EvidenceCEvaluatedOct 22, 2026
Rank27ModelMELlama 3.1 405B InstructMetaScore88.60%Percentile62.32%Participants70EvidenceCEvaluatedOct 22, 2026
Rank28ModelOPGPT-4.5OpenAIScore88.20%Percentile60.87%Participants70EvidenceCEvaluatedOct 22, 2026
Rank29ModelACQwen3 VL 235B A22B ThinkingAlibaba Cloud / Qwen TeamScore88.20%Percentile59.42%Participants70EvidenceCEvaluatedOct 22, 2026
Rank30ModelACQwen3-235B-A22B-Thinking-2507Alibaba Cloud / Qwen TeamScore87.80%Percentile57.97%Participants70EvidenceCEvaluatedOct 22, 2026

IFEval Highlights

The leading models and scores on this benchmark.

IFEval Score Distribution

A closer view of the leading scores on this benchmark.

IFEval

The Top AI Models for IFEval

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

What is IFEval?

What IFEval measures and how its scores work.

IFEval is an Instruction-Following Evaluation benchmark for large language models.

IFEval measures Score as a ratio for performance on prompts containing verifiable instructions and constraints.

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

Family
IFEval
Modality
text
Primary category
chat
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 IFEval.

Which model scores highest on IFEval?

Qwen3.5-27B is currently ranked first with 95.00%.

What are the top three models on IFEval?

The current leaders are Qwen3.5-27B (95.00%), Qwen3.7-Plus (94.60%), and Qwen3.7 Max (94.30%).

Which IFEval model has the lowest official input price?

Nova Micro has the lowest matched official input price at $0.04 input / $0.14 output per 1M tokens.

Which models are fastest among IFEval results?

The fastest matched records are Llama 3.3 70B Instruct (2,220.00 tok/s via Cerebras), Llama 3.1 8B Instruct (2,047.00 tok/s via Cerebras), and Llama 3.1 70B Instruct (1,204.00 tok/s via Cerebras).

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 IFEval measure?

IFEval measures Score as a ratio for performance on prompts containing verifiable instructions and constraints.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

70 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 #1Qwen3.5-27B95.00%
Rank #2Qwen3.7-Plus94.60%
Rank #3Qwen3.7 Max94.30%
Rank #4Qwen3.6 Plus94.30%

Ranking basisThis ifeval 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
    AC
    Qwen3.5-27BAlibaba Cloud / Qwen Team
    Score
    95.00%
    Price
    $0.30 input / $2.4 output per 1M tokens

    Strengths

    • Ranks #1 of 70 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures IFEval, not total model capability
  2. 02
    AC
    Qwen3.7-PlusAlibaba Cloud / Qwen Team
    Score
    94.60%
    Price
    $0.40 input / $1.6 output per 1M tokens

    Strengths

    • Ranks #2 of 70 compared models
    • 99th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures IFEval, not total model capability
  3. 03
    AC
    Alibaba Cloud / Qwen Team
    Score
    94.30%
    Price
    $2.5 input / $7.5 output per 1M tokens

    Strengths

    • Ranks #3 of 70 compared models
    • 97th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures IFEval, not total model capability
  4. 04
    AC
    Alibaba Cloud / Qwen Team
    Score
    94.30%
    Price
    $0.50 input / $3.0 output per 1M tokens

    Strengths

    • Ranks #4 of 70 compared models
    • 96th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures IFEval, not total model capability
  5. 05
    OP
    OpenAI
    Score
    93.90%
    Price
    $1.1 input / $4.4 output per 1M tokens
    Speed
    Up to 115.00 tok/s via Azure

    Strengths

    • Ranks #5 of 70 compared models
    • 94th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures IFEval, not total model capability

Selection summary

Best AI Models for IFEval

Qwen3.5-27B currently leads IFEval with 95.00%. 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.

Qwen3.7 Max
Qwen3.6 Plus
o3-mini
Benchmark rank #1Qwen3.5-27B95.00% · $0.30 input / $2.4 output per 1M tokens
Benchmark rank #2Qwen3.7-Plus94.60% · $0.40 input / $1.6 output per 1M tokens
Benchmark rank #3Qwen3.7 Max94.30% · $2.5 input / $7.5 output per 1M tokens