IFEval
Models answer about 500 prompts containing verifiable instructions such as word limits or required keywords. Code checks each instruction for strict and loose accuracy.
Frontier models now score near 90 percent, so it is close to saturation.
In the authors’ words
One core capability of Large Language Models (LLMs) is to follow natural language instructions. However, the evaluation of such abilities is not standardized: Human evaluations are expensive, slow, and not objectively reproducible, while LLM-based auto-evaluation is potentially biased or limited by the ability of the evaluator LLM. To overcome these issues, we introduce Instruction-Following Eval (IFEval) for large language models. IFEval is a straightforward and easy-to-reproduce evaluation benchmark. It focuses on a set of "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times". We identified 25 types of those verifiable instructions and constructed around 500 prompts, with each prompt containing one or more verifiable instructions. We show evaluation results of two widely available LLMs on the market. Our code and data can be found at https://github.com/google-research/google-research/tree/master/instruction_following_eval
- Level
- Everyday Most adults, without training
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Open-ended Free-form text, documents, designs or media judged as a whole.
Gemma 4 31B IT leads with 98.9%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 98.9
Gemma 4 31B IT
- 98.5
Gemma 4 26B A4B
- 97.4
DiffusionGemma 26B A4B
- 96.8 LFM2.5-24B-A2B
- 96.7 LFM3-1.7B
- 94.6
Qwen3.7-Plus
- 94.3
Qwen 3.6 Plus
- 94.3
Qwen3.7-Max
- 93.8
Olmo 3.1 32B Think
- 93.6
Spark-X2.6-4B
- 93.4
Qwen3.5-122B-A10B
- 93.3
ERNIE 5.0
- 93.2 Claude 3.7 Sonnet
- 92.6
Qwen3.5 397B-A17B
- 92.4
K-EXAONE 2.0
- 92.2
Keye-VL-2.0-30B-A3B
- 92.1
Llama 3.3 70B Instruct
- 91.8 LFM2.5-8B-A1B
- 91.5
Amazon Nova Premier
- 90.9
Cohere Command A
- 90.4
Gemma 3 27B
- 90.3 nvidia-nemotron-nano-9b-v2
- 90.2 Claude 3.5 Sonnet (October 2024)
- 90.2
Gemma 3 4B
- 90.0
LongCat-2.0
- 89.8 Kimi K2
- 89.7
K-EXAONE
- 89.7
Qwen3.5-Omni-Plus
- 89.7
LongCat-Flash-Chat
- 89.7 Granite 4.1 30B
- 89.6
EXAONE 4.5 33B
- 89.5
Llama-3.1-Nemotron-Ultra-253B-v1
- 89.4
Mistral Medium 3
- 89.4
Falcon-H1-34B
- 89.2
Llama-3.3-Nemotron-Super-49B-v1
- 89.1
MiniMax-Text-01
- 89.1 Apple Foundation Model Server (2025)
- 89.0
Olmo 3 32B Think
- 88.9
Gemma 3 12B
- 88.9
Qwen3-Next-80B-A3B
- 88.6
Llama-3.3-Nemotron-Super-49B-v1.5
- 88.6
Llama 3.1-405B
- 88.6
Hunyuan-TurboS
- 88.4 LFM2.5-1.2B-Thinking
- 88.2
Qwen3-VL-235B-A22B
- 88.0
ERNIE-4.5-300B-A47B
- 88.0
Solar Open 100B
- 87.9
Nemotron 3 Nano 4B
- 87.8
Solar Pro 2
- 87.8
Qwen3-235B-A22B-Thinking
- 87.5 Granite 4.0 H Small
- 87.5
Llama 3.1-70B
- 87.4
Seed-Thinking-v1.5
- 87.4 GPT-4.1
- 87.2 Kimi k1.5
- 87.0 Sarvam-M
- 86.9
LongCat-Flash-Thinking
- 86.8
sonar
- 86.7
Intern-S1
- 86.5
Qwen3-30B-A3B
- 86.3
Qwen2.5-VL-72B
- 86.3 GLM-4.5-Air
- 86.2 LFM2.5-1.2B-Instruct
- 86.1
DeepSeek-V3
- 86.1 GLM-4.5
- 86.0
Tulu 3 405B
- 86.0
Baichuan-M2-32B
- 85.9 Claude 3.5 Haiku
- 85.8
seed-oss-36b-instruct
- 85.6
OLMo 2 32B Instruct
- 85.4
Qwen3-14B
- 85.2
Ring-1T
- 85.0
Qwen3-32B
- 85.0
Qwen3-8B
- 84.9
Phi-4-Reasoning-plus
- 84.8
EXAONE 4.0 32B
- 84.8 Sarvam 105B
- 84.7
Hunyuan-A13B-Instruct
- 84.5 GLM-Z1-32B-0414
- 84.3
Qwen2.5-14B-Instruct-1M
- 84.3
ERNIE-4.5-21B-A3B-Thinking
- 84.1
Qwen2.5-72B
- 84.1 GPT-4.1 mini
- 83.7
InternVL3.5-241B-A28B
- 83.4
Qwen3-235B-A22B
- 83.4
Phi-4-reasoning
- 83.3
DeepSeek-R1
- 82.9
Mistral Small 3
- 82.4
LongCat-Flash-Omni
- 82.3 LFM2.5-VL-3B
- 82.2
STEP3-VL-10B
- 81.9
Qwen3-4B
- 81.5 INTELLECT-2
- 81.5 Hermes 4 405B
- 81.0
Qwen2.5-14B
- 81.0
Qwen3-Omni-30B-A3B
- 80.4
Llama 3.1-8B
- 80.2
Gemma 3 1B
- 79.5
Qwen2.5-32B
- 79.3
InternLM3-8B-Instruct
- 78.5
Aya Vision 32B
- 78.2
Nemotron Nano 12B v2 VL
- 77.9 Hermes 4.3 36B
- 77.6
Qwen2-72B
- 77.6 LFM2-8B-A1B
- 77.4 granite-4.0-1b
- 76.3
Qwen-Turbo
- 75.2
Apertus 70B
- 74.9
North Micro Vision Instruct
- 74.9 LFM2-1.2B
- 74.8 Granite 3.3 8B Instruct
- 74.5 GPT-4.1 nano
- 74.3 Granite 3.2 8B Instruct
- 72.5
Qwen3-1.7B
- 71.2
Qwen2.5-7B
- 70.1
Phi-4 Mini
- 70.1
OLMo 2 1B Instruct
- 67.7 GLM-4
- 67.0
AFM-4.5B
- 63.8
DeepSeek-V2 (MoE-236B, May 2024)
- 63.0
Phi-4
- 61.0 MiMo-7B-RL
- 57.6
Falcon-Arabic-7B
- 55.5
DeepSeek LLM 67B
- 53.5
BitNet b1.58 2B4T
- 51.2
Gemma 3 270M
- 1
Gemma 4 31B IT 98.9%
- 2
Gemma 4 26B A4B 98.5%
- 3
DiffusionGemma 26B A4B 97.4%
- 4 LFM2.5-24B-A2B 96.8%
- 5 LFM3-1.7B 96.7%
- 6
Qwen3.7-Plus 94.6%
- 7
Qwen 3.6 Plus 94.3%
- 8
Qwen3.7-Max 94.3%
- 9
Olmo 3.1 32B Think 93.8%
- 10
Spark-X2.6-4B 93.6%
- 11
Qwen3.5-122B-A10B 93.4%
- 12
ERNIE 5.0 93.3%
- 13 Claude 3.7 Sonnet 93.2%
- 14
Qwen3.5 397B-A17B 92.6%
- 15
K-EXAONE 2.0 92.4%
- 16
Keye-VL-2.0-30B-A3B 92.2%
- 17
Llama 3.3 70B Instruct 92.1%
- 18 LFM2.5-8B-A1B 91.8%
- 19
Amazon Nova Premier 91.5%
- 20
Cohere Command A 90.9%
- 21
Gemma 3 27B 90.4%
- 22 nvidia-nemotron-nano-9b-v2 90.3%
- 23 Claude 3.5 Sonnet (October 2024) 90.2%
- 24
Gemma 3 4B 90.2%
- 25
LongCat-2.0 90%
- 26 Kimi K2 Jul 2025 89.8%
- 27
K-EXAONE 89.7%
- 28
Qwen3.5-Omni-Plus 89.7%
- 29
LongCat-Flash-Chat 89.7%
- 30 Granite 4.1 30B 89.7%
- 31
EXAONE 4.5 33B 89.6%
- 32
Llama-3.1-Nemotron-Ultra-253B-v1 89.5%
- 33
Mistral Medium 3 89.4%
- 34
Falcon-H1-34B 89.4%
- 35
Llama-3.3-Nemotron-Super-49B-v1 89.2%
- 36
MiniMax-Text-01 89.1%
- 37 Apple Foundation Model Server (2025) 89.1%
- 38
Olmo 3 32B Think 89%
- 39
Gemma 3 12B 88.9%
- 40
Qwen3-Next-80B-A3B 88.9%
- 41
Llama-3.3-Nemotron-Super-49B-v1.5 88.6%
- 42
Llama 3.1-405B 88.6%
- 43
Hunyuan-TurboS 88.6%
- 44 LFM2.5-1.2B-Thinking 88.4%
- 45
Qwen3-VL-235B-A22B 88.2%
- 46
ERNIE-4.5-300B-A47B 88%
- 47
Solar Open 100B 88%
- 48
Nemotron 3 Nano 4B 87.9%
- 49
Solar Pro 2 87.8%
- 50
Qwen3-235B-A22B-Thinking Jul 2025 87.8%
- 51 Granite 4.0 H Small 87.5%
- 52
Llama 3.1-70B 87.5%
- 53
Seed-Thinking-v1.5 87.4%
- 54 GPT-4.1 87.4%
- 55 Kimi k1.5 87.2%
- 56 Sarvam-M 87%
- 57
LongCat-Flash-Thinking 86.9%
- 58
sonar 86.8%
- 59
Intern-S1 86.7%
- 60
Qwen3-30B-A3B 86.5%
- 61
Qwen2.5-VL-72B 86.3%
- 62 GLM-4.5-Air 86.3%
- 63 LFM2.5-1.2B-Instruct 86.2%
- 64
DeepSeek-V3 86.1%
- 65 GLM-4.5 86.1%
- 66
Tulu 3 405B 86%
- 67
Baichuan-M2-32B 86%
- 68 Claude 3.5 Haiku 85.9%
- 69
seed-oss-36b-instruct 85.8%
- 70
OLMo 2 32B Instruct 85.6%
- 71
Qwen3-14B 85.4%
- 72
Ring-1T 85.2%
- 73
Qwen3-32B 85%
- 74
Qwen3-8B 85%
- 75
Phi-4-Reasoning-plus 84.9%
- 76
EXAONE 4.0 32B 84.8%
- 77 Sarvam 105B 84.8%
- 78
Hunyuan-A13B-Instruct 84.7%
- 79 GLM-Z1-32B-0414 84.5%
- 80
Qwen2.5-14B-Instruct-1M 84.3%
- 81
ERNIE-4.5-21B-A3B-Thinking 84.3%
- 82
Qwen2.5-72B 84.1%
- 83 GPT-4.1 mini 84.1%
- 84
InternVL3.5-241B-A28B 83.7%
- 85
Qwen3-235B-A22B 83.4%
- 86
Phi-4-reasoning 83.4%
- 87
DeepSeek-R1 83.3%
- 88
Mistral Small 3 82.9%
- 89
LongCat-Flash-Omni 82.4%
- 90 LFM2.5-VL-3B 82.3%
- 91
STEP3-VL-10B 82.2%
- 92
Qwen3-4B 81.9%
- 93 INTELLECT-2 81.5%
- 94 Hermes 4 405B 81.5%
- 95
Qwen2.5-14B 81%
- 96
Qwen3-Omni-30B-A3B 81%
- 97
Llama 3.1-8B 80.4%
- 98
Gemma 3 1B 80.2%
- 99
Qwen2.5-32B 79.5%
- 100
InternLM3-8B-Instruct 79.3%
- 101
Aya Vision 32B 78.5%
- 102
Nemotron Nano 12B v2 VL 78.2%
- 103 Hermes 4.3 36B 77.9%
- 104
Qwen2-72B 77.6%
- 105 LFM2-8B-A1B 77.6%
- 106 granite-4.0-1b 77.4%
- 107
Qwen-Turbo 76.3%
- 108
Apertus 70B 75.2%
- 109
North Micro Vision Instruct 74.9%
- 110 LFM2-1.2B 74.9%
- 111 Granite 3.3 8B Instruct 74.8%
- 112 GPT-4.1 nano 74.5%
- 113 Granite 3.2 8B Instruct 74.3%
- 114
Qwen3-1.7B 72.5%
- 115
Qwen2.5-7B 71.2%
- 116
Phi-4 Mini 70.1%
- 117
OLMo 2 1B Instruct 70.1%
- 118 GLM-4 67.7%
- 119
AFM-4.5B 67%
- 120
DeepSeek-V2 (MoE-236B, May 2024) 63.8%
- 121
Phi-4 63%
- 122 MiMo-7B-RL 61%
- 123
Falcon-Arabic-7B 57.6%
- 124
DeepSeek LLM 67B 55.5%
- 125
BitNet b1.58 2B4T 53.5%
- 126
Gemma 3 270M 51.2%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 98.9% | Jul 2026 | Model card | |
| 2 | | 98.5% | Jul 2026 | Model card | |
| 3 | | 97.4% | Jul 2026 | Model card | |
| 4 | LFM2.5-24B-A2B | 96.8% | Oct 2026 | Model card | |
| 5 | LFM3-1.7B | 96.7% | Oct 2026 | Model card | |
| 6 | | 94.6% | Jun 2026 | Model card | |
| 7 | | 94.3% | Apr 2026 | Model card | |
| 8 | | 94.3% | May 2026 | Model card | |
| 9 | | 93.8% | Dec 2025 | Model card | |
| 10 | | 93.6% | Oct 2026 | Model card | |
| 11 | | 93.4% | Feb 2026 | Model card | |
| 12 | | 93.3% | Feb 2026 | Model card | |
| 13 | Claude 3.7 Sonnet | 93.2% | Feb 2025 | Model card | |
| 14 | | 92.6% | Feb 2026 | Model card | |
| 15 | | 92.4% | Jul 2026 | Model card | |
| 16 | | 92.2% | May 2026 | Model card | |
| 17 | | 92.1% | Dec 2024 | Model card | |
| 18 | LFM2.5-8B-A1B | 91.8% | May 2026 | Model card | |
| 19 | | 91.5% | Apr 2025 | Model card | |
| 20 | | 90.9% | Apr 2025 | Model card | |
| 21 | | 90.4% | Mar 2025 | Model card | |
| 22 | nvidia-nemotron-nano-9b-v2 | 90.3% | Aug 2025 | Model card | |
| 23 | Claude 3.5 Sonnet (October 2024) | 90.2% | Oct 2024 | Model card | |
| 24 | | 90.2% | Mar 2025 | Model card | |
| 25 | | 90% | Jul 2026 | Model card | |
| 26 | Kimi K2 Jul 2025 | 89.8% | Jul 2025 | Model card | |
| 27 | | 89.7% | Dec 2025 | Model card | |
| 28 | | 89.7% | Mar 2026 | Model card | |
| 29 | | 89.7% | Aug 2025 | Model card | |
| 30 | Granite 4.1 30B | 89.7% | Apr 2026 | Model card | |
| 31 | | 89.6% | Apr 2026 | Model card | |
| 32 | | 89.5% | Apr 2025 | Model card | |
| 33 | | 89.4% | May 2025 | Model card | |
| 34 | | 89.4% | May 2025 | Model card | |
| 35 | | 89.2% | Mar 2025 | Model card | |
| 36 | | 89.1% | Jan 2025 | Model card | |
| 37 | Apple Foundation Model Server (2025) | 89.1% | Jul 2025 | Model card | |
| 38 | | 89% | Nov 2025 | Model card | |
| 39 | | 88.9% | Mar 2025 | Model card | |
| 40 | | 88.9% | Sep 2025 | Model card | |
| 41 | | 88.6% | Jul 2025 | Model card | |
| 42 | | 88.6% | Jul 2024 | Model card | |
| 43 | | 88.6% | Feb 2025 | Model card | |
| 44 | LFM2.5-1.2B-Thinking | 88.4% | Jan 2026 | Model card | |
| 45 | | 88.2% | Sep 2025 | Model card | |
| 46 | | 88% | Jun 2025 | Model card | |
| 47 | | 88% | Dec 2025 | Model card | |
| 48 | | 87.9% | Mar 2026 | Model card | |
| 49 | | 87.8% | Jul 2025 | Model card | |
| 50 | | 87.8% | Jul 2025 | Model card | |
| 51 | Granite 4.0 H Small | 87.5% | Oct 2025 | Model card | |
| 52 | | 87.5% | Jul 2024 | Model card | |
| 53 | | 87.4% | Apr 2025 | Model card | |
| 54 | GPT-4.1 | 87.4% | Apr 2025 | Model card | |
| 55 | Kimi k1.5 | 87.2% | Jan 2025 | Model card | |
| 56 | Sarvam-M | 87% | May 2025 | Model card | |
| 57 | | 86.9% | Sep 2025 | Model card | |
| 58 | | 86.8% | Feb 2025 | Model card | |
| 59 | | 86.7% | Jul 2025 | Model card | |
| 60 | | 86.5% | May 2025 | Model card | |
| 61 | | 86.3% | Feb 2025 | Model card | |
| 62 | GLM-4.5-Air | 86.3% | Aug 2025 | Model card | |
| 63 | LFM2.5-1.2B-Instruct | 86.2% | Jan 2026 | Model card | |
| 64 | | 86.1% | Dec 2024 | Model card | |
| 65 | GLM-4.5 | 86.1% | Aug 2025 | Model card | |
| 66 | | 86% | Jan 2025 | Model card | |
| 67 | | 86% | Sep 2025 | Model card | |
| 68 | Claude 3.5 Haiku | 85.9% | Oct 2024 | Model card | |
| 69 | | 85.8% | Aug 2025 | Model card | |
| 70 | | 85.6% | Mar 2025 | Model card | |
| 71 | | 85.4% | May 2025 | Model card | |
| 72 | | 85.2% | Oct 2025 | Model card | |
| 73 | | 85% | May 2025 | Model card | |
| 74 | | 85% | May 2025 | Model card | |
| 75 | | 84.9% | Apr 2025 | Model card | |
| 76 | | 84.8% | Jul 2025 | Model card | |
| 77 | Sarvam 105B | 84.8% | Mar 2026 | Model card | |
| 78 | | 84.7% | Jun 2025 | Model card | |
| 79 | GLM-Z1-32B-0414 | 84.5% | Apr 2025 | Model card | |
| 80 | | 84.3% | Jan 2025 | Model card | |
| 81 | | 84.3% | Sep 2025 | Model card | |
| 82 | | 84.1% | Sep 2024 | Model card | |
| 83 | GPT-4.1 mini | 84.1% | Apr 2025 | Model card | |
| 84 | | 83.7% | Aug 2025 | Model card | |
| 85 | | 83.4% | Apr 2025 | Model card | |
| 86 | | 83.4% | Apr 2025 | Model card | |
| 87 | | 83.3% | Jan 2025 | Model card | |
| 88 | | 82.9% | Jan 2025 | Model card | |
| 89 | | 82.4% | Oct 2025 | Model card | |
| 90 | LFM2.5-VL-3B | 82.3% | Aug 2026 | Model card | |
| 91 | | 82.2% | Jan 2026 | Model card | |
| 92 | | 81.9% | May 2025 | Model card | |
| 93 | INTELLECT-2 | 81.5% | May 2025 | Model card | |
| 94 | Hermes 4 405B | 81.5% | Aug 2025 | Model card | |
| 95 | | 81% | Dec 2024 | Model card | |
| 96 | | 81% | Sep 2025 | Model card | |
| 97 | | 80.4% | Jul 2024 | Model card | |
| 98 | | 80.2% | Mar 2025 | Model card | |
| 99 | | 79.5% | Dec 2024 | Model card | |
| 100 | | 79.3% | Jan 2025 | Model card | |
| 101 | | 78.5% | May 2025 | Model card | |
| 102 | | 78.2% | Nov 2025 | Model card | |
| 103 | Hermes 4.3 36B | 77.9% | Dec 2025 | Model card | |
| 104 | | 77.6% | Jun 2024 | Model card | |
| 105 | LFM2-8B-A1B | 77.6% | Oct 2025 | Model card | |
| 106 | granite-4.0-1b | 77.4% | Oct 2025 | Model card | |
| 107 | | 76.3% | Dec 2024 | Model card | |
| 108 | | 75.2% | Sep 2025 | Model card | |
| 109 | | 74.9% | Aug 2026 | Model card | |
| 110 | LFM2-1.2B | 74.9% | Jul 2025 | Model card | |
| 111 | Granite 3.3 8B Instruct | 74.8% | Apr 2025 | Model card | |
| 112 | GPT-4.1 nano | 74.5% | Apr 2025 | Model card | |
| 113 | Granite 3.2 8B Instruct | 74.3% | Feb 2025 | Model card | |
| 114 | | 72.5% | May 2025 | Model card | |
| 115 | | 71.2% | Dec 2024 | Model card | |
| 116 | | 70.1% | Mar 2025 | Model card | |
| 117 | | 70.1% | Apr 2025 | Model card | |
| 118 | GLM-4 | 67.7% | Jan 2024 | Model card | |
| 119 | | 67% | Jul 2025 | Model card | |
| 120 | | 63.8% | May 2024 | Model card | |
| 121 | | 63% | Dec 2024 | Model card | |
| 122 | MiMo-7B-RL | 61% | Apr 2025 | Model card | |
| 123 | | 57.6% | Nov 2025 | Model card | |
| 124 | | 55.5% | Jan 2024 | Model card | |
| 125 | | 53.5% | Apr 2025 | Model card | |
| 126 | | 51.2% | Aug 2025 | Model card |
11 models have held number one since its release in Nov 2023
Lab claims count from the day the card came out, independent runs from the model’s release.
The best score passed 85% in Jul 2024
All the scores we have, one dot each, so a model tested at several settings appears more than once.
6 labs put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 27 Oct 2026 | LFM3-1.7B | 96.7 | IFEval |
| 22 Oct 2026 | 93.6 | IFEval | |
| 13 Oct 2026 | LFM2.5-24B-A2B | 96.8 | IFEval |
| 12 Aug 2026 | LFM2.5-VL-3B | 82.3 | IFEval |
| 12 Aug 2026 | 74.9 | IFEval | |
| 31 Jul 2026 | 92.4 | IFEval | |
| 5 Jul 2026 | 90 | IFEval | |
| 1 Jun 2026 | 94.6 | IFEval | |
| 28 May 2026 | LFM2.5-8B-A1B | 91.8 | IFEval |
| 25 May 2026 | 92.2 | IFEval | |
| 20 May 2026 | 94.3 | IFEval | |
| 29 Apr 2026 | Granite 4.1 30B | 89.7 | IFEval Avg |
| 9 Apr 2026 | 89.6 | IFEval | |
| 2 Apr 2026 | 94.3 | IFEvalstrict prompt | |
| 30 Mar 2026 | 89.7 | IFEval | |
| 16 Mar 2026 | 87.9 | IFEVAL-Prompt (reasoning on); Instruction 92 | |
| 6 Mar 2026 | Sarvam 105B | 84.8 | IF Eval |
| 24 Feb 2026 | 93.4 | IFEval | |
| 16 Feb 2026 | 92.6 | IFEval | |
| 6 Feb 2026 | 93.3 | IFEval | |
| 20 Jan 2026 | LFM2.5-1.2B-Thinking | 88.4 | IFEval (± 0.35) |
| 5 Jan 2026 | LFM2.5-1.2B-Instruct | 86.2 | IFEval |
| 31 Dec 2025 | 88 | IFEval | |
| 31 Dec 2025 | 89.7 | IFEval | |
| 12 Dec 2025 | 93.8 | IFEval | |
| 3 Dec 2025 | Hermes 4.3 36B | 77.9 | IFEval |
| 20 Nov 2025 | 89 | IFEval | |
| 31 Oct 2025 | 82.4 | IFEval(acc), text | |
| 28 Oct 2025 | granite-4.0-1b | 77.4 | IFEval Average |
| 7 Oct 2025 | LFM2-8B-A1B | 77.6 | IFEval |
| 2 Oct 2025 | Granite 4.0 H Small | 87.5 | IFEval Average |
| 23 Sep 2025 | 88.2 | IFEval | |
| 22 Sep 2025 | 81 | IFEval | |
| 21 Sep 2025 | 86.9 | IFEval(strict prompt) | |
| 11 Sep 2025 | 88.9 | IFEval | |
| 9 Sep 2025 | 84.3 | IFEval (Prompt Strict Accuracy) | |
| 29 Aug 2025 | 89.7 | IFEval(acc) | |
| 26 Aug 2025 | Hermes 4 405B | 81.5 | IFEval (Loose) (reasoning) |
| 25 Aug 2025 | 83.7 | IFEval | |
| 20 Aug 2025 | 85.8 | IFEval | |
| 18 Aug 2025 | 90.3 | IFEval (Instruction Strict) | |
| 14 Aug 2025 | 51.2 | IF Eval 0-shot (IT) | |
| 29 Jul 2025 | 67 | IFEval Inst Strict | |
| 25 Jul 2025 | 88.6 | IFEval (Strict:Instruction) | |
| 25 Jul 2025 | 87.8 | IFEval | |
| 24 Jul 2025 | 86.7 | IFEval | |
| 15 Jul 2025 | 84.8 | IFEval 84.8 non-reasoning (83.7 reasoning) | |
| 11 Jul 2025 | Kimi K2 | 89.8 | IFEval, Prompt Strict |
| 10 Jul 2025 | LFM2-1.2B | 74.9 | IFEval |
| 10 Jul 2025 | 87.8 | IFEval | |
| 30 Jun 2025 | 88 | IFEval | |
| 27 Jun 2025 | 84.7 | IF-Eval | |
| 23 May 2025 | Sarvam-M | 87 | IFEval (0.87) |
| 20 May 2025 | 89.4 | IFEval | |
| 11 May 2025 | INTELLECT-2 | 81.5 | IFEval |
| 7 May 2025 | 89.4 | IfEval 0-shot | |
| 30 Apr 2025 | 83.4 | IFEval Strict | |
| 30 Apr 2025 | 91.5 | IFEval | |
| 30 Apr 2025 | 84.9 | IFEval Strict | |
| 29 Apr 2025 | 70.1 | IFEval | |
| 29 Apr 2025 | MiMo-7B-RL | 61 | IF-Eval (Prompt Strict) |
| 29 Apr 2025 | 83.4 | IFEval strict prompt | |
| 16 Apr 2025 | Granite 3.3 8B Instruct | 74.8 | IFEval |
| 15 Apr 2025 | 53.5 | IFEval | |
| 14 Apr 2025 | GPT-4.1 nano | 74.5 | IFEval |
| 14 Apr 2025 | GPT-4.1 mini | 84.1 | IFEval |
| 14 Apr 2025 | GLM-Z1-32B-0414 | 84.5 | IFEval |
| 14 Apr 2025 | GPT-4.1 | 87.4 | IFEval |
| 10 Apr 2025 | 87.4 | IFEval | |
| 7 Apr 2025 | 89.5 | IFEval Strict:Instruction (reasoning on) | |
| 18 Mar 2025 | 89.2 | IFEval Strict:Instruction (reasoning off) | |
| 13 Mar 2025 | IFEval (chart only) | ||
| 13 Mar 2025 | 85.6 | IFEval | |
| 12 Mar 2025 | 90.4 | IFEval 0-shot (IT) | |
| 6 Mar 2025 | IFEval | ||
| 27 Feb 2025 | 88.6 | IF-Eval | |
| 26 Feb 2025 | Granite 3.2 8B Instruct | 74.3 | IFEval |
| 24 Feb 2025 | Claude 3.7 Sonnet | 93.2 | Instruction-following IFEval (64K extended thinking) |
| 11 Feb 2025 | 86.8 | IFEval | |
| 30 Jan 2025 | 86 | IFEval (prompt loose) | |
| 30 Jan 2025 | 82.9 | ifeval | |
| 20 Jan 2025 | Kimi k1.5 | 87.2 | IF-Eval (Prompt Strict), short-CoT |
| 20 Jan 2025 | 83.3 | IF-Eval (Prompt Strict) | |
| 15 Jan 2025 | 89.1 | IFEval (avg) | |
| 13 Jan 2025 | 79.3 | IFEval(Prompt-Strict) | |
| 26 Dec 2024 | 86.1 | IF-Eval (Prompt Strict) | |
| 12 Dec 2024 | 63 | IFEval | |
| 6 Dec 2024 | 92.1 | IFEval | |
| 22 Oct 2024 | Claude 3.5 Sonnet (October 2024) | 90.2 | IFEval |
| 19 Sep 2024 | 84.1 | IFeval strict-prompt | |
| 23 Jul 2024 | 88.6 | IFEval | |
| 7 Jun 2024 | 77.6 | IFEval (Prompt Strict-Acc.) | |
| 16 Jan 2024 | GLM-4 | 67.7 | IFEval Prompt级别、英文 (prompt-level, English) |
| 29 Nov 2023 | Instruction Following Evaluation (prompt-level loose) |
86 system cards report it, from 31 labs
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 5 Aug 2026 | | 92.4 | IFEval (Table 6, Reasoning mode) | Capabilities |
| 31 Jul 2026 | | 97.4 | IFEval, TD, 94.5 no-think (Table 3) | Capabilities |
| 24 Jul 2026 | | 98.9 | IFEval, thinking (Table 5) | Capabilities |
| 24 Jul 2026 | | 98.5 | IFEval, thinking (Table 5) | Capabilities |
| 9 Jun 2026 | | 92.2 | IFEval (Figure 5, printed value) | Capabilities |
| 17 Apr 2026 | | 89.7 | IFEval (Table 4) | Capabilities |
| 9 Apr 2026 | | 89.6 | IFEval (Table 3, Reasoning mode) | Capabilities |
| 4 Feb 2026 | | 93.3 | IFEval (Table 2) | Capabilities |
| 14 Jan 2026 | | 82.2 | IFEval, SeRe (Table 2) | Capabilities |
| 11 Jan 2026 | | 88 | IFEval (Table 6) | Capabilities |
| 5 Jan 2026 | | 89.7 | IFEval, prompt-strict (Table 3, Reasoning mode). Non-reasoning 85.5 (Table 4) | Capabilities |
| 15 Dec 2025 | | 89 | IFEval, Final Think 3.0 column (Tables 14 and 24) | Capabilities |
| 15 Dec 2025 | | 93.8 | IFEval (Tables 1, 14 and 24) | Capabilities |
| 28 Nov 2025 | LFM2-8B-A1B technical report | 77.6 | IFEval, LFM2-8B-A1B (Table 7) | Capabilities |
| 28 Nov 2025 | LFM2-1.2B technical report | 74.9 | IFEval, LFM2-1.2B (Table 6) | Capabilities |
| 26 Nov 2025 | | 88.2 | IFEval (Table 6) | Capabilities |
| 8 Nov 2025 | | 57.6 | IFEval 0-shot, English (Table 8) | Capabilities |
| 6 Nov 2025 | | 78.2 | IFEval_prompt_strict, reasoning on, SFT stage 4 (Table 6). Instruction strict 84.7 | Capabilities |
| 31 Oct 2025 | | 82.4 | IFEval (acc) (Table 13) | Capabilities |
| 21 Oct 2025 | | 85.2 | IFEval (Table 1) | Capabilities |
| 8 Oct 2025 | | 70.1 | IFE, IFEval (Table 7, Table 23) | Capabilities |
| 23 Sep 2025 | | 86.9 | IFEval(strict prompt) (Table 2) | Capabilities |
| 22 Sep 2025 | | 81 | IFEval, Instruct (Table 4). Thinking 85.1 (Table 5) | Capabilities |
| 17 Sep 2025 | | 75.2 | IFEval, prompt-level loose (Table 19) | Capabilities |
| 2 Sep 2025 | | 86 | IFEval (Table 1) | Capabilities |
| 1 Sep 2025 | | 89.7 | IFEval (acc) (Table 3) | Capabilities |
| 25 Aug 2025 | | 83.7 | IFEval (Table 2) | Capabilities |
| 25 Aug 2025 | Hermes 4 405B technical report | 81.5 | IFEval (Loose), reasoning mode (Table 3). Non-reasoning 84.9 | Capabilities |
| 21 Aug 2025 | | 86.7 | IFEval (Table 2) | Capabilities |
| 8 Aug 2025 | GLM-4.5 technical report | 86.1 | IFEval (Prompt Strict) (Table 6) | Capabilities |
| 8 Aug 2025 | GLM-4.5-Air technical report | 86.3 | IFEval (Prompt Strict) (Table 6) | Capabilities |
| 30 Jul 2025 | | 89.4 | IFEval, instruction and prompt average accuracy (Table 24) | Capabilities |
| 21 Jul 2025 | Kimi K2 technical report | 89.8 | IFEval (Prompt Strict) (Table 3) | Capabilities |
| 17 Jul 2025 | Apple Foundation Model Server (2025) technical report | 89.1 | IFEval (instruct), AFM Server at 16 bits (Table 3). AFM Server Opt at 3.6 bits per weight 90.2 | Capabilities |
| 15 Jul 2025 | | 83.7 | IFEval (Table 3, Reasoning). Non-reasoning 84.8 (Table 4) | Capabilities |
| 29 Jun 2025 | | 88 | IFEval (Table 5) | Capabilities |
| 26 Jun 2025 | | 84.7 | IF-Eval, slow-thinking (Table 3). Fast-thinking 84.4 (Table 4) | Capabilities |
| 14 May 2025 | | 85 | IFEval strict prompt, thinking mode (Table 17). Non-thinking 83.0 (Table 18) | Capabilities |
| 14 May 2025 | | 81.9 | IFEval strict prompt, thinking mode (Table 17). Non-thinking 81.2 (Table 18) | Capabilities |
| 14 May 2025 | | 85 | IFEval strict prompt, thinking mode (Table 13). Non-thinking 83.2 (Table 14) | Capabilities |
| 14 May 2025 | | 86.5 | IFEval strict prompt, thinking mode (Table 15). Non-thinking 83.7 (Table 16) | Capabilities |
| 14 May 2025 | | 83.4 | IFEval strict prompt, thinking mode (Table 11). Non-thinking 83.2 (Table 12) | Capabilities |
| 14 May 2025 | | 85.4 | IFEval strict prompt, thinking mode (Table 15). Non-thinking 84.8 (Table 16) | Capabilities |
| 14 May 2025 | | 72.5 | IFEval strict prompt, thinking mode (Table 19). Non-thinking 68.2 (Table 20) | Capabilities |
| 13 May 2025 | | 78.5 | IFEval, English only (Table 5) | Capabilities |
| 12 May 2025 | MiMo-7B-RL technical report | 61 | IF-Eval (Prompt Strict), average of 8 runs (Table 4) | Capabilities |
| 12 May 2025 | INTELLECT-2 technical report | 81.5 | IFEval, evalchemy defaults, target length 10,000 tokens (Table 1) | Capabilities |
| 2 May 2025 | | 89.2 | IFEval (Strict-Instruction), reasoning on, reasoning off 89.0 (Table 4) | Capabilities |
| 2 May 2025 | | 88.9 | IFEval (Strict-Instruction), reasoning on, reasoning off 89.5 (Table 5) | Capabilities |
| 30 Apr 2025 | | 83.4 | IFEval Strict (Table 2) | Capabilities |
| 30 Apr 2025 | | 84.9 | IFEval Strict (Table 2) | Capabilities |
| 30 Apr 2025 | | 91.5 | IFEval, instruction-level loose accuracy, +/-2.4 (Table 1) | Capabilities |
| 16 Apr 2025 | | 53.5 | IFEval (0-shot; Instruct-Strict) (Table 1, Table 2) | Capabilities |
| 10 Apr 2025 | | 87.4 | IFEval (Table 2) | Capabilities |
| 1 Apr 2025 | | 90.9 | IFEval, mean of prompt-level and instruction-level strict accuracy (Table 3) | Capabilities |
| 25 Mar 2025 | | 90.2 | IFEval, IT, 0-shot (Table 18) | Capabilities |
| 25 Mar 2025 | | 90.4 | IFEval, IT, 0-shot (Table 18) | Capabilities |
| 25 Mar 2025 | | 80.2 | IFEval, IT, 0-shot (Table 18) | Capabilities |
| 25 Mar 2025 | | 88.9 | IFEval, IT, 0-shot (Table 18) | Capabilities |
| 3 Mar 2025 | | 70.1 | IFEval (0-shot) (Table 7) | Capabilities |
| 19 Feb 2025 | | 86.3 | IFEval (Table 4) | Capabilities |
| 26 Jan 2025 | | 84.3 | IFEval, strict prompt-level accuracy (Table 6) | Capabilities |
| 20 Jan 2025 | Kimi k1.5 technical report | 87.2 | IF-Eval (Prompt Strict), short-CoT (Table 3) | Capabilities |
| 20 Jan 2025 | | 83.3 | IF-Eval (Prompt Strict), DeepSeek-R1 (Table 8, same as final column of Table 3) | Capabilities |
| 14 Jan 2025 | | 89.1 | IFEval (avg) (Table 8) | Capabilities |
| 31 Dec 2024 | | 85.6 | IFEval, prompt loose (Tables 7 and 16) | Capabilities |
| 26 Dec 2024 | | 86.1 | IF-Eval (Prompt Strict), DeepSeek-V3 (Table 6) | Capabilities |
| 19 Dec 2024 | | 71.2 | IFEval, Qwen2.5-7B-Instruct (Table 8) | Capabilities |
| 19 Dec 2024 | | 84.1 | IFEval, Qwen2.5-72B-Instruct (Table 6) | Capabilities |
| 19 Dec 2024 | | 79.5 | IFEval, Qwen2.5-32B-Instruct (Table 7) | Capabilities |
| 19 Dec 2024 | | 81 | IFEval, Qwen2.5-14B-Instruct (Table 7) | Capabilities |
| 19 Dec 2024 | | 76.3 | IFEval, Qwen2.5-Turbo (Table 7) | Capabilities |
| 12 Dec 2024 | | 63 | IFEval (Table 1) | Capabilities |
| 6 Dec 2024 | | 92.1 | IFEval (Instruction tuned models table) | Capabilities |
| 23 Nov 2024 | | 80.4 | IFEval, post-trained (Table 2) | Capabilities |
| 23 Nov 2024 | | 87.5 | IFEval, post-trained (Table 2) | Capabilities |
| 23 Nov 2024 | | 88.6 | IFEval, post-trained (Table 2) | Capabilities |
| 22 Nov 2024 | | 86 | IFEval (loose prompt), RLVR (Table 4) | Capabilities |
| 22 Oct 2024 | Claude 3.5 Sonnet (October 2024) model card | 90.2 | IFEval (Table 7) | Capabilities |
| 22 Oct 2024 | Claude 3.5 Haiku model card | 85.9 | IFEval (Table 8) | Capabilities |
| 23 Jul 2024 | | 80.4 | IFEval, instruction tuned (Instruction tuned models table) | Capabilities |
| 23 Jul 2024 | | 87.5 | IFEval, instruction tuned (Instruction tuned models table) | Capabilities |
| 23 Jul 2024 | | 88.6 | IFEval, instruction tuned (Instruction tuned models table) | Capabilities |
| 15 Jul 2024 | | 77.6 | IFEval strict-prompt, Qwen2-72B-Instruct (Table 6) | Capabilities |
| 7 May 2024 | | 63.8 | IFEval 0-shot, DeepSeek-V2 Chat (RL) (Table 3) | Capabilities |
| 5 Jan 2024 | | 55.5 | IFEval prompt-level loose, held-out (Table 9) | Capabilities |
Versions
More in Instructions and chat
- IFBench In use 58 new out-of-domain verifiable output constraints. Scored by programmatic constraint checks (strict or loose).
Nemotron-Cascade 2 30B-A3B 82.9%
- MultiChallenge In use Multi-turn conversations testing instruction retention, inference memory, versioned editing and self-coherence. Graded by LLM judge with rubrics. GPT-6.1 Sol 82%
- Multi-IF In use Models follow verifiable instructions over three-turn conversations in eight languages, extending IFEval. Scored by instruction accuracy at each turn. LFM3-1.7B 89.1%
- AdvancedIF In use Models respond to over 1,600 prompts with complex, multi-turn, and system-level instructions. A judge model checks each response against expert-written rubrics.
MAI-Thinking-1 85%
- IFStruct In use Models generate JSON or YAML that must follow a requested schema and phrasing constraints, without constrained decoding. A response passes only if every structural constraint holds. LFM3-1.7B 93.7%
- NV Chat Benchmark In use Models answer chat prompts from an internal NVIDIA set. The Nemotron 3.5 Super VL card labels the score an LMArena v3 proxy.
Nemotron 3.5 Super VL 52.7