All evaluations

IFBench Benchmark Leaderboard

A benchmark evaluating precise instruction-following generalization on 58 diverse, verifiable out-of-domain constraints that test models' ability to follow specific output requirements.

IFBench addresses the problem that current language models strongly overfit to a small set of verifiable constraints and cannot generalize well to unseen output constraints, a critical skill for practical AI applications.
The benchmark introduces 58 new, diverse, and challenging verifiable constraints to test precise instruction-following generalization, going beyond existing benchmarks that focus on a limited set of constraint types.
Developed by the Allen Institute for AI, IFBench uses reinforcement learning with verifiable rewards (RLVR) to improve instruction following and includes 29 additional hand-annotated training constraints with verification functions.

All evaluations are conducted independently by Artificial Analysis. More information can be found on our Intelligence Benchmarking Methodology page.

Publication

View on arXiv

Generalizing Verifiable Instruction Following

Valentina Pyatkin, Saumya Malik, Victoria Graf, Hamish Ivison, Shengyi Huang, Pradeep Dasigi, Nathan Lambert, Hannaneh Hajishirzi.

A crucial factor for successful human and AI interaction is the ability of language models or chatbots to follow human instructions precisely. A common feature of instructions are output constraints like "only answer with yes or no" or "mention the word 'abrakadabra' at least 3 times" that the user adds to craft a more useful answer. Even today's strongest models struggle with fulfilling such constraints. We find that most models strongly overfit on a small set of verifiable constraints from the benchmarks that test these abilities, a skill called precise instruction following, and are not able to generalize well to unseen output constraints. We introduce a new benchmark, IFBENCH, to evaluate precise instruction following generalization on 58 new, diverse, and challenging verifiable out-of-domain constraints. In addition, we perform an extensive analysis of how and on what data models can be trained to improve precise instruction following generalization. Specifically, we carefully design constraint verification modules and show that reinforcement learning with verifiable rewards (RLVR) significantly improves instruction following. In addition to IFBENCH, we release 29 additional new hand-annotated training constraints and verification functions, RLVR training prompts, and code.

IFBench

Grok 4.20 0309 (Reasoning) scores the highest on IFBench with a score of 82.9%, followed by Grok 4.20 0309 v2 (Reasoning) with a score of 81.2%, and Nemotron Cascade 2 30B A3B with a score of 80.4%

IFBench Benchmark Leaderboard: Results

Independently benchmarked by Artificial Analysis

IFBench Benchmark Leaderboard: Token Usage

Tokens used to run the evaluation
Input tokens
Reasoning tokens
Answer tokens

The total number of tokens used to run the evaluation, including input tokens (prompt), reasoning tokens (for reasoning models), and answer tokens (final response).

IFBench Benchmark Leaderboard: Cost Breakdown

Cost (USD) to run the evaluation
Input cost
Reasoning cost
Answer cost

The cost to run the evaluation, calculated using the model's input and output token pricing and the number of tokens used.

IFBench Benchmark Leaderboard: Score vs. Release Date

Most attractive region
Alibaba
Amazon
Anthropic
DeepSeek
Google
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Meta
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Mistral
NVIDIA
OpenAI
Upstage
xAI
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Z AI

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