Multi-IF

Multi-IF benchmarks LLMs on multi-turn and multilingual instruction following. It expands upon IFEval by incorporating multi-turn sequences and translating English prompts into 7 other languages, resulting in 4,501 multilingual conversations with three turns each. The benchmark reveals that current leading LLMs struggle with maintaining accuracy in multi-turn instructions and shows higher error rates for non-Latin script languages.

Qwen3-235B-A22B-Thinking-2507 from Alibaba Cloud / Qwen Team currently leads the Multi-IF leaderboard with a score of 0.806 across 20 evaluated AI models.

Paper

Alibaba Cloud / Qwen TeamQwen3-235B-A22B-Thinking-2507 leads with 80.6%, followed by OpenAIo3-mini at 79.5% and Alibaba Cloud / Qwen TeamQwen3 VL 235B A22B Thinking at 79.1%.

Progress Over Time

Interactive timeline showing model performance evolution on Multi-IF

State-of-the-art frontier
Open
Proprietary

Multi-IF Leaderboard

20 models
ContextCostLicense
1
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
235B262K$0.30 / $3.00
2
OpenAI
OpenAI
200K$1.10 / $4.40
3
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B262K$0.45 / $3.49
4
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
5
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
80B66K$0.15 / $1.50
6
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
235B262K$0.15 / $0.80
7
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
236B262K$0.30 / $1.49
8
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
80B66K$0.15 / $1.50
9
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.18 / $2.09
9
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B262K$0.08 / $0.50
11
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B262K$0.10 / $1.00
12
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B262K$0.20 / $1.00
13
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B128K$0.10 / $0.44
14
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
33B
15
OpenAI
OpenAI
1.0M$2.00 / $8.00
15
OpenAI
OpenAI
128K$75.00 / $150.00
171.0M$0.40 / $1.60
18
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
31B262K$0.20 / $0.70
19
OpenAI
OpenAI
128K$2.50 / $10.00
201.0M$0.10 / $0.40
Notice missing or incorrect data?

FAQ

Common questions about Multi-IF.

What is the Multi-IF benchmark?

Multi-IF benchmarks LLMs on multi-turn and multilingual instruction following. It expands upon IFEval by incorporating multi-turn sequences and translating English prompts into 7 other languages, resulting in 4,501 multilingual conversations with three turns each. The benchmark reveals that current leading LLMs struggle with maintaining accuracy in multi-turn instructions and shows higher error rates for non-Latin script languages.

What is the Multi-IF leaderboard?

The Multi-IF leaderboard ranks 20 AI models based on their performance on this benchmark. Currently, Qwen3-235B-A22B-Thinking-2507 by Alibaba Cloud / Qwen Team leads with a score of 0.806. The average score across all models is 0.729.

What is the highest Multi-IF score?

The highest Multi-IF score is 0.806, achieved by Qwen3-235B-A22B-Thinking-2507 from Alibaba Cloud / Qwen Team.

How many models are evaluated on Multi-IF?

20 models have been evaluated on the Multi-IF benchmark, with 0 verified results and 20 self-reported results.

Where can I find the Multi-IF paper?

The Multi-IF paper is available at https://arxiv.org/abs/2410.15553. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does Multi-IF cover?

Multi-IF is categorized under communication, instruction following, language, reasoning, and structured output. The benchmark evaluates text models with multilingual support.

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