Multi-Challenge

MultiChallenge is a realistic multi-turn conversation evaluation benchmark that challenges frontier LLMs across four key categories: instruction retention (maintaining instructions throughout conversations), inference memory (recalling and connecting details from previous turns), reliable versioned editing (adapting to evolving instructions during collaborative editing), and self-coherence (avoiding contradictions in responses). The benchmark evaluates models on sustained, contextually complex dialogues across diverse topics including travel planning, technical documentation, and professional communication.

GPT-5 from OpenAI currently leads the Multi-Challenge leaderboard with a score of 0.696 across 24 evaluated AI models.

OpenAIGPT-5 leads with 69.6%, followed by Alibaba Cloud / Qwen TeamQwen3.5-397B-A17B at 67.6% and StepFunStep3-VL-10B at 62.6%.

Progress Over Time

Interactive timeline showing model performance evolution on Multi-Challenge

State-of-the-art frontier
Open
Proprietary

Multi-Challenge Leaderboard

24 models
ContextCostLicense
1
OpenAI
OpenAI
2
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
397B262K$0.60 / $3.60
310B
4
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
122B262K$0.40 / $3.20
5
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
27B262K$0.30 / $2.40
6
OpenAI
OpenAI
7
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
35B262K$0.25 / $2.00
8120B
9
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
9B
10
Moonshot AI
Moonshot AI
1.0T
101.0T
12
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
4B
13456B
13456B
15
OpenAI
OpenAI
16
OpenAI
OpenAI
17
OpenAI
OpenAI
128K$2.50 / $10.00
18
OpenAI
OpenAI
1932B262K$0.06 / $0.24
20
OpenAI
OpenAI
1.0M$2.00 / $8.00
211.0M$0.40 / $1.60
22
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
2B
23
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
800M
241.0M$0.10 / $0.40
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FAQ

Common questions about Multi-Challenge.

What is the Multi-Challenge benchmark?

MultiChallenge is a realistic multi-turn conversation evaluation benchmark that challenges frontier LLMs across four key categories: instruction retention (maintaining instructions throughout conversations), inference memory (recalling and connecting details from previous turns), reliable versioned editing (adapting to evolving instructions during collaborative editing), and self-coherence (avoiding contradictions in responses). The benchmark evaluates models on sustained, contextually complex dialogues across diverse topics including travel planning, technical documentation, and professional communication.

What is the Multi-Challenge leaderboard?

The Multi-Challenge leaderboard ranks 24 AI models based on their performance on this benchmark. Currently, GPT-5 by OpenAI leads with a score of 0.696. The average score across all models is 0.478.

What is the highest Multi-Challenge score?

The highest Multi-Challenge score is 0.696, achieved by GPT-5 from OpenAI.

How many models are evaluated on Multi-Challenge?

24 models have been evaluated on the Multi-Challenge benchmark, with 0 verified results and 24 self-reported results.

Where can I find the Multi-Challenge paper?

The Multi-Challenge paper is available at https://arxiv.org/abs/2501.17399. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does Multi-Challenge cover?

Multi-Challenge is categorized under communication and reasoning. The benchmark evaluates text models.

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