# MultiChallenge > Source: Benchize (https://benchize.com) — https://benchize.com/benchmarks/scale%3Amultichallenge. When citing or reusing this content, credit "Benchize" and link to https://benchize.com/benchmarks/scale%3Amultichallenge. Conducting multi-turn conversations with human users is a common yet challenging task for LLMs. It is challenging because conducting multi-turn conversations requires not only accurate instruction following, but also careful attention allocation of conversation context, and more importantly, near or surpassing human in-context reasoning capability at the same time. Despite the growing demand and complexity of multi-turn LLM conversations with humans, there are limited comprehensive evaluation frameworks designed for multi-turn scenarios. Some of them, such as the widely adopted MT-Bench[1], are saturated by frontier LLMs with near-perfect results. Others[2] focus more on multi-turn explicit instruction following, missing the opportunity to assess the actual set of mixed model capabilities required in conducting natural multi-turn conversations with human users. To bridge this gap, we present MultiChallenge, a pioneering benchmark evaluating large language models (LLMs) on conducting multi-turn conversations with human users, a crucial yet underexamined capability for their applications. MultiChallenge identifies four categories of challenges in multi-turn conversations that are not only common and realistic among current human-LLM interactions, but are also challenging to all current frontier LLMs. The 4 challenges are instruction retention, inference memory of user information, reliable versioned editing, and self-coherence. All 4 challenges require accurate instruction-following, context allocation, and in-context reasoning capabilities at the same time. Canonical URL: https://benchize.com/benchmarks/scale%3Amultichallenge Last updated: Wed, 21 Oct 2026 18:52:20 GMT Reproduction and quotation must name Benchize, link to the canonical HTML URL, and retain individual source citations. Conducting multi-turn conversations with human users is a common yet challenging task for LLMs. It is challenging because conducting multi-turn conversations requires not only accurate instruction following, but also careful attention allocation of conversation context, and more importantly, near or surpassing human in-context reasoning capability at the same time. Despite the growing demand and complexity of multi-turn LLM conversations with humans, there are limited comprehensive evaluation frameworks designed for multi-turn scenarios. Some of them, such as the widely adopted MT-Bench[1], are saturated by frontier LLMs with near-perfect results. Others[2] focus more on multi-turn explicit instruction following, missing the opportunity to assess the actual set of mixed model capabilities required in conducting natural multi-turn conversations with human users. To bridge this gap, we present MultiChallenge, a pioneering benchmark evaluating large language models (LLMs) on conducting multi-turn conversations with human users, a crucial yet underexamined capability for their applications. MultiChallenge identifies four categories of challenges in multi-turn conversations that are not only common and realistic among current human-LLM interactions, but are also challenging to all current frontier LLMs. The 4 challenges are instruction retention, inference memory of user information, reliable versioned editing, and self-coherence. All 4 challenges require accurate instruction-following, context allocation, and in-context reasoning capabilities at the same time. Instruction following · Models tested: 11 · Score publishers: [scale](https://labs.scale.com/leaderboard/multichallenge) ## Model results | # | Model | Score | ± | | | --- | --- | --- | --- | --- | | 1 | [GPT-6.1 Sol](https://benchize.com/models/gpt-6-1-sol.md) | 81.95source score | CI 77.79–86.11 | | | ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 81.95 \| source_score \| reported \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration GPT 6.1 Sol sourceModelVersion gpt-6.1-sol thinkingEnabled Not reported ### Reported statistics Uncertainty Confidence interval: 77.79–86.11 contaminationWarning Not reported rank 1 reportedConfidenceHalfWidth 4.16 ### Source [Original source](https://labs.scale.com/leaderboard/multichallenge) Source scale Original model label GPT 6.1 Sol Observed 2026-10-21T16:35:37.135Z Evidence SHA-256 `92e84e933979bccb14193567e45702ffc95189cc48216f6af220e7e235bfa3a0` Source locator `/rows/0` | | | | | | 2 | [Muse Spark 1.1](https://benchize.com/models/muse-spark-1-1.md) | 75.3source score | CI 74.7–75.9 | | | ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 75.3 \| source_score \| reported \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration Muse Spark 1.1 sourceModelVersion Not reported thinkingEnabled Not reported ### Reported statistics Uncertainty Confidence interval: 74.7–75.9 contaminationWarning Not reported rank 3 reportedConfidenceHalfWidth 0.6 ### Source [Original source](https://labs.scale.com/leaderboard/multichallenge) Source scale Original model label Muse Spark 1.1 Observed 2026-10-21T16:35:37.135Z Evidence SHA-256 `92e84e933979bccb14193567e45702ffc95189cc48216f6af220e7e235bfa3a0` Source locator `/rows/2` | | | | | | 3 | [Gemini 3.1 Pro Preview](https://benchize.com/models/gemini-3-1-pro-preview.md) | 71.37source score | CI 69.63–73.11 | | | ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 71.37 \| source_score \| reported \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration gemini-3.1-pro-preview sourceModelVersion Not reported thinkingEnabled Not reported ### Reported statistics Uncertainty Confidence interval: 69.63–73.11 contaminationWarning Not reported rank 4 reportedConfidenceHalfWidth 1.7366 ### Source [Original source](https://labs.scale.com/leaderboard/multichallenge) Source scale Original model label gemini-3.1-pro-preview Observed 2026-10-21T16:35:37.135Z Evidence SHA-256 `92e84e933979bccb14193567e45702ffc95189cc48216f6af220e7e235bfa3a0` Source locator `/rows/3` | | | | | | 4 | [Claude Haiku 5.5](https://benchize.com/models/claude-haiku-5-5.md) | 67.67source score | CI 62.33–73.01 | | | ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 67.67 \| source_score \| reported \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration Claude Haiku 5.5 sourceModelVersion claude-haiku-5-5 thinkingEnabled Not reported ### Reported statistics Uncertainty Confidence interval: 62.33–73.01 contaminationWarning Not reported rank 6 reportedConfidenceHalfWidth 5.34 ### Source [Original source](https://labs.scale.com/leaderboard/multichallenge) Source scale Original model label Claude Haiku 5.5 Observed 2026-10-21T16:35:37.135Z Evidence SHA-256 `92e84e933979bccb14193567e45702ffc95189cc48216f6af220e7e235bfa3a0` Source locator `/rows/5` | | | | | | 5 | [Gemini 3.1 Flash Lite Preview](https://benchize.com/models/gemini-3-1-flash-lite-preview.md) | 60.61source score | CI 60.23–60.99 | | | ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 60.61 \| source_score \| reported \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration gemini-3.1-flash-lite-preview sourceModelVersion Not reported thinkingEnabled Not reported ### Reported statistics Uncertainty Confidence interval: 60.23–60.99 contaminationWarning Not reported rank 12 reportedConfidenceHalfWidth 0.3795 ### Source [Original source](https://labs.scale.com/leaderboard/multichallenge) Source scale Original model label gemini-3.1-flash-lite-preview Observed 2026-10-21T16:35:37.135Z Evidence SHA-256 `92e84e933979bccb14193567e45702ffc95189cc48216f6af220e7e235bfa3a0` Source locator `/rows/11` | | | | | | 6 | [Claude Opus 4.6](https://benchize.com/models/claude-opus-4-6-off.md)Non-thinking | 56.02source score | CI 54.94–57.1 | | | ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 56.02 \| source_score \| reported \| Higher is better \| ### Configuration Reasoning effort non-thinking Model configuration claude-opus-4-6 (Non-Thinking) sourceModelVersion Not reported thinkingEnabled false ### Reported statistics Uncertainty Confidence interval: 54.94–57.1 contaminationWarning Not reported rank 19 reportedConfidenceHalfWidth 1.08 ### Source [Original source](https://labs.scale.com/leaderboard/multichallenge) Source scale Original model label claude-opus-4-6 (Non-Thinking) Observed 2026-10-21T16:35:37.135Z Evidence SHA-256 `92e84e933979bccb14193567e45702ffc95189cc48216f6af220e7e235bfa3a0` Source locator `/rows/18` | | | | | | 7 | [Gemini 2.5 Pro](https://benchize.com/models/gemini-2-5-pro.md) | 53.62source score | CI 52.3–54.94 | | | ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 53.62 \| source_score \| reported \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration gemini-2-5-pro sourceModelVersion Not reported thinkingEnabled Not reported ### Reported statistics Uncertainty Confidence interval: 52.3–54.94 contaminationWarning Not reported rank 22 reportedConfidenceHalfWidth 1.3178 ### Source [Original source](https://labs.scale.com/leaderboard/multichallenge) Source scale Original model label gemini-2-5-pro Observed 2026-10-21T16:35:37.135Z Evidence SHA-256 `92e84e933979bccb14193567e45702ffc95189cc48216f6af220e7e235bfa3a0` Source locator `/rows/21` | | | | | | 8 | [gpt-oss-120b](https://benchize.com/models/gpt-oss-120b.md) | 45.34source score | CI 43.19–47.49 | | | ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 45.34 \| source_score \| reported \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration gpt-oss-120b sourceModelVersion Not reported thinkingEnabled Not reported ### Reported statistics Uncertainty Confidence interval: 43.19–47.49 contaminationWarning Not reported rank 27 reportedConfidenceHalfWidth 2.1519 ### Source [Original source](https://labs.scale.com/leaderboard/multichallenge) Source scale Original model label gpt-oss-120b Observed 2026-10-21T16:35:37.135Z Evidence SHA-256 `92e84e933979bccb14193567e45702ffc95189cc48216f6af220e7e235bfa3a0` Source locator `/rows/26` | | | | | | 9 | [Qwen3 235B A22B](https://benchize.com/models/qwen3-235b-a22b.md) | 41.22source score | CI 39.43–43.01 | | | ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 41.22 \| source_score \| reported \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration qwen3-235b-a22b sourceModelVersion Not reported thinkingEnabled Not reported ### Reported statistics Uncertainty Confidence interval: 39.43–43.01 contaminationWarning Not reported rank 29 reportedConfidenceHalfWidth 1.7903 ### Source [Original source](https://labs.scale.com/leaderboard/multichallenge) Source scale Original model label qwen3-235b-a22b Observed 2026-10-21T16:35:37.135Z Evidence SHA-256 `92e84e933979bccb14193567e45702ffc95189cc48216f6af220e7e235bfa3a0` Source locator `/rows/28` | | | | | | 10 | [GPT-4.1](https://benchize.com/models/gpt-4-1.md) | 39.43source score | CI 38.49–40.37 | | | ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 39.43 \| source_score \| reported \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration gpt-4.1 sourceModelVersion Not reported thinkingEnabled Not reported ### Reported statistics Uncertainty Confidence interval: 38.49–40.37 contaminationWarning Not reported rank 30 reportedConfidenceHalfWidth 0.9393 ### Source [Original source](https://labs.scale.com/leaderboard/multichallenge) Source scale Original model label gpt-4.1 Observed 2026-10-21T16:35:37.135Z Evidence SHA-256 `92e84e933979bccb14193567e45702ffc95189cc48216f6af220e7e235bfa3a0` Source locator `/rows/29` | | | | | | 11 | [Gemini 2.0 Flash](https://benchize.com/models/gemini-2-0-flash.md) | 36.35source score | CI 35.57–37.13 | | | ### Original source values \| Metric \| Value \| Unit \| Scope \| Direction \| \| --- \| --- \| --- \| --- \| --- \| \| `score` \| 36.35 \| source_score \| reported \| Higher is better \| ### Configuration Reasoning effort unknown Model configuration gemini-2-0-flash sourceModelVersion Not reported thinkingEnabled Not reported ### Reported statistics Uncertainty Confidence interval: 35.57–37.13 contaminationWarning Not reported rank 32 reportedConfidenceHalfWidth 0.7769 ### Source [Original source](https://labs.scale.com/leaderboard/multichallenge) Source scale Original model label gemini-2-0-flash Observed 2026-10-21T16:35:37.135Z Evidence SHA-256 `92e84e933979bccb14193567e45702ffc95189cc48216f6af220e7e235bfa3a0` Source locator `/rows/31` | | | | | --- Cite as: MultiChallenge — Benchize, https://benchize.com/benchmarks/scale%3Amultichallenge (retrieved 2026-10-21)