LongBench-Chat
Qwen2.5-14B-Instruct-1M 8.76 leads since Jan 2025 Dropped Models follow real user instructions over contexts of 10K to 100K tokens. GPT-4 scores each answer from 1 to 10 against references.
Introduced with the LongAlign paper.
- Level
- Everyday Most adults, without training
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Open-ended Free-form text, documents, designs or media judged as a whole.
Qwen2.5-14B-Instruct-1M leads with 8.76 among models released by Jan 2025
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.
The newest model with a score here came out in Jan 2025, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- GPT-6 Astra
- Claude Opus 5.5
- GPT-6.1 Terra
- GPT-6.1 Sol
- and 16 more
- 1 Qwen2.5-14B-Instruct-1M 8.76
- 2 Qwen2.5-72B 8.72
- 3 Qwen2.5-32B 8.70
- 4 Qwen-Turbo 8.34
- 5 Qwen2.5-14B 8.04
- 6 Qwen2.5-7B 7.42
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Qwen2.5-14B-Instruct-1M | 8.76 | Jan 2025 | Model card | |
| 2 | Qwen2.5-72B | 8.72 | Dec 2024 | Model card | |
| 3 | Qwen2.5-32B | 8.70 | Dec 2024 | Model card | |
| 4 | Qwen-Turbo | 8.34 | Dec 2024 | Model card | |
| 5 | Qwen2.5-14B | 8.04 | Dec 2024 | Model card | |
| 6 | Qwen2.5-7B | 7.42 | Dec 2024 | Model card |
2 models have held number one since its release in Jan 2024
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
No launch post has listed it since Jan 2025
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 Jan 2025 | Qwen2.5-14B-Instruct-1M | 8.76 | LongBench-Chat |
6 system cards report it, from 1 lab
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 |
|---|---|---|---|---|
| 26 Jan 2025 | Qwen2.5-14B-Instruct-1M technical report | 8.76 | LongBench-Chat (Table 5, Table 3). 8.56 before RL | Capabilities |
| 19 Dec 2024 | Qwen2.5-7B technical report | 7.42 | LongBench-Chat (Table 17) | Capabilities |
| 19 Dec 2024 | Qwen2.5-72B technical report | 8.72 | LongBench-Chat (Table 17) | Capabilities |
| 19 Dec 2024 | Qwen2.5-32B technical report | 8.7 | LongBench-Chat (Table 17) | Capabilities |
| 19 Dec 2024 | Qwen2.5-14B technical report | 8.04 | LongBench-Chat (Table 17) | Capabilities |
| 19 Dec 2024 | Qwen-Turbo technical report | 8.34 | LongBench-Chat (Table 17) | Capabilities |
More in Long context
- MRCR v2 In use Multi-round coreference: retrieve the n-th of several similar requests hidden in a long synthetic conversation, up to 1M tokens.
Muse Spark 1.3 98.5% - LongBench v2 In use 503 multiple-choice questions over 8K-2M-word contexts (QA, dialogue, code repos, structured data). Accuracy. Beam 1.0 66.9%
- GraphWalks In use A graph is given as an edge list in long context. The model answers BFS or parent queries, scored by F1. Claude Fable 5 100%
- OpenAI MRCR v2 In use Models read a long synthetic chat that hides 2, 4 or 8 identical writing requests and must reproduce the response to the i-th one. Scored by mean string match ratio per context bin.
K-EXAONE 2.0 94.4% - CL-bench In use Model must learn new knowledge from a supplied long context (rules, procedures, data) and solve tasks. Strict LLM-verified rubrics. GPT-5.4 27.9%
- AA-LCR 1.1 In use Models answer 100 questions that need reasoning across sets of real documents of about 100K tokens. An LLM equality checker with a fixed system prompt compares each answer with the key.
Step 5 Preview 88.3%