long context benchmark
MRCR v2 (8-needle) is a variant of the Multi-Round Coreference Resolution benchmark that includes 8 needle items to retrieve from long contexts. This tests models' ability to simultaneously track and reason about multiple pieces of information across extended conversations.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | OP | 91.5% | 100.0% | 21 | C | |
| 02 | OP | 89.6% | 95.0% | 21 | C | |
| 03 | AN | 76.0% | 90.0% | 21 | C | |
| 04 | OP | 74.0% | 85.0% | 21 | C | |
| 05 | GO | 66.4% | 80.0% | 21 | C | |
| 06 | GO | 60.1% | 75.0% | 21 | C | |
| 07 | GO | 54.0% | 70.0% | 21 | C | |
| 08 | GO | 44.1% | 65.0% | 21 | C | |
| 09 | GO | 43.4% | 60.0% | 21 | C | |
| 10 | OP | 41.3% | 55.0% | 21 | C | |
| 11 | OP | 33.6% | 50.0% | 21 | C | |
| 12 | OP | 33.1% | 45.0% | 21 | C | |
| 13 | GO | 26.6% | 40.0% | 21 | C | |
| 14 | GO | 26.3% | 35.0% | 21 | C | |
| 15 | GO | 26.3% | 30.0% | 21 | C | |
| 16 | GO | 25.4% | 25.0% | 21 | C | |
| 17 | GO | 22.1% | 20.0% | 21 | C | |
| 18 | GO | 21.3% | 15.0% | 21 | C | |
| 19 | GO | 19.1% | 10.0% | 21 | C | |
| 20 | GO | 16.4% | 5.0% | 21 | C | |
| 21 | GO | 13.5% | 0.0% | 21 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What MRCR v2 (8-needle) measures and how its scores work.
MRCR v2 (8-needle) is a variant of the Multi-Round Coreference Resolution benchmark that includes 8 needle items to retrieve from long contexts. This tests models' ability to simultaneously track and reason about multiple pieces of information across extended conversations.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of C.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about MRCR v2 (8-needle).
GPT-5.6 Sol is currently ranked first with 91.5%.
MRCR v2 (8-needle) is a variant of the Multi-Round Coreference Resolution benchmark that includes 8 needle items to retrieve from long contexts. This tests models' ability to simultaneously track and reason about multiple pieces of information across extended conversations.
Yes. Higher values rank better for this benchmark.
21 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.