reasoning 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 7, 2026
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | OP | 91.5% | 100.0% | 21 | C | |
| 2 | OP | 89.6% | 95.0% | 21 | C | |
| 3 | AN | 76.0% | 90.0% | 21 | C | |
| 4 | OP | 74.0% | 85.0% | 21 | C | |
| 5 | GO | 66.4% | 80.0% | 21 | C | |
| 6 | GO | 60.1% | 75.0% | 21 | C | |
| 7 | GO | 54.0% | 70.0% | 21 | C | |
| 8 | GO | 44.1% | 65.0% | 21 | C | |
| 9 | 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 |
Top published rows on the benchmark's original scale.
The top published results on this benchmark's own scale.
Definition and scoring fields from the benchmark registry.
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. The current registry marks this benchmark as not independently verified with evidence level C.
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
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 unique published model results are currently shown.
This benchmark is marked as eligible for the current LLMBoard capability methodology.