reasoning benchmark
OpenAI-MRCR: 2 needle 256k
Multi-Round Co-reference Resolution (MRCR) benchmark that tests long-context reasoning by evaluating a model's ability to distinguish between similar outputs, reason about ordering, and reproduce specific content from multi-turn conversations containing multiple writing requests on overlapping topics at 256k tokens.
Updated Aug 7, 2026
Published models1
Registry coverage1
MetricScore
EvidenceB
OpenAI-MRCR: 2 needle 256k leaderboard
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | OP | 86.8% | 100.0% | 1 | C |
OpenAI-MRCR: 2 needle 256k highlights
The top published results on this benchmark's own scale.
What is OpenAI-MRCR: 2 needle 256k?
Definition and scoring fields from the benchmark registry.
Multi-Round Co-reference Resolution (MRCR) benchmark that tests long-context reasoning by evaluating a model's ability to distinguish between similar outputs, reason about ordering, and reproduce specific content from multi-turn conversations containing multiple writing requests on overlapping topics at 256k tokens.
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
- Family
- OpenAI-MRCR: 2 needle 256k
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- openai-mrcr:-2-needle-256k|llm-stats-current
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
FAQ
Common questions about OpenAI-MRCR: 2 needle 256k.
Which model scores highest on OpenAI-MRCR: 2 needle 256k?
GPT-5 is currently ranked first with 86.8%.
What does OpenAI-MRCR: 2 needle 256k measure?
Multi-Round Co-reference Resolution (MRCR) benchmark that tests long-context reasoning by evaluating a model's ability to distinguish between similar outputs, reason about ordering, and reproduce specific content from multi-turn conversations containing multiple writing requests on overlapping topics at 256k tokens.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 unique published model results are currently shown.
Does this benchmark affect the overall score?
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.