reasoning benchmark
OpenAI-MRCR: 2 needle 1M
Multi-Round Co-reference Resolution benchmark that tests an LLM's ability to distinguish between multiple similar needles hidden in long conversations. Models must reproduce specific instances of content (e.g., 'Return the 2nd poem about tapirs') from multi-turn synthetic conversations, requiring reasoning about context, ordering, and subtle differences between similar outputs.
Updated Aug 7, 2026
Published models5
Registry coverage5
MetricScore
EvidenceB
OpenAI-MRCR: 2 needle 1M leaderboard
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | MI | 58.6% | 100.0% | 5 | C | |
| 2 | MI | 56.2% | 75.0% | 5 | C | |
| 3 | OP | 46.3% | 50.0% | 5 | C | |
| 4 | OP | 33.3% | 25.0% | 5 | C | |
| 5 | OP | 12.0% | 0.0% | 5 | C |
Score distribution
Top published rows on the benchmark's original scale.
OpenAI-MRCR: 2 needle 1M highlights
The top published results on this benchmark's own scale.
What is OpenAI-MRCR: 2 needle 1M?
Definition and scoring fields from the benchmark registry.
Multi-Round Co-reference Resolution benchmark that tests an LLM's ability to distinguish between multiple similar needles hidden in long conversations. Models must reproduce specific instances of content (e.g., 'Return the 2nd poem about tapirs') from multi-turn synthetic conversations, requiring reasoning about context, ordering, and subtle differences between similar outputs.
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
- Family
- OpenAI-MRCR: 2 needle 1M
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- openai-mrcr:-2-needle-1m|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 1M.
Which model scores highest on OpenAI-MRCR: 2 needle 1M?
MiniMax M1 40K is currently ranked first with 58.6%.
What does OpenAI-MRCR: 2 needle 1M measure?
Multi-Round Co-reference Resolution benchmark that tests an LLM's ability to distinguish between multiple similar needles hidden in long conversations. Models must reproduce specific instances of content (e.g., 'Return the 2nd poem about tapirs') from multi-turn synthetic conversations, requiring reasoning about context, ordering, and subtle differences between similar outputs.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
5 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.