long context benchmark
OpenAI-MRCR: 2 needle 128k
Multi-round Co-reference Resolution (MRCR) benchmark for evaluating an LLM's ability to distinguish between multiple needles hidden in long context. Models are given a long, multi-turn synthetic conversation and must retrieve a specific instance of a repeated request, requiring reasoning and disambiguation skills beyond simple retrieval.
Updated Aug 11, 2026
Models9
Model coverage9
MetricScore
EvidenceB
OpenAI-MRCR: 2 needle 128k Ranking
Higher score ranks better on this benchmark.
| 01 | OP | 95.2% | 100.0% | 9 | C | |
| 02 | MI | 76.1% | 87.5% | 9 | C | |
| 03 | MI | 73.4% | 75.0% | 9 | C | |
| 04 | OP | 57.2% | 62.5% | 9 | C | |
| 05 | OP | 47.2% | 50.0% | 9 | C | |
| 06 | OP | 38.5% | 37.5% | 9 | C | |
| 07 | OP | 36.6% | 25.0% | 9 | C | |
| 08 | OP | 31.9% | 12.5% | 9 | C | |
| 09 | OP | 18.7% | 0.0% | 9 | C |
OpenAI-MRCR: 2 needle 128k Score Distribution
A closer view of the leading scores on this benchmark.
OpenAI-MRCR: 2 needle 128k Highlights
The leading models and scores on this benchmark.
What is OpenAI-MRCR: 2 needle 128k?
What OpenAI-MRCR: 2 needle 128k measures and how its scores work.
Multi-round Co-reference Resolution (MRCR) benchmark for evaluating an LLM's ability to distinguish between multiple needles hidden in long context. Models are given a long, multi-turn synthetic conversation and must retrieve a specific instance of a repeated request, requiring reasoning and disambiguation skills beyond simple retrieval.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- OpenAI-MRCR: 2 needle 128k
- Modality
- text
- Primary category
- long context
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- openai-mrcr:-2-needle-128k|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about OpenAI-MRCR: 2 needle 128k.
Which model scores highest on OpenAI-MRCR: 2 needle 128k?
GPT-5 is currently ranked first with 95.2%.
What does OpenAI-MRCR: 2 needle 128k measure?
Multi-round Co-reference Resolution (MRCR) benchmark for evaluating an LLM's ability to distinguish between multiple needles hidden in long context. Models are given a long, multi-turn synthetic conversation and must retrieve a specific instance of a repeated request, requiring reasoning and disambiguation skills beyond simple retrieval.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
9 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.