MRCR Long Context (1M context window)

Can the model retain and reason over evidence spread across a very long input?

Version / measure

Version not specifiedExact reported variant

Long Context3 ranked models3 reported values1 reportsHigher is better

Top rankings

One row per model, using its best reported score across effort settings.

Rank	Model	Best score	Best reported setting
1	GPT-5.5OpenAI	74.0%	xhigh effortMeta report1 value · 1 reportJul 9, 2026 · source
2	Muse Spark 1.1Meta	54.1%	xhigh effortMeta report1 value · 1 reportJul 9, 2026 · source
3	Gemini 3.1 ProGoogle	26.3%	high effortMeta report1 value · 1 reportJul 9, 2026 · source

Every sourced cost-linked effort value for this exact version. Lines connect complete sweeps only.

No cost-linked effort sweep for this version.

Definition and comparison boundarypublic methodology

Can the model retain and reason over evidence spread across a very long input?

Long-context retrieval and reasoning, not context-window size alone. Higher is better. The value is the percentage reported in this lab's table.

Harness, tools, prompts, attempt count, and benchmark version can materially change the result. Compare within one reporting context.

Meta's general-capability figure; the report labels this as a 1M context window result.