modelgrep

Best LLMs for Long-Context Reasoning — inclusionAI

Match · Updated August 2026

The best inclusionAI model for reasoning over long inputs is Ring-2.6-1T, scoring 64.3% on long-context reasoning — a different question from how large a window it accepts. Ling-2.6-1T (34.7%) and Ling-2.6-flash (25.0%) round out the top three.

64.3%LCR
30.6Intelligence
60 t/sSpeed
$0.075Input /M
262KContext
  1. 1I
    ring-2.6-1t
    ReasoningToolsJSON30.6 intel · $0.075/M · 60 t/s
    64.3%
    LCR
  2. 2I
    ling-2.6-1t
    ToolsJSON26.1 intel · $0.075/M · 36 t/s
    34.7%
    LCR
  3. 3I
    ling-2.6-flash
    ToolsJSON14.1 intel · $0.010/M · 62 t/s
    25.0%
    LCR

How this is ranked

AI models ranked by LCR — reasoning accuracy over long inputs, not just the size of the window they accept. A large context window is a capacity claim; this is the measurement of whether the model can still reason over information buried deep inside it. Pair it with the longest-context ranking, which sorts on raw window size.

Frequently asked

Which inclusionAI model reasons best over long inputs?

The best inclusionAI model for reasoning over long inputs is Ring-2.6-1T, scoring 64.3% on long-context reasoning — a different question from how large a window it accepts. Ling-2.6-1T (34.7%) and Ling-2.6-flash (25.0%) round out the top three.

What's a good alternative to Ring-2.6-1T?

Ling-2.6-1T (34.7%) is the closest alternative on this metric, followed by Ling-2.6-flash (25.0%). See the full ranking above for the tradeoffs.

How many inclusionAI models are there?

modelgrep tracks 4 inclusionAI models with live benchmarks, speed, latency and per-provider pricing, led on intelligence by Ring-2.6-1T. 3 of them qualify for this ranking.

More inclusionAI rankings

All rankings