GPT-6 Luna vs LFM2.5 24B A2B

Overall
61
vs
Overall
7

GPT-6 Luna is clearly better. It wins 96 of the 97 test cases where they differ.

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Task by task

A score turns orange only when that model wins clearly more of the task's test cases. Under each score, the average time per test case on the machine it ran on. LFM2.5 24B A2B spends 6.3× as many thinking tokens per test case.

Thinking tokensAnswer tokens
TaskAB
Overall
6149.1s
1.9K, 73% thinking9.2K, 93% thinking
71m 53s
Agents
561m 18s
1.3K, 67% thinking18.5K, 98% thinking
123m 49s
Structured Extraction
9514.5s
999, 72% thinking4.4K, 94% thinking
1054.9s
Coding
732m 18s
3.6K, 60% thinking6.1K, 68% thinking
11m 15s
Grounded Answers
6715.6s
1.1K, 72% thinking4.7K, 96% thinking
557.8s
Instruction Following
8432.3s
2K, 74% thinking13.4K, 97% thinking
162m 44s
Business Logic
3743.1s
3.2K, 93% thinking5K, 83% thinking
51m 2s
Security & permissions
6224.5s
1K, 68% thinking9K, 97% thinking
101m 52s
Diagnosis & investigation
1737.7s
1.1K, 68% thinking19.9K, 98% thinking
04m 12s

What you'd run

The build behind each score. Orange marks the faster, smaller, cheaper or longer-context side.

SpecAB
Speed
90 tok/s
82 tok/s
Machine
Hosted
Apple M5 Pro, 48 GB
Memory at 32K
—
25.0 GB
Context
1.1M (better)
128K
Cost per run
$0.10
Not measured
Cost per case
$0.00067
Not measured
Time per case
49.1s (better)
1m 53s
Quantization
—
Q8_0
Harness
openrouter
llama.cpp

The race

No lead changes

Each model's running score as the test cases go by. The dashed line is a perfect run.

GPT-6 LunaLFM2.5 24B A2B
025507510050100150Perfect run617

Every test case

153 test cases

Each square is one test case, colored by the model that scored higher.

96 won by GPT-6 Luna1 won by LFM2.5 24B A2B56 tied