GPT-6 Luna vs Mellum2.1 Thinking

Overall
61
vs
Overall
9

GPT-6 Luna is clearly better. It wins 93 of the 94 test cases where they differ.

Learn to build evals like this in the AI Engineering Academy

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. Mellum2.1 Thinking spends 4.7× as many thinking tokens per test case.

Thinking tokensAnswer tokens
TaskAB
Overall
6149.1s
1.9K, 73% thinking10.3K, 63% thinking
91m 56s
Agents
561m 18s
1.3K, 67% thinking10.8K, 93% thinking
92m 3s
Structured Extraction
9514.5s
999, 72% thinking8.3K, 51% thinking
51m 36s
Coding
732m 18s
3.6K, 60% thinking11.3K, 38% thinking
52m 11s
Grounded Answers
6715.6s
1.1K, 72% thinking7.3K, 62% thinking
131m 24s
Instruction Following
8432.3s
2K, 74% thinking17.3K, 63% thinking
283m 10s
Business Logic
3743.1s
3.2K, 93% thinking9K, 50% thinking
01m 39s
Security & permissions
6224.5s
1K, 68% thinking9.1K, 75% thinking
81m 40s
Diagnosis & investigation
1737.7s
1.1K, 68% thinking6.8K, 98% thinking
01m 15s

What you'd run

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

SpecAB
Speed
90 tok/s
90 tok/s
Machine
Hosted
Apple M5 Pro, 48 GB
Memory at 32K
—
12.9 GB
Context
1.1M (better)
128K
Cost per run
$0.10 (better)
$2.46 est.
Cost per case
$0.00067 (better)
$0.016 est.
Time per case
49.1s (better)
1m 56s
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 LunaMellum2.1 Thinking
025507510050100150Perfect run619

Every test case

153 test cases

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

93 won by GPT-6 Luna1 won by Mellum2.1 Thinking59 tied