Flint vs GPT-6 Luna

Overall
21
vs
Overall
61

GPT-6 Luna is clearly better. It wins 70 of the 71 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. Flint spends 7.2× as many thinking tokens per test case.

Thinking tokensAnswer tokens
TaskAB
Overall
211m 17s
10.9K, 90% thinking1.9K, 73% thinking
6149.1s
Agents
1447.7s
4.8K, 85% thinking1.3K, 67% thinking
561m 18s
Structured Extraction
291m 17s
10.9K, 87% thinking999, 72% thinking
9514.5s
Coding
141m 19s
12K, 98% thinking3.6K, 60% thinking
732m 18s
Grounded Answers
2957.7s
8.7K, 93% thinking1.1K, 72% thinking
6715.6s
Instruction Following
352m 28s
22.2K, 93% thinking2K, 74% thinking
8432.3s
Business Logic
101m 20s
11.7K, 78% thinking3.2K, 93% thinking
3743.1s
Security & permissions
3552.6s
7.1K, 90% thinking1K, 68% thinking
6224.5s
Diagnosis & investigation
057.0s
7.3K, 99% thinking1.1K, 68% thinking
1737.7s

What you'd run

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

SpecAB
Speed
149 tok/s (better)
90 tok/s
Machine
Hosted
Hosted
Memory at 32K
—
—
Context
256K
1.1M (better)
Cost per run
Not measured
$0.10
Cost per case
Not measured
$0.00067
Time per case
1m 17s
49.1s (better)
Quantization
BF16
—
Harness
openrouter
openrouter

The race

No lead changes

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

FlintGPT-6 Luna
0255075100501001502161

Every test case

153 test cases

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

1 won by Flint70 won by GPT-6 Luna82 tied