Flint vs LFM2.5 24B A2B

Overall
21
vs
Overall
7

Flint is clearly better. It wins 29 of the 34 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. Both models spend about the same on thinking per test case.

Thinking tokensAnswer tokens
TaskAB
Overall
211m 17s
10.9K, 90% thinking9.2K, 93% thinking
71m 53s
Agents
1447.7s
4.8K, 85% thinking18.5K, 98% thinking
123m 49s
Structured Extraction
291m 17s
10.9K, 87% thinking4.4K, 94% thinking
1054.9s
Coding
141m 19s
12K, 98% thinking6.1K, 68% thinking
11m 15s
Grounded Answers
2957.7s
8.7K, 93% thinking4.7K, 96% thinking
557.8s
Instruction Following
352m 28s
22.2K, 93% thinking13.4K, 97% thinking
162m 44s
Business Logic
101m 20s
11.7K, 78% thinking5K, 83% thinking
51m 2s
Security & permissions
3552.6s
7.1K, 90% thinking9K, 97% thinking
101m 52s
Diagnosis & investigation
057.0s
7.3K, 99% 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
149 tok/s
82 tok/s
Machine
Hosted
Apple M5 Pro, 48 GB
Memory at 32K
—
25.0 GB
Context
256K (better)
128K
Cost per run
Not measured
Not measured
Cost per case
Not measured
Not measured
Time per case
1m 17s (better)
1m 53s
Quantization
BF16
Q8_0
Harness
openrouter
llama.cpp

The race

2 lead changes

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

FlintLFM2.5 24B A2B
025507510050100150217

Every test case

153 test cases

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

29 won by Flint5 won by LFM2.5 24B A2B119 tied