LFM2.5 24B A2B vs Underdog Saluki 27B
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 1.5× as many thinking tokens per test case.
TaskALFM2.5 24B A2BBUnderdog Saluki 27B
Overall
71m 53s
335m 15s
Agents
123m 49s
409m 2s
Structured Extraction
1054.9s
433m 17s
Coding
11m 15s
166m 54s
Grounded Answers
557.8s
433m 22s
Instruction Following
162m 44s
466m 5s
Business Logic
51m 2s
333m 55s
Security & permissions
101m 52s
433m 47s
Diagnosis & investigation
04m 12s
06m 39s
What you'd run
The build behind each score. Orange marks the faster, smaller, cheaper or longer-context side.
SpecALFM2.5 24B A2BBUnderdog Saluki 27B
Speed
82 tok/s (better)
23 tok/s
Machine
Apple M5 Pro, 48 GB
Apple M5 Pro, 48 GB
Memory at 32K
25.0 GB
10.2 GB (better)
Context
128K
256K (better)
Cost per run
Not measured
Not measured
Cost per case
Not measured
Not measured
Time per case
1m 53s (better)
5m 15s
Quantization
Q8_0
IQ2_XS
Harness
llama.cpp
llama.cpp
The race
Each model's running score as the test cases go by. The dashed line is a perfect run.
Every test case
Each square is one test case, colored by the model that scored higher.
Agents
Structured Extraction
Coding
Grounded Answers
Instruction Following
Business Logic
Security & permissions
Diagnosis & investigation