LFM2.5 24B A2B vs Muse Spark 1.3 Contributor

Overall
7
vs
Overall
66

Muse Spark 1.3 Contributor is clearly better. It wins 103 of the 105 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
71m 53s
9.2K, 93% thinking8.7K, 91% thinking
661m 42s
Agents
123m 49s
18.5K, 98% thinking14.7K, 88% thinking
573m 16s
Structured Extraction
1054.9s
4.4K, 94% thinking4.2K, 91% thinking
9037.9s
Coding
11m 15s
6.1K, 68% thinking10.8K, 95% thinking
482m 1s
Grounded Answers
557.8s
4.7K, 96% thinking6.1K, 91% thinking
811m 1s
Instruction Following
162m 44s
13.4K, 97% thinking8.7K, 91% thinking
841m 18s
Business Logic
51m 2s
5K, 83% thinking6K, 95% thinking
761m 7s
Security & permissions
101m 52s
9K, 97% thinking8.2K, 87% thinking
761m 59s
Diagnosis & investigation
04m 12s
19.9K, 98% thinking17.2K, 90% thinking
173m 34s

What you'd run

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

SpecAB
Speed
82 tok/s
107 tok/s
Machine
Apple M5 Pro, 48 GB
Hosted
Memory at 32K
25.0 GB
—
Context
128K
1M (better)
Cost per run
Not measured
$0.41
Cost per case
Not measured
$0.0027
Time per case
1m 53s
1m 42s (better)
Quantization
Q8_0
—
Harness
llama.cpp
openrouter

The race

No lead changes

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

LFM2.5 24B A2BMuse Spark 1.3 Contributor
025507510050100150Perfect run766

Every test case

153 test cases

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

2 won by LFM2.5 24B A2B103 won by Muse Spark 1.3 Contributor48 tied