LFM3 1.7B vs Claude Haiku 5.5
Task by task
A score turns orange only when that model wins clearly more of the task's test cases. The time under each score is the average per test case, on the machine it ran on. LFM3 1.7B spends 2.9× as many thinking tokens per test case.
TaskALFM3 1.7BBClaude Haiku 5.5
Overall
62m 11s
7025.5s
Agents
132m 40s
6139.5s
Structured Extraction
01m 25s
9013.5s
Coding
51m 21s
8630.6s
Grounded Answers
01m 54s
7613.9s
Instruction Following
144m 39s
9431.5s
Business Logic
01m 37s
6726.0s
Security & permissions
161m 48s
6723.4s
Diagnosis & investigation
01m 36s
1725.5s
What you'd run
The build behind each score. Orange marks the faster, smaller, cheaper or longer-context side.
SpecALFM3 1.7BBClaude Haiku 5.5
Speed
104 tok/s
225 tok/s
Machine
Apple M5 Pro, 48 GB
Hosted
Memory at 32K
2.8 GB
—
Context
256K
1M (better)
Cost per run
Not measured
$0.42
Cost per case
Not measured
$0.0028
Time per case
2m 11s
25.5s (better)
Quantization
Q8_0
—
Harness
llama.cpp
openrouter
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