Claude Haiku 5.5 vs LFM3 1.7B

Overall
70
vs
Overall
6

Claude Haiku 5.5 is clearly better. It wins 110 of the 110 test cases where they differ.

Learn to build evals like this in the AI Engineering Academy

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.

Thinking tokensAnswer tokens
TaskAB
Overall
7025.5s
3.9K, 78% thinking13.5K, 66% thinking
62m 11s
Agents
6139.5s
3.7K, 61% thinking18.4K, 94% thinking
132m 40s
Structured Extraction
9013.5s
2.6K, 83% thinking10.1K, 41% thinking
01m 25s
Coding
8630.6s
6.8K, 76% thinking9.5K, 45% thinking
51m 21s
Grounded Answers
7613.9s
2.2K, 78% thinking9.8K, 65% thinking
01m 54s
Instruction Following
9431.5s
4.3K, 82% thinking23.1K, 68% thinking
144m 39s
Business Logic
6726.0s
5.5K, 95% thinking11.5K, 37% thinking
01m 37s
Security & permissions
6723.4s
2.7K, 70% thinking13K, 78% thinking
161m 48s
Diagnosis & investigation
1725.5s
2.3K, 50% thinking11.6K, 99% thinking
01m 36s

What you'd run

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

SpecAB
Speed
225 tok/s
104 tok/s
Machine
Hosted
Apple M5 Pro, 48 GB
Memory at 32K
—
2.8 GB
Context
1M (better)
256K
Cost per run
$0.42
Not measured
Cost per case
$0.0028
Not measured
Time per case
25.5s (better)
2m 11s
Quantization
—
Q8_0
Harness
openrouter
llama.cpp

The race

No lead changes

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

Claude Haiku 5.5LFM3 1.7B
025507510050100150706

Every test case

153 test cases

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

110 won by Claude Haiku 5.50 won by LFM3 1.7B43 tied