Compare/LFM2 24B A2B vs Qwen3.5 2B (Reasoning)

LFM2 24B A2BvsQwen3.5 2B (Reasoning)

Side-by-side comparison of pricing, 12 benchmarks, and generation speed.

Liquid AI

LFM2 24B A2B

Input
$0.03/M
Output
$0.12/M
Speed
203 tok/s
TTFT
0.22s
Alibaba

Qwen3.5 2B (Reasoning)

Input
$0.02/M
Output
$0.1/M
Speed
357 tok/s
TTFT
0.54s

Winner by Category

Cheaper
Qwen3.5 2B (Reasoning)
Faster (tok/s)
Qwen3.5 2B (Reasoning)
Lower Latency
LFM2 24B A2B
Benchmarks (5-2)
LFM2 24B A2B

Pricing Comparison

MetricLFM2 24B A2BQwen3.5 2B (Reasoning)
Input ($/M tokens)$0.03$0.02
Output ($/M tokens)$0.12$0.1
Cost for 1M input + 100K output tokens:
LFM2 24B A2B$0.04
Qwen3.5 2B (Reasoning)$0.03

Speed Comparison

Output Speed (tokens/s) — higher is better
LFM2 24B A2B
203 tok/s
Qwen3.5 2B (Reasoning)
357 tok/s
Time to First Token (seconds) — lower is better
LFM2 24B A2B
0.22s
Qwen3.5 2B (Reasoning)
0.54s

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
10.516.3
Coding Index
3.63.5
Math Index
GPQA Diamond
47.4%45.6%
MMLU-Pro
LiveCodeBench
AIME 2025
MATH-500
Humanity's Last Exam
4.4%2.1%
SciCode
10.9%2.8%
IFBench
45.9%31.5%
TerminalBench
0.0%3.8%
LFM2 24B A2B5 wins
2 winsQwen3.5 2B (Reasoning)

Frequently Asked Questions

Which is cheaper, LFM2 24B A2B or Qwen3.5 2B (Reasoning)?

Qwen3.5 2B (Reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.04/M tokens vs $0.05/M for LFM2 24B A2B.

Which model performs better on benchmarks?

LFM2 24B A2B wins 5 out of 12 benchmarks compared to 2 for Qwen3.5 2B (Reasoning). See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

Qwen3.5 2B (Reasoning) generates tokens faster at 357 tok/s vs 203 tok/s. LFM2 24B A2B also has lower time-to-first-token (0.22s vs 0.54s).

When should I use LFM2 24B A2B vs Qwen3.5 2B (Reasoning)?

Choose based on your priorities: Qwen3.5 2B (Reasoning) for lower cost, LFM2 24B A2B for stronger benchmark performance, and Qwen3.5 2B (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.