Compare/LFM2 2.6B vs GPT-5.4 mini (Non-Reasoning)

LFM2 2.6BvsGPT-5.4 mini (Non-Reasoning)

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

Liquid AI

LFM2 2.6B

Input
$0/M
Output
$0/M
Speed
TTFT
OpenAI

GPT-5.4 mini (Non-Reasoning)

Input
$0.75/M
Output
$4.5/M
Speed
202 tok/s
TTFT
0.45s

Winner by Category

Cheaper
LFM2 2.6B
Faster (tok/s)
GPT-5.4 mini (Non-Reasoning)
Lower Latency
LFM2 2.6B
Benchmarks (4-7)
GPT-5.4 mini (Non-Reasoning)

Pricing Comparison

MetricLFM2 2.6BGPT-5.4 mini (Non-Reasoning)
Input ($/M tokens)$0$0.75
Output ($/M tokens)$0$4.5
Cost for 1M input + 100K output tokens:
LFM2 2.6B$0.00
GPT-5.4 mini (Non-Reasoning)$1.20

Speed Comparison

Output Speed (tokens/s) — higher is better
LFM2 2.6B
GPT-5.4 mini (Non-Reasoning)
202 tok/s
Time to First Token (seconds) — lower is better
LFM2 2.6B
GPT-5.4 mini (Non-Reasoning)
0.45s

Benchmark Comparison

Data from Artificial Analysis API — 12 benchmarks

Intelligence Index
8.023.3
Coding Index
1.425.3
Math Index
8.3
GPQA Diamond
30.6%60.6%
MMLU-Pro
29.8%
LiveCodeBench
8.1%
AIME 2025
8.3%
MATH-500
Humanity's Last Exam
5.2%5.7%
SciCode
2.5%39.6%
IFBench
19.5%38.8%
TerminalBench
0.8%18.2%
LFM2 2.6B4 wins
7 winsGPT-5.4 mini (Non-Reasoning)

Frequently Asked Questions

Which is cheaper, LFM2 2.6B or GPT-5.4 mini (Non-Reasoning)?

LFM2 2.6B is cheaper overall. Its blended price (3:1 input/output ratio) is $0.00/M tokens vs $1.69/M for GPT-5.4 mini (Non-Reasoning).

Which model performs better on benchmarks?

GPT-5.4 mini (Non-Reasoning) wins 7 out of 12 benchmarks compared to 4 for LFM2 2.6B. See the detailed benchmark chart above for per-category results.

Which is faster for real-time applications?

GPT-5.4 mini (Non-Reasoning) generates tokens faster at 202 tok/s vs 0 tok/s. LFM2 2.6B also has lower time-to-first-token (0.00s vs 0.45s).

When should I use LFM2 2.6B vs GPT-5.4 mini (Non-Reasoning)?

Choose based on your priorities: LFM2 2.6B for lower cost, GPT-5.4 mini (Non-Reasoning) for stronger benchmark performance, and GPT-5.4 mini (Non-Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.