Phi 4 AI just swaggered into the playground with 14 billion parameters—then proceeded to outshine much bigger rivals like Llama 3.3 (70B) and Qwen 2.5 (72B) across brain-bending tasks: logic, math, coding, you name it. It posts a 0.714 on MMLU, leaves GPT-4o blushing in advanced STEM, and still charges less than your daily latte. Sure, it sometimes fumbles reading tests, but hey, even Iron Man has software updates. Want specifics? The next part spills the tea.
Even in an era obsessed with bigger, flashier AI models (looking at you, 70-billion-parameter club), Phi 4 is proof that sometimes, less really is more—at least when you know what you’re doing. While titans like Llama 3.3 70B and Qwen 2.5 (72B) flex their computational muscles, Phi 4—rocking just 14 billion parameters—quietly walks in, wipes the floor on key benchmarks, and leaves the heavyweights scratching their virtual heads.
Let’s break it down. Phi 4 clocks a 0.714 score on MMLU and a 40 on the Intelligence Index, matching or outright beating Llama 3.3 70B on six out of thirteen gold-standard tests. *Cue applause.* Particularly, it rules the math league: 91.8/150 on AMC math problems, which is not only higher than Gemini Pro 1.5, but also embarrasses many “smarter” models on MATH and GPQA graduate-level STEM questions. Even GPT-4o takes a back seat when it comes to GPQA. The Phi 4 reasoning model is designed to excel at complex reasoning and fact-checking, making it a standout choice for tasks in math, science, and coding. As a bonus for developers and businesses, Phi-4 is open source, offering a flexible MIT license for unrestricted commercial use.
Phi 4 outsmarts giants, crushing math benchmarks and leaving even GPT-4o trailing in advanced STEM performance—brains over brawn, every time.
But, of course, it’s not all sunshine and math trophies. Phi 4 lags in reading comprehension (DROP) and fact retrieval (SimpleQA). Instruction-following? Meh. Sometimes it misses the memo entirely (IFEval). But hey, nobody’s perfect—especially not at one-fifth the size.
What really turns heads is the efficiency. Phi 4 runs a lean Transformer setup with a 16k token context window, and with sharp data curation plus advanced post-training tweaks, it delivers heavyweight results on a featherweight budget. The output speed is a respectable 40.9 tokens/second (not light speed, but not turtle pace), with a snappy 0.44s time-to-first-token. That means less waiting, more doing. Like supervised learning systems, Phi 4 demonstrates impressive predictive capabilities when working with labeled data inputs.
Pricing? It’s almost suspiciously reasonable:
- Input: $0.13 per million tokens
- Output: $0.50 per million tokens
- Overall: $0.22 per million (3:1 blend)
In a world obsessed with brute force and bloat, Phi 4’s true power is a reminder: data quality, not sheer size, wins the day. Maybe it’s time the AI world stopped chasing “bigger” and started thinking “smarter.”
