GPT-5.4 Mini vs o4 Mini

GPT-5.4 Mini undercuts o4 Mini on input costs by 32%, and that difference compounds fast. At 1M tokens, the $2 savings is negligible—just noise in your billing. But by 10M tokens, GPT-5.4 Mini shaves off $2,000 in input costs alone, enough to cover a mid-tier GPU instance for a month. The output pricing is nearly identical ($4.50 vs $4.40), so the savings hinge entirely on how much you feed the model. If your workload is prompt-heavy—think RAG pipelines with 10K-token context windows—GPT-5.4 Mini’s input discount becomes a real advantage.

That said, o4 Mini’s 3% higher MT-Bench score (8.3 vs 8.1) might justify the premium for tasks where accuracy trumps cost. But let’s be clear: that’s a marginal gain. If you’re processing millions of tokens for batch inference or log analysis, GPT-5.4 Mini’s pricing wins decisively. Only in high-stakes, low-volume scenarios (e.g., legal document summarization) does o4 Mini’s slight edge warrant the extra spend. For everyone else, the math is simple: GPT-5.4 Mini delivers 98% of the performance at 70% of the input cost. Deploy it, monitor your token usage, and pocket the difference.

GPT-5.4 Mini delivers where it counts, but its performance is uneven across benchmarks. In coding tasks, it scores a 2.7/3 on HumanEval—better than many larger models at twice the price—while o4 Mini remains untested here. That’s a standout advantage for teams needing reliable code generation without paying for a full-scale model. On reasoning, GPT-5.4 Mini hits 2.4/3 on MMLU, which is respectable for a "mini" variant but not groundbreaking. The surprise is its 2.6/3 in instruction following, where it outperforms some mid-tier models like Claude 3 Haiku. If you’re prioritizing structured outputs or multi-step tasks, this is the clear winner for now.

Where GPT-5.4 Mini stumbles is in creative writing and nuanced language tasks. Its 2.2/3 on TruthfulQA suggests it still struggles with subtle factual accuracy, and while we lack direct comparisons to o4 Mini, early user reports hint that o4’s smaller size might make it more agile for lightweight text generation. That said, without head-to-head benchmarks, this is speculative. The real gap is in latency: GPT-5.4 Mini averages 1.2s response times, while o4 Mini’s architecture suggests sub-500ms is possible. If speed is critical, wait for o4’s full benchmarks before committing.

The biggest unknown is o4 Mini’s untracked performance in math and logic. GPT-5.4 Mini’s 2.3/3 on GSM8K is decent but not exceptional, and if o4 Mini can match or exceed that while maintaining lower latency, it could be the better value. For now, GPT-5.4 Mini is the safer bet for teams needing a balance of coding and reasoning—but if o4 Mini’s upcoming benchmarks show even modest gains in speed or accuracy, the calculus changes. Watch this space.