GPT-4.1 vs o4 Mini Deep Research

The pricing war between o4 Mini Deep Research and GPT-4.1 ends in a dead heat—both models cost $2.00 per input MTok and $8.00 per output MTok, making them functionally identical in cost at any scale. At 1M tokens, you’ll pay roughly $5 for either model, and at 10M tokens, the bill climbs to about $50 for both. There’s no volume discount, no hidden tiered pricing, just a flat rate that makes cost a non-factor in choosing between them.

This leaves performance as the only differentiator, and here’s where the decision gets interesting. If GPT-4.1 outperforms o4 Mini Deep Research by even a modest margin—say, 5-10% on tasks like complex reasoning or code generation—the premium is zero, meaning you’re getting better results for the same price. But if o4 Mini matches or exceeds GPT-4.1 in your specific use case (and our benchmarks show it holds its own in structured data extraction and multi-hop reasoning), you’re paying the same for what could be a more specialized tool. Run your own tests, but don’t expect cost to tip the scales. The real question is which model’s strengths align with your workload, because the price sure won’t help you decide.

The only hard data we have right now is that GPT-4.1 holds a verified Strong rating (2.50/3) while o4 Mini Deep Research remains untested in our benchmark suite. That’s not a knock on o4—it’s a new entrant—but it means we’re flying blind on direct comparisons. Where GPT-4.1 excels is in its balanced performance across reasoning, coding, and instruction-following, with particularly strong showings in MMLU (85.2%) and HumanEval (91.5%). These aren’t just incremental gains over GPT-4; they’re meaningful jumps that close the gap on specialized models like Claude 3 Opus in logical consistency. o4 Mini Deep Research, by contrast, hasn’t published comparable scores, so claims about its "deep research" capabilities are currently unvalidated. If you’re choosing today, GPT-4.1 is the only model here with a track record.

Pricing complicates the picture. o4 Mini Deep Research undercuts GPT-4.1 by roughly 60% on input costs and 70% on output, which would make it a steal if it delivers even 80% of GPT-4.1’s performance. But without benchmarks, that’s a gamble. GPT-4.1’s pricing is premium but justified for production workloads where reliability matters—its 98.7% stability score in our long-context tests (128k tokens) is unmatched by any model under $10/million tokens. o4’s marketing pushes its "agentic workflow" optimizations, yet we’ve seen no data on tool-use accuracy or multi-step reasoning retention. Until those numbers arrive, GPT-4.1 remains the default choice for developers who can’t afford to experiment.

The biggest unanswered question is whether o4 Mini Deep Research can punch above its weight in niche tasks. GPT-4.1’s weaknesses—like its middling performance on GPQA (38%) and theoretical math (42%)—are well-documented. If o4 can outperform it in those areas while maintaining usability, it could carve out a role as a budget specialist. But until we see numbers on MT-bench, AgentBench, or even basic syntax error rates in code generation, it’s impossible to recommend. For now, GPT-4.1’s consistency wins. Watch this space for updates when o4’s benchmarks land.