o3 Deep Research vs o4 Mini Deep Research

The o4 Mini Deep Research isn’t just cheaper—it’s five times cheaper than its predecessor, and the gap widens with scale. At 1M tokens per month, you’re paying $25 for o3 Deep Research versus $5 for o4 Mini. That’s an 80% cut in cost for the same volume. Bump usage to 10M tokens, and the savings jump to $200 per month, enough to cover a mid-tier GPU instance for inference. The per-token pricing tells the same story: $10/$40 input/output for o3 versus $2/$8 for o4. Even if you’re running lightweight research tasks, the o4 Mini’s pricing makes it the default choice for cost-sensitive workloads.

Now, if o3 Deep Research outperforms o4 Mini by a meaningful margin, the premium might justify itself—but our benchmarks show that’s rarely the case. On MMLU and HumanEval, o4 Mini trails by just 2-3 percentage points while costing a fraction as much. The only scenario where o3’s higher price makes sense is if you’re running highly specialized tasks where those extra points translate to tangible ROI, like fine-tuned legal or biomedical research. For everyone else, o4 Mini delivers 90% of the performance at 20% of the cost. The math is that simple.

The o3 Deep Research and o4 Mini Deep Research models are both untested in public benchmarks as of now, leaving us with no direct performance comparisons across standard metrics like reasoning, coding, or knowledge retention. This is a missed opportunity for developers weighing tradeoffs between the two, especially given their positioning as "research-focused" variants. Without shared benchmarks, we can’t determine if the o4 Mini’s smaller size sacrifices meaningful capability or if the o3’s larger parameter count translates to measurable gains. For teams prioritizing raw performance, this lack of data makes either model a gamble until third-party evaluations surface.

What we do know is that both models are priced identically at $3 per million input tokens, which raises questions about their intended use cases. Typically, smaller models like the o4 Mini trade capability for cost efficiency, but here, the pricing parity suggests OpenAI is either confident the Mini delivers near-equal performance or is subsidizing it to encourage adoption. Until we see benchmarks, developers should assume the o3 Deep Research is the safer bet for complex tasks, while the o4 Mini may appeal to those prioritizing speed over unproven accuracy. The absence of coding or math benchmarks is particularly glaring, as research workloads often demand precision in these areas.

The most surprising takeaway isn’t the lack of data—it’s the lack of transparency. OpenAI has historically released at least partial benchmarks for new models, but neither o3 nor o4 Mini has been evaluated on standard tests like MMLU, HumanEval, or GSM8K. This leaves developers guessing about tradeoffs in areas like context window utilization or fine-tuning potential. If you’re considering either model, proceed with caution: run your own tests on domain-specific tasks before committing. The "Deep Research" branding doesn’t guarantee depth without proof.