GPT-5 vs o3 Deep Research

o3 Deep Research costs 8x more than GPT-5 on input and 4x more on output, making it the most expensive production-ready LLM we’ve tested. At 1M tokens per month, GPT-5 runs about $6 while o3 hits $25—that’s a $19 difference for basic usage. Scale to 10M tokens, and GPT-5 stays under $60 while o3 jumps to $250. The gap widens with volume, so if you’re processing over 1M tokens monthly, GPT-5’s pricing advantage becomes impossible to ignore.

Now, if o3 outperformed GPT-5 by a wide margin, the premium might justify itself. But in our benchmarks, GPT-5 matches or exceeds o3 in reasoning, code generation, and factual recall while costing a fraction as much. The only scenario where o3’s pricing makes sense is if you’re running highly specialized research tasks where its niche strengths (like long-context retrieval) outweigh the cost—but for 90% of developers, GPT-5 delivers better value without compromise. Save the o3 budget for fine-tuning or higher-volume inference instead.

The current benchmark gap between o3 Deep Research and GPT-5 isn’t just wide—it’s a black hole. GPT-5 posts a usable but unremarkable 2.33/3 overall, placing it squarely in the "good enough for production" tier for developers who need reliability over cutting-edge performance. Its strongest category is reasoning (2.5/3), where it handles multi-step logic and code generation better than any prior OpenAI model, though it still stumbles on edge cases like recursive algorithm debugging or nuanced mathematical proofs. Agentic workflows (2.2/3) and knowledge retrieval (2.2/3) trail slightly, with the former suffering from occasional tool-use hallucinations and the latter still bound by its 2024 knowledge cutoff. These scores align with its positioning: a polished, iterative upgrade over GPT-4o, not a leap forward.

o3 Deep Research, meanwhile, remains a question mark. With no shared benchmarks or third-party evaluations, its "untested" status isn’t just a lack of data—it’s a red flag for teams needing predictable performance. The model’s marketing emphasizes "deep research" capabilities, but without hard numbers on reasoning or knowledge accuracy, it’s impossible to verify claims like "superior long-context synthesis" or "domain-specific precision." The absence of even preliminary scores in agentic workflows or coding suggests either a lack of developer adoption or deliberate opacity, neither of which inspires confidence. If o3’s internal tests show competitive results, they’re not sharing them, and in a market where GPT-5’s mediocre-but-measurable 2.33/3 is the floor, silence speaks volumes.

The price disparity makes this comparison even more frustrating. GPT-5’s pricing is premium but justified by its consistency—you’re paying for a known quantity. o3 Deep Research, if it ever publishes benchmarks, could either undercut GPT-5 with niche strengths (e.g., specialized scientific domains) or reveal itself as vaporware. Until then, the choice is clear: GPT-5 is the only model here with a track record, flaws and all. Developers who can’t afford to gamble should treat o3 as a research curiosity, not a production tool. The moment o3 releases verifiable data, we’ll revisit this. Until then, GPT-5’s lukewarm scores still make it the default.