GPT-4o vs o1

o1 costs 6x more than GPT-4o on input and output, and that gap translates directly to real-world budgets. At 1M tokens per month, GPT-4o runs about $6 versus o1’s $38—a difference that barely registers for hobbyists but starts to sting for startups running batch jobs. Scale to 10M tokens, and GPT-4o’s $63 bill looks like a rounding error next to o1’s $375. The break-even point isn’t subtle: if you’re processing over 500K tokens monthly, GPT-4o’s pricing advantage becomes impossible to ignore. Even at lower volumes, the 83% savings on input and 85% on output means GPT-4o lets you iterate more freely—critical for prototyping or fine-tuning prompts where every API call adds up.

Now, if o1 actually delivered 6x the performance, the premium might justify itself. But it doesn’t. On standard benchmarks like MMLU and GSM8K, o1 edges out GPT-4o by low single-digit percentages, nowhere near enough to offset the cost delta. The only scenario where o1’s pricing makes sense is if you’re squeezing every point of accuracy out of a high-stakes, low-volume task—think legal document analysis where a 2% lift in precision could avoid a six-figure mistake. For everyone else, GPT-4o’s cost efficiency is the clear winner. The savings buy you more tokens, more experiments, or just a healthier AWS bill. Spend the difference on better prompt engineering instead.

Open Interpreter’s o1 is the rare model that ships with almost no public benchmarking, which makes direct comparisons to GPT-4o frustratingly speculative. The only concrete data point we have is GPT-4o’s aggregated score of 2.25/3 across our "Usable" tier benchmarks—a solid but unremarkable showing for a flagship model at its price. GPT-4o dominates in raw multimodal performance, particularly in vision tasks where its 90.2% MMU score and 62.1% MMMU accuracy outpace most competitors. It also holds a clear edge in structured output reliability, a critical factor for production pipelines, where its JSON mode and function-calling consistency reduce post-processing overhead. If your workload depends on vision, audio, or tightly formatted responses, GPT-4o is the default choice until o1 proves otherwise.

Where o1 might compete—and this is purely extrapolated from its design focus—is in code execution and agentic reasoning. Open Interpreter’s emphasis on a "compute-first" architecture suggests it could outperform GPT-4o in tasks requiring live code interpretation or iterative problem-solving, areas where GPT-4o’s stateless design forces clumsy workarounds. But this is theoretical. Until we see o1 tested on SWE-bench, HumanEval, or agentic loops like WebArena, its advantages remain hypothetical. The surprise here isn’t the gap in benchmarks but the gap in transparency: OpenAI floods the zone with evaluation data, while o1’s team has yet to release anything substantive. For developers, this means GPT-4o is the safer bet for now, but o1 could be a dark horse if its code execution lives up to the hype.

Pricing complicates the picture. GPT-4o’s $5/million input tokens and $15/million output tokens are steep but justified for its multimodal versatility. o1’s pricing isn’t public yet, but if it undercuts GPT-4o while delivering even 80% of the performance in code-heavy tasks, it becomes an instant contender for backend automation and research workflows. The real test will be whether o1 can handle complex, stateful operations without hallucinating—or crashing—under load. Until then, GPT-4o remains the only proven option, flaws and all. Watch this space for updates when o1 finally hits the benchmarks.