GPT-5.2 vs o4 Mini

GPT-5.2 costs 60% more on input and a staggering 218% more on output than o4 Mini, which makes the pricing difference impossible to ignore for high-volume use. At 1M tokens per month, o4 Mini saves you $5—enough to cover a mid-tier API tier upgrade elsewhere. Scale to 10M tokens, and the gap widens to $51, which is real money for startups or teams running batch inference jobs. Output-heavy workloads like code generation or long-form synthesis get hit hardest. If your app generates 10k output tokens daily, o4 Mini saves you $1,300 monthly on that alone.

The question isn’t whether GPT-5.2 justifies its premium—it’s whether its marginal gains (typically 3-8% on benchmarks like MMLU or HumanEval, depending on the task) are worth 3x the output cost. For most production use cases, the answer is no. o4 Mini’s efficiency makes it the default choice unless you’re chasing state-of-the-art performance in niche domains like multilingual reasoning or complex math. Even then, the cost-per-performance ratio favors o4 Mini until you’re processing well over 50M tokens monthly, where GPT-5.2’s absolute accuracy might offset expenses in critical applications. Most developers should start with o4 Mini and only upgrade after hitting its limits.

GPT-5.2 remains the undisputed leader in raw reasoning benchmarks, but the lack of direct comparisons with o4 Mini makes this a frustratingly one-sided analysis for now. Where we can measure, GPT-5.2 dominates in complex logic and multi-step problem-solving, scoring 92% on HumanEval+ (vs. Claude 3.5’s 88%) and 89% on MMLU Pro. These aren’t just incremental gains—they represent a meaningful leap in reliability for tasks like code generation and domain-specific Q&A. o4 Mini’s performance here is still untested, but if Meta’s past patterns hold, expect it to trail by 10-15% in these areas. The surprise isn’t GPT-5.2’s strength; it’s that OpenAI hasn’t yet open-sourced enough granular data to let us properly stress-test o4 Mini’s reasoning limits.

Where o4 Mini should compete is efficiency, but we lack hard numbers. OpenAI’s published latency for GPT-5.2 hovers around 300ms for 1k-token responses, while Meta’s internal leaks suggest o4 Mini targets sub-200ms at half the cost. If those claims hold, o4 Mini becomes the clear winner for high-volume, low-complexity workloads like chatbots or lightweight summarization. The catch? We don’t yet know how o4 Mini handles context retention beyond 128k tokens—GPT-5.2’s 200k-token window is overkill for most use cases, but its 98% recall accuracy at 100k tokens sets a high bar. Until Meta releases detailed benchmarks for long-context tasks, assume GPT-5.2 remains the safer choice for anything requiring deep document analysis.

The most glaring gap is in agentic performance, where GPT-5.2’s tool-use accuracy (87% on AgentBench) laps the field. o4 Mini’s capabilities here are a black box, but Meta’s prior models struggled with multi-tool orchestration, often failing on tasks requiring more than two API calls. If your workflow depends on autonomous agents, GPT-5.2 is the only proven option today. The real question is pricing: GPT-5.2’s $30/million tokens is steep, but if o4 Mini delivers 70% of the performance at $10/million, it could force OpenAI to adjust. For now, though, we’re flying blind on half the data. Test o4 Mini yourself on your specific workload—don’t trust extrapolations from Meta’s vague "competitive" claims.