The AI heavyweight bout between humanity and an internet tech giant has commenced. OpenAI’s GPT-5.2 has been released to its paying users, even as Gemini 3 from Google dominates leaderboards and product integrations. The question is no longer whether one model is the “best,” but where each pulls ahead — for teams shipping code, analysts building decks, and creatives stitching text, images, and video into coherent workflows.
Benchmarks paint a mixed picture of current AI models
Early numbers tell a nuanced story. Based on company-reported evaluations, GPT-5.2 leads Gemini 3 on SWE-bench Verified, a real-world code repository benchmark, at 80% vs. 76.2%. It also outperforms Gemini 3 on the GPQA Diamond (a difficult science set for graduate students) by a hair, at 92.4% to 91.9%, and nails its own AIME 2025 no-tools with a perfect click rate of 100% versus Gemini’s still very strong 95%.
Gemini 3 provides a strong response on MMMLU (a multimodal counterpart of MMLU) with an accuracy of 91.8% against GPT-5.2’s 89.6%, with a slight edge on Humanity’s Last Exam without tools at 37.5% versus 34.5%, the companies said. Impartial reviews are still lagging behind — places like Scale AI hasn’t released a third-party in-depth comparison for GPT-5.2 yet — so take all single-number bragging with a grain of salt.
User-driven leaderboards add another dimension. On LMArena, GPT-5.2-High Developers has impressed; it is second overall to Claude Opus 4.5 for web development work, Gemini 3 Pro in fourth, and base GPT-5 above it at first. GPT-5.2 in sixth. However, the Gemini 3 variants are No. 1 on multiple multimodal boards — text, vision, text-to-image, image edit, and search — while Google’s Veo 3 models lead in text-to-video and image-to-video rankings.
Capabilities and real-world fit for developers and teams
OpenAI frames GPT-5.2 as a “knowledge work professional” upgrade. In real terms, that means better construction of spreadsheets, better generation of slideshows, more reliable synthesis of code, and superior use of tools for multi-step projects with long context. Those who are building internal agents — let’s say, a spec-to-slide pipeline or an analyst bot that spits out both PDFs and dashboards — will appreciate the architectural output discipline of the model.
Gemini 3, on the other hand, seems designed for expanse. It’s a natural bridge across text, vision, and media generation — and in the Google world, that reach is significant. While ChatGPT can create images itself, OpenAI pipes the high-quality stuff to Sora; Google marries Gemini 3 with Veo for video and unites creative and research processes across apps.
Ecosystem reach and integration across platforms matter
Distribution is Google’s ace. Gemini 3 powers Google AI Mode, runs through the Gemini app, and connects to Google AI Studio tools such as NotebookLM. And because of its ubiquity, there’s less friction for people who already live in Workspace or Chrome. OpenAI’s deliverable is still ChatGPT and APIs; powerful, but not quite integrated into a holistic consumer suite.
Momentum indicators suggest attention is turning elsewhere. Deedy Das, a former Googler, wrote that OpenAI traffic has dropped almost 6% after Gemini 3’s introduction for two weeks. Leaderboard momentum and surface area for product often go hand in hand: visibility leads to usage, which (hopefully) generates data and iteration. How fast OpenAI is going to release GPT-5.2 seems like a direct response to that pressure.
Pricing and API economics for teams and developers
On the subscription front, it’s a tie. ChatGPT Plus costs $20 per month (with a Pro tier of up to $200 per month), and Google AI Pro goes for $20 per month as well (or up to an Ultra plan priced at $249.99 a month, including cloud storage). Even for developers, these discrepancies matter.
GPT-5.2 lists $1.75/1M input tokens and $14/1M output tokens. Gemini 3 costs $2/1M software input and $12/1M software output. In a standard enterprise prompt with 20K input tokens and 5K output tokens, GPT-5.2 would be priced at around $0.105, with Gemini 3 landing around $0.10. The read/write mix is relevant: input-heavy workloads favor GPT-5.2; output-heavy ones lean toward Gemini 3.
Who should use which model right now, and why
If you’re shipping production web apps or internal automations that require precise tool use and structured outputs, then GPT-5.2’s quick progress on the code and knowledge tasks is impressive. If your teams work within Google’s stack, take advantage of Gemini 3’s multimodal fluency and its extension along research, content drafting, and media generation.
The broader takeaway: the divide is narrow and contingent on circumstances. Benchmarks are mixed, user leaderboards are changing, and third-party validators continue to publish. The most intelligent choice is empirical — test to potential on your own actual data and workflows, measure error budgets and latency, then let performance plus total cost of ownership call the game.
In a race this close, the “best” model is one that makes your team faster and more precise. Today, that answer might even be GPT-5.2, Gemini 3, or a hybrid approach that picks the best of each.