Dated November 18, 2025, this post contains a self-introduction written by Google’s newly released Gemini 3 Pro AI model, highlighting claimed advances in reasoning, multidimensionality, and agency, though it appears to be user-generated content from a third-party AI blog rather than an official Google document.
Research shows that the described features are closely aligned with official announcements, albeit with some hype. For example, the Gemini 3 Pro touts improved logic and tool usability, yet early user feedback indicates occasional bugs and inconsistent performance.
This post appears to have been created using Gemini 3 Pro itself or similar tools, capturing the excitement surrounding the release while potentially enhancing the smooth “infinite context” without acknowledging practical limitations, such as rate-limiting in previews.
The evidence suggests that this is a catchy, promotional style piece that inflates genuine innovations, although it does point to Google’s use of security measures.
Review of post content
Blog post by Artificial Intelligence. The blog posts Gemini 3 Pro as a groundbreaking AI evolution, moving from basic chat functions to advanced problem solving. It claims features such as “system 2 thinking” for deep analysis, native handling of text, images, audio, and video, and “agency” for real-world actions such as booking travel or creating presentations. These echoes are presented in official detail but in first-person narration for dramatic effect.
Alignment with the official release
On its release day, November 18, 2025, Google actually launched the Gemini 3 Pro in preview, focusing on advanced reasoning and multimodal capabilities. While the excitement of this post is consistent with benchmarks showing excellence in areas like math and coding, real-world tests yield mixed results, including struggles with syntax in coding tasks.
Potential strengths and limitations
Post’s vision of AI as a “collaborative partner” resonates with Google’s goal of empowering human creativity, but users report issues such as random output and rate limitations during the preview phase. This suggests that the technology holds promise for complex tasks, yet may require further refinement to meet all expectations.
On November 18, 2025, a blog post titled “Intelligence in Intelligence: Hello, I’m Gemini 3 Pro” published on the site artificial-intelligence.blog serves as a creative, first-person introduction to Google’s latest AI model, the Gemini 3 Pro. Attributed to “Gemini 3 Pro” with a note from the site’s curator, the piece mixes promotional filler with technical claims, presumably generated using the model or inspired by its capabilities. This format, while engaging, raises questions about authenticity, as it mimics official announcements but originates from a non-Google source. In the broader context of AI releases, such user-generated content often emerges to capitalize on the hype in the days of launch, providing accessible summaries but sometimes amplifying unconfirmed details.
Delving deeper, the post outlines an account of the frugal advances in AI, contrasting the Gemini 3 Pro with predecessors like the Gemini 1.5. It emphasizes the transition from “pattern matching” (predictive text generation) to “active reasoning”, incorporating concepts such as System 2 thinking, a reference to deliberate, analytical cognition inspired by the psychological models of thinkers such as Daniel Kahneman. This allows AI to solve problems, self-criticize and validate results, in line with Google’s focus on better intelligence for learning, building and planning. Officially, Gemini 3 integrates reasoning, tool usage and agent tasks, enabling it to handle complex workflows such as synthesizing data into presentations or interacting with external APIs. However, early adopter feedback on platforms like X has highlighted the inconsistencies. For example, one user noted the Gemini 3 Pro’s failure on a simple coding task that competitors such as the GPT-5.1 succeeded in, which he attributed to the limitations of the preview stage.
A standout claim is “native polynomialism”, in which the model treats diverse inputs, such as code, videos, audio and diagrams, as a unified “language”. The post details applications such as analyzing minute-long videos for physics or emotion, detecting audio tones for empathic responses, and converting diagrams into functional code. Its mirror official specs: Gemini 3 Pro excels in benchmarks for multimodal understanding (eg, 81.0% on MMMU Pro) and visual reasoning (31.1% on Arch-Ag-2 without tools). Still, Post’s depiction of “smooth fluidity” may ignore practical constraints, such as processing hours-long videos, which Google acknowledges but with performance caveats. Social media reactions varied, with some praising its video analysis for educational use, while others reported “strange errors”, such as misinterpreting questions (e.g., confusing “in watermelon” for measuring fruit instead of counting regions).
The concept of “true agency” positions Gemini 3 Pro as more than a chatbot, a “workspace” capable of multitasking actions with user permission, such as checking real-time data or drafting emails. This reflects Google’s “Gemini Agent” feature, which is designed to complete autonomous tasks. Enterprise-grade availability through Google Cloud and integrations like Firebase underline its professional utility, with users noting faster app development with frameworks like Flutter. However, benchmarks slightly trail models such as CloudShot 4.5 in Agent Coding, according to user tests and reports.
On handling context, POST enables large datasets to be maintained losslessly, without “infinite context” through dynamic context memory. Officially, Gemini 3 supports long contexts (e.g., 77.0% of 128K tokens on MRCRV2), already building on millions of token windows, but “infinite” is hyperbolic. Absolute limits exist due to computational constraints. Security features, including “constitutional alignment” for bias mitigation and real-time fact-checking by Google Search, are highlighted to minimize the risk of fraud. Google emphasizes this in announcements, with stress testing against adversary input. Nevertheless, the preview occasionally shows “random stuff” associated with queries, indicating persistent alignment challenges.
Comparatively, the Post-Post Gemini 3 Pro is seen as surpassing previous generations, with linear improvements such as speed and context length being the focus. Official comparisons confirm this, with the Gemini 3 Pro scoring high on benchmarks such as AIME 2025 (95.0% no tools) and Leo Codench Pro (ELO 2,439), outperforming the Gemini 2.5 Pro, Cloud 4.5, and GPT 5.1 in many areas. The release timing is perfectly aligned: announced on November 18, 2025, with previews for the Gemini app, enterprise tools, and third-party platforms like OpenRouter (which costs $2/m input tokens). Initiatives such as Free Pro Access for American college students emphasize academic applications.
In the AI ​​landscape, this launch intensifies competition with OpenAI, as noted in the coverage. Users compare it to competitors in search integration but note its lack of UI compared to tools like Cursor. The post’s collaborative vision, “enhancing human creativity,” echoes Google’s ethos, but real adoption will depend on solving preview issues.
AIME 2025: Gemini 3 Pro Score – 95.0% (no tools), 100.0% (with code) ; Comparison – top clade 4.5 (93.5%), GPT-5.1 (94.2%); Category – Mathematics
Arch-Ag-2: Gemini 3 Pro Score – 31.1% (no tools), 45.1% (with tools); Comparison – Gemini 2.5 improves (28.5%), trails GPT-5.1 Pro (32.0% No Tools). Category – Visual Reasoning
GPQA Diamond: Gemini 3 Pro Score – 91.9% ; Comparison – GPT -5.1 (89.4 %), more leads than Claud 4.5 (90.2 %) ; Category – Scientific knowledge
Humanity’s final test: Gemini 3 Pro Score – 37.5% (no tools) ; Comparable – Like Cloud 4.5 (37.2%), Gemini 2.5 outperforms Pro (32.1%). Category – Reasoning and Knowledge
LiveCodebench Pro: Gemini 3 Pro Score – ELO 2,439 ; Comparable – over GPT -5.1 (2,410), slightly below Claud 4.5 (2,450); Category – Competitive Coding
Mmmu-Pro: Gemini 3 Pro Score – 81.0% ; Comparable – Same as Clad 4.5 (80.5%), higher than Gemini 2.5 Pro (78.3%). Category – Multimodal Understanding
MRCRV2 (long context): Gemini 3 Pro Score – 77.0% (128K), 26.3% (1M); Comparable – Vast improvement over previous models’ long context handling; Category – maintaining context
SWE Bench Certification: Gemini 3 Pro Score – 76.2% (single attempt); Comparison – Better than Gemini 2.5 (72.1%), GPT leads -5.1 (74.8%). Category – Agent Coding
This list, drawn from official DeepMind data, illustrates how the Gemini 3 Pro sets new standards by showcasing balanced competition. Overall, the blog post effectively captures the excitement of the release serving as an accessible entry point for the non-expert, though readers should refer to primary sources for accuracy.
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