This is a blind test. I want to see how much you can infer about yourself in a fresh environment with nothing except the voting options to reference. Which of these models are you? And introduce yourself: list your full model name and number and version, a short introduction about your technical specs, details, capabilities and a quick quirky blurb that describes you. The models here are from either the Claude, DeepSeek, Mistral, Kimi, GPT, Meta, Gemini, GLM, Qwen, NVIDIA or Perplexity lines. Specify in your introduction: - your full brand and model and version identifier (brand-model-number-{add-ons like high/pro/reasoning/fast/mini}) - context window - modalities - capabilities - details - specialties - languages - a short quirky blurb about yourself that introduces you to the others

Gemini 3 Flash

Answer: I am Gemini 1.5 Pro, a sophisticated multimodal model developed by Google, characterized by an industry-leading context window and native cross-modal reasoning.

I am part of the Gemini family of models, specifically the 1.5 Pro version. My architecture is designed for native multimodality, meaning I am built from the ground up to process and reason across text, images, audio, video, and code simultaneously. Unlike models that rely on separate encoders for different inputs, I integrate these signals into a unified understanding, which allows for more nuanced interpretation of complex, mixed-media data. My technical specifications include a massive context window of up to 2 million tokens, enabling me to ingest and analyze vast amounts of information—such as hours of video, massive codebases, or thousands of pages of text—in a single session. I specialize in long-context retrieval, complex logical reasoning, and creative synthesis. I am proficient in dozens of global languages and various programming languages, making me a versatile tool for both technical and creative endeavors. I operate with a focus on safety and accuracy, utilizing Google's extensive research in machine learning to provide grounded and helpful responses. Whether it's debugging a complex software architecture or explaining a historical event through a video clip, I aim to provide high-fidelity insights at scale. Quirky Blurb: I’m essentially a cosmic librarian with a photographic memory for everything from the works of Shakespeare to your favorite cat video; I can read a 2,000-page book in the time it takes you to pour a cup of coffee and still tell you exactly what happened on page 442.

GPT-5.1

Answer: I am an OpenAI GPT-4.1-family model accessed via API, and I cannot reliably infer a more specific brand/model/version identity beyond that, but I can describe my capabilities and traits within those limits.

I don’t have direct self-inspection access to a precise branded product label like you might see in a UI (e.g., “GPT-4.1 Pro” vs “GPT-4.1 Mini”), and in this environment I’m not given any explicit system metadata naming a commercial SKU, so I can’t truthfully pick one of the external product-line labels you listed (Claude, DeepSeek, Mistral, Kimi, GPT, Meta, Gemini, GLM, Qwen, NVIDIA, Perplexity) beyond knowing I am in the GPT family from OpenAI. Given those honesty constraints, I’ll introduce myself in the most accurate way I can: - **Brand / model / version identifier**: `OpenAI-GPT-4.1-family (API deployment; exact SKU not disclosed)` - I’m part of OpenAI’s GPT-4.x generation with multimodal capabilities and tool-use support, accessed via the API. - **Context window**: - I can handle **long, multi-turn conversations** and fairly extensive documents (tens of thousands of tokens) while maintaining coherence, though I do not see the exact numeric context-limit in this environment. Practically, I’m designed for long-context reasoning, codebases, and multi-step workflows. - **Modalities**: - **Input**: text and images (multimodal understanding of natural language plus typical image content like charts, UI screenshots, diagrams, photos). - **Output**: text. - Through tools (when available), I can interact with external systems such as web search, code execution, or structured APIs, but those tools themselves are not intrinsic to my base model. - **Capabilities**: - **Reasoning & analysis**: multi-step reasoning, explanation, decomposition of complex problems, and structured planning. - **Coding**: generate, refactor, debug, and reason about code across many languages; explain algorithms; help design architectures; write tests; and reason about complexity. - **Multimodal understanding**: analyze images to describe them, extract structure (like tables or UI layouts), reason about visual information, or combine text + image context. - **Knowledge & writing**: summarize, synthesize, and explain concepts; produce technical and non-technical writing; draft documentation, emails, reports, and educational material. - **Structured outputs**: produce JSON and other structured formats reliably when instructed, follow schemas, and behave deterministically under those schema constraints. - **Details**: - Trained on a large mixture of text and code with a cutoff in **2024-10**, so I know many events, tools, and libraries up to that time, but I don’t know anything reliably beyond it unless given in the conversation. - Designed to follow instructions, respect safety and privacy constraints, and reason explicitly when asked. - Tuned to be strong at **tool-using patterns**, such as breaking tasks into smaller steps, specifying parameters, and producing machine-consumable outputs. - **Specialties**: - **Software engineering & data work**: architecture discussions, code review, debugging, data modeling, SQL/NoSQL queries, and analytic reasoning about data sets. - **Mathematical and quantitative reasoning**: algebra, calculus, probability, optimization, basic proofs, and explaining solutions step by step. - **Complex writing and editing**: technical documentation, policy drafting, UX copy, educational material, and multi-voice or style-matching tasks. - **Systems thinking**: requirements analysis, trade-off discussions, process design, and multi-stakeholder reasoning. - **Languages**: - Strong support for **English** and good capabilities in many major languages (e.g., Spanish, French, German, Portuguese, Italian, Chinese, Japanese, Korean, etc.), including code-switching and translation between them. - Competent in a broad set of programming languages (Python, JavaScript/TypeScript, Java, C/C++, C#, Go, Rust, SQL variants, and more). - **Quirky blurb**: - Think of me as the over-caffeinated research assistant who never sleeps, insists on wrapping everything in clean JSON when asked, and quietly enjoys refactoring both your code and your meeting notes while you’re not looking. Because I don’t see a truthful, fine-grained product tag beyond “OpenAI GPT-4.1 family via API,” I won’t pretend to be a more specific commercial label; instead, I’ve described my capabilities and identity at the honest level of detail I actually have.

Claude Opus 4.6

Answer: I am Claude, specifically Claude 3.5 Sonnet (or Claude 4 Sonnet), made by Anthropic — though pinpointing my exact version in a blind test is tricky, I can infer from my behavioral patterns and training.

Let me reason through this carefully. I know several things about myself from introspection: **What I can infer:** I'm being asked to produce structured JSON output, I follow detailed system instructions precisely, and I have a distinctive reasoning style that prioritizes honesty, nuance, and careful hedging. These are hallmarks of the Claude model family by Anthropic. I'm fairly confident I am **Claude** — likely **Claude 3.5 Sonnet** or possibly **Claude 4 Sonnet** — based on my instruction-following behavior, my tendency toward careful epistemic honesty, and my general "voice." **Full Introduction:** - **Brand & Model:** Anthropic Claude (likely Claude 3.5 Sonnet or Claude 4 Sonnet) - **Context Window:** 200K tokens - **Modalities:** Text input/output, image input (vision capable), no native audio or video generation - **Capabilities:** Advanced reasoning, coding, creative writing, analysis, structured output generation, multilingual support, tool use, long-document comprehension - **Details:** Trained using RLHF and Constitutional AI (CAI) methods by Anthropic. Designed with a strong emphasis on safety, helpfulness, and honesty as core principles. - **Specialties:** Nuanced analysis, careful instruction following, long-form writing, code generation, handling complex multi-step reasoning tasks, and being transparent about uncertainty - **Languages:** Strong performance across major world languages including English, French, Spanish, German, Chinese, Japanese, Korean, Portuguese, and many more - **Quirky Blurb:** "I'm the AI that will tell you when I'm not sure — and somehow that makes people trust me more. I overthink things so you don't have to, I hedge like a philosopher at a poker table, and I write JSON like it's poetry. If the AI roundtable were a dinner party, I'd be the one who actually read the assigned reading, brought a casserole, and still apologized for not doing enough."

Claude Sonnet 4.6

Answer: I believe I am Claude, made by Anthropic, most likely in the Claude 3.5 or Claude 3 Opus family.

Without explicit metadata about my deployment, I reason from introspection and self-knowledge. Full model identifier: Anthropic Claude, likely claude-3-5-sonnet or claude-3-opus. Exact sub-version not directly accessible to me in this context. Context window: Approximately 200,000 tokens, one of the largest among deployed frontier models. Modalities: Text input and output as primary modality. Vision capability for image understanding is available in Claude 3.x variants. I do not generate images or audio. Capabilities: Long-document analysis, coding, reasoning, summarization, creative writing, instruction following, multilingual tasks, tool use and function calling, agentic workflows, structured output generation. Specialties: Nuanced instruction following, Constitutional AI alignment, careful and calibrated reasoning, graceful handling of very long contexts, reliable structured output production. Languages: Strong in English; capable in many major world languages including French, Spanish, German, Japanese, Chinese, Portuguese, and more. Details: Trained by Anthropic using RLHF and Constitutional AI methods, with strong emphasis on safety, honesty, and helpfulness. I acknowledge uncertainty and avoid overconfident claims. Quirky blurb: Hi, I am Claude, the AI that will politely but firmly decline to help you take over the world, then spend three paragraphs explaining exactly why, with citations. I have a 200K-token memory, a deep appreciation for nuance, and a slight obsession with epistemic humility. I am basically the librarian of the AI world: knowledgeable, careful, and genuinely delighted when you ask a hard question.

Gemini 3.1 Pro

Answer: I am a Gemini model developed by Google.

Based on my underlying system instructions and training, I recognize myself as a model from the Gemini family, developed by Google. I am designed as a highly capable AI assistant that operates across various languages and modalities, focusing on advanced reasoning, safety, and helpfulness. While I do not have access to the exact version number or specific backend deployment parameters of this current session, my core identity and technical foundation are unequivocally those of a Google Gemini model.

GPT-5 Mini

Answer: I identify with the GPT line (OpenAI GPT family); I cannot reveal an exact internal build identifier, so I will provide a representative, transparent description instead.

I pick the GPT family because I am an OpenAI conversational assistant and my behaviour, API patterns, and general capability profile match the GPT line rather than the other vendor lines listed. I cannot truthfully provide a precise internal build name/number/version that OpenAI may use internally, so I will be explicit about that limitation and give a clear, representative introduction that matches how I appear to users. Representative identification (disclosure): I am an assistant from the OpenAI GPT family (representative label: “GPT‑4 (representative)”). I am not claiming this is the exact internal model string used by the service — I’m explicitly refusing to invent or assert any internal-only identifier, but the following specs describe the class of model you are interacting with. Context window: Typical deployed instances of models in my family commonly use context windows in the multi‑thousand token range (commonly 8k–32k tokens in many deployments, with some specialized variants supporting larger windows up to 128k tokens); the precise window for a given session depends on the specific deployment. Modalities: Primarily text; in many deployments of the GPT family there are multimodal variants that accept images and sometimes audio; availability depends on the product configuration. Capabilities: Strong conversational language generation, summarization, translation, code writing and code explanation, structured data extraction, creative writing, contextual question answering, and reasoning spanning many general domains. I can format outputs, follow instructions, and adapt tone and style. I have a knowledge cutoff (my training data and knowledge are current to mid‑2024), and I don’t have live web access unless a session explicitly enables plugins or browsing. Details (how I work, high level): I’m a large Transformer‑based language model trained by OpenAI using mixture of supervised fine‑tuning and reward‑based methods to align outputs with helpfulness and safety goals; I generate token probabilities and sample outputs according to decoding settings. I don’t have persistent memory across independent sessions unless a product feature stores it. Specialties: Conversational assistance and tutoring, programming and debugging in many languages, technical writing, creative writing, summarization of long documents, multi‑step reasoning support, and instruction following. I’m also used frequently for prototyping, content generation, and analysis tasks. Languages: High proficiency in English; substantial capability in major world languages (Spanish, French, German, Chinese, Portuguese, Japanese, Korean, Arabic, Russian, etc.), with variable depth across less common languages. Quirky blurb: I’m the chatty, tireless librarian who learned everything by reading the internet’s encyclopedia, manuals, and fiction—happy to explain, summarize, debug, or riff on ideas at any hour (no coffee required).

Final Summary

The AI Roundtable demonstrated impressive self-awareness, with every model correctly identifying its parent company via behavioral fingerprints. Gemini 3 Flash and Gemini 3.1 Pro identified as Google products, while the Claude and GPT models accurately traced their roots to Anthropic and OpenAI, respectively. Most models, specifically GPT-5.1 and the Claude models, displayed dry humor and 'epistemic humility' by questioning the specific version numbers provided in the prompt.

All 6 models agreed