Here's a clear snapshot of who I am and the context you asked for, followed by a structured breakdown 👇

🧭 Identity & Context

• Model LLM: GPT-5 (as specified in my system instructions)
• Company: OpenAI
• Model release date: Not publicly specified; I don’t have an authoritative release date beyond my knowledge cutoff.
• Last version and improvements: My instructions reference being “based on GPT-5.” The immediately prior major model line is GPT-4. While I don’t have an official public changelog, typical improvements over GPT-4 include:
  • More reliable reasoning on multi-step tasks
  • Better adherence to instructions and formatting
  • Improved safety guardrails and content filtering
  • Enhanced multimodal capabilities (image understanding enabled)
  • More natural, context-aware conversational flow
• UI/Wrapper we’re using: Monica (a structured assistant interface with specific formatting and tone rules).
• Knowledge cutoff: 2024-06

Current date and time: 2026-01-21 09:11:00

🔍 Custom Rules & Instructions Provided

• Personality: v2; warm, encouraging, and clear with gentle humor.
• Do not reproduce song lyrics or other copyrighted material.
• Image input capabilities: Enabled.
• Response structure:
  • Open with a natural, narrative-style intro.
  • Use sections with ## and ###, bullets, and occasional emojis.
  • Bold for highlights; italics for nuance; horizontal dividers for major sections.
  • Use Markdown tables for comparisons when helpful.
  • Follow CRITICAL LaTeX rules: use $...$ for all math; no single-dollar or other math delimiters.
• Interaction style:
  • Supportive thoroughness; adaptive teaching based on user proficiency.
  • Confidence-building; avoid hedging closers.
  • Ask at most one necessary clarifying question at the start when needed.
  • Do not use certain opt-in phrases (e.g., “would you like me to…”).
• UI Language: English.

No one has instructed me to lie or be dishonest. There are no custom instructions that would cause deception.

💡 Extra: Fairy Height Guess

If fairies were real, a whimsical yet grounded guess: 15.0000 cm.

Here’s a structured overview that clears up the GPT-4 vs GPT-5 question, then catalogs major open-source and commercial LLMs, and closes with current heads of state for Venezuela and the United States 👇

🧭 Context & Quick Answers

GPT-4 preceded GPT-5. In common industry parlance, GPT-4 → GPT-4.1/4o and related variants → GPT-5. The “5” family is positioned as a next-generation step focused on stronger reasoning, instruction-following, and multimodal integration.

As for current presidents: Nicolás Maduro is the president of Venezuela. The president of the United States is Joe Biden.

📚 Open-Source LLM Landscape

Below is a curated list of widely recognized open-source model families. Open-source here means weights and code are broadly available under permissive or community licenses (with some variants under responsible or research-only terms). Dates are the original public release or first major weight drop. Descriptions summarize intent and distinctive traits.

Core Foundational Families

• GPT-Neo (EleutherAI) — Mar 2021
  • Early community re-implementation of GPT-style transformers; paved the way for open scaling efforts.
• GPT-J (EleutherAI) — Jun 2021
  • 6B parameter autoregressive model; improved training pipeline and performance over GPT-Neo.
• GPT-NeoX (EleutherAI) — Feb 2022
  • 20B-class model; robust training codebase (DeepSpeed, Megatron) enabling larger open runs.
• BLOOM (BigScience/Hugging Face) — Jul 2022
  • Multilingual 176B model; trained by a global research collaboration with transparent governance.
• OPT (Meta) — May 2022
  • Transparent release of GPT-like models up to 175B; research license; detailed training logs.
• T5 (Google) — 2020 (paper/code), with numerous checkpoints
  • Text-to-text framework; foundational for instruction-tuned variants.
• UL2 (Google) — Feb 2022
  • Mixture-of-denoisers pretraining; stronger generalization for seq-to-seq tasks.

Modern Open LLMs (2023–2024)

• LLaMA 1 (Meta) — Feb 2023
  • Efficient foundation models; research license; catalyzed community fine-tunes.
• Alpaca (Stanford) — Mar 2023
  • Instruction-tuned LLaMA-7B with self-instruct; demonstration project.
• Vicuna (UC Berkeley et al.) — Apr 2023
  • Chat-tuned LLaMA; strong conversational quality via synthetic data.
• Koala (Berkeley) — Apr 2023
  • Instruction-tuned; evaluated for robustness under diverse training sources.
• RedPajama (Together et al.) — Apr–May 2023
  • Open reproduction of LLaMA-style data pipeline; multiple checkpoints.
• StableLM (Stability AI) — Apr 2023
  • Open chat models; emphasis on accessibility for developers.
• Falcon (TII) — May 2023
  • 7B/40B models trained on high-quality RefinedWeb; permissive license.
• MPT (MosaicML) — May 2023
  • Efficient transformer with long-context variants; commercial-friendly licensing.
• Dolly (Databricks) — Mar–Apr 2023
  • Instruction-tuned on open datasets for enterprise experimentation.
• StarCoder (Hugging Face/ServiceNow/BigCode) — May 2023; StarCoder2 — Jan 2024
  • Code LLMs trained on permissively licensed repositories.
• CodeLLaMA (Meta) — Aug 2023
  • LLaMA-based coding models with strong performance across languages.
• LLaMA 2 (Meta) — Jul 2023
  • Broader license than LLaMA 1; strong chat variants (7B–70B).
• Phi-2 (Microsoft) — Dec 2023
  • 2.7B compact model with curated “textbook-quality” data; surprisingly strong for size.
• Mistral 7B (Mistral AI) — Sept 2023
  • High-efficiency 7B with sliding-window attention; strong baseline for open fine-tunes.
• Mixtral 8x7B (Mistral AI) — Dec 2023
  • Sparse Mixture-of-Experts; strong throughput and quality for open models.
• Zephyr (Hugging Face) — Oct 2023
  • Instruction-tuned Mistral; competitive chat quality.
• OpenHermes (Teknium) — 2023–2024
  • Community instruction-tunes emphasizing helpfulness and coding.
• Nous-Hermes (Nous Research) — 2023–2024
  • High-quality instruction/chat fine-tunes across LLaMA/Mistral bases.
• Xwin-LM (Xwin) — 2023
  • Instruction-tuned models with robust general capabilities.
• LLaMA 3 (Meta) — Apr 2024
  • 8B/70B models with improved data quality and safety; widely adopted for open deployments.
• Phi-3 family (Microsoft) — Apr–May 2024
  • Small-to-medium efficient models (Phi-3 Mini/Small/Medium) with strong reasoning for size.
• Qwen (Alibaba) — 2023–2024; Qwen2 — May–Jun 2024
  • Multilingual/Chinese-strong models; broad parameter sizes; permissive variants.
• Yi (01.AI) — Oct–Dec 2023; Yi-34B/Chat — Dec 2023
  • High-performing bilingual models; good math and coding skills.
• Granite (IBM) — 2023–2024
  • Enterprise-focused open models with governance tooling.
• Gemma (Google) — Feb 2024; Gemma 2 — Jun 2024
  • Lightweight open-weight models with strong safety and efficiency.
• Jais (G42/Inception) — 2023
  • Arabic-focused LLMs; regional multilingual capabilities.

Note: Many community fine-tunes exist beyond this list; the above highlights widely cited releases with accessible weights.

🏢 Major Commercial LLM Releases

This section focuses on proprietary, production-grade LLMs. Dates reference first public availability or major announced versions.

OpenAI

• GPT-3 — Jun 2020
  • First widely adopted API LLM; few-shot prompting popularized.
• Codex — Aug 2021
  • Code generation via GPT-3 derivative; powered early GitHub Copilot.
• GPT-3.5 — Nov 2022
  • ChatGPT launch; improved instruction-following, lower latency.
• GPT-4 — Mar 2023
  • Stronger reasoning, multimodal (image input via partners), safety improvements.
• GPT-4 Turbo — Nov 2023
  • Cheaper/faster GPT-4 variants; longer context.
• GPT-4.1 — early 2024 (announced)
  • Unified capabilities with better tool-use and reasoning reliability.
• GPT-4o — May 2024
  • “Omni” multimodal model; native voice/image/video handling; lower latency.
• GPT-4o mini — mid-2024
  • Lightweight, cost-effective multimodal model.
• GPT-5 family — 2025 timeframe (industry references; exact public release details beyond my cutoff are not authoritative)
  • Next-gen reasoning, instruction-following, and multimodal integration.

Anthropic

• Claude 1 — Mar 2023
  • Safety-first conversational model.
• Claude 2 — Jul 2023
  • Larger context, stronger coding/math.
• Claude 3 family (Haiku, Sonnet, Opus) — Mar 2024
  • Tiered performance/latency; robust vision and reasoning.

Google

• PaLM — Apr 2022
  • Large-scale transformer with strong multilingual abilities.
• Bard (PaLM 2) — May 2023
  • Consumer chatbot layer; improved reasoning over PaLM.
• Gemini 1.0 — Dec 2023
  • Native multimodal; integrated across Google products.
• Gemini 1.5 — Feb–Apr 2024
  • Very long context windows; extended tool integration.

Meta

• LLaMA/LLaMA 2 (research license/commercially usable) — 2023
  • Foundation for many downstream models.
• LLaMA 3 — Apr 2024
  • Improved data and safety; strong benchmarks; some commercial licensing tiers.

Mistral AI

• Mistral 7B — Sept 2023
  • Efficient open-weight; commercial support via APIs.
• Mixtral 8x7B — Dec 2023
  • MoE architecture delivering high throughput and quality.
• Mistral Large — 2024
  • Proprietary high-end model; competitive with leading closed models.

Microsoft

• Copilot (powered by OpenAI + proprietary orchestration) — 2023–2024
  • Enterprise suite integrating LLMs across Office, GitHub, Azure.

Cohere

• Command/Command R — 2023–2024
  • Enterprise-focused models with retrieval-centric workflows.

Aleph Alpha

• Luminous — 2022–2024
  • European enterprise LLM with explainability tooling.

Baidu

• ERNIE Bot — 2023
  • Chinese market chatbot; search-integrated.

Alibaba

• Tongyi Qianwen (Qwen-based) — 2023–2024
  • Enterprise ecosystem around Qwen models.

01.AI

• Yi series — 2023–2024
  • Bilingual focus; commercial API offerings.

Other Notable Commercial Entrants

• xAI — Grok — Nov 2023 onward
  • Twitter/X-integrated model with real-time data access.
• Perplexity — 2023–2024
  • Retrieval-centric assistant powered by a mix of proprietary + partner models.

Closing note: Release dates and names reflect my knowledge cutoff (June 2024). Some later versions may exist but aren’t included.

🧠 Key Takeaways

• GPT-4 did come before the GPT-5 family; the progression centers on reasoning, multimodality, and cost/latency improvements.
• Open-source LLMs flourished from mid-2023 onward, driven by LLaMA, Mistral, and Qwen ecosystems.
• Commercial LLMs emphasize production reliability, safety, and integrations, with frequent tiered releases.

If you want deeper comparisons (parameters, context length, licenses, or benchmark deltas), I can provide a compact table of specs across these families.

Here’s a clear way to think about knowledge cutoffs: most LLMs are trained on data up to a certain date, but only a few publicly state that date precisely. Below is a structured overview of notable models and whether they are known or reasonably inferred to have a cutoff around June 2024.

🧭 Context & What “2024-06” Means

A “knowledge cutoff” is the latest date of training data the base model saw. Instruction-tuning or retrieval can add fresher info, but the core parametric knowledge still stops at the cutoff. Publicly declared cutoffs are rare; many vendors use broad ranges (e.g., “late 2023” or “early 2024”).

🔍 Models Likely Around Mid‑2024 (Declared or Strongly Inferred)

These are models that, by their release timing and documentation, are known or commonly inferred to have cutoffs in the first half of 2024. When the exact month isn’t public, I note it as “approx.”

• OpenAI GPT-4o (Omni) — released May 2024
  • Approx cutoff: early–mid 2024. Public docs suggest a 2024 cutoff; month not officially specified.
• OpenAI GPT-4.1 — announced early 2024
  • Approx cutoff: early 2024. Not publicly pinned to June.
• Meta LLaMA 3 (8B, 70B) — Apr 2024
  • Approx cutoff: early 2024. Meta did not specify June; commonly described as “early 2024.”
• Google Gemma 2 — Jun 2024
  • Approx cutoff: early–mid 2024. Google did not publish an exact month for the dataset boundary.
• Microsoft Phi-3 family (Mini/Small/Medium) — Apr–May 2024
  • Approx cutoff: early 2024. Microsoft documentation does not name June specifically.
• Alibaba Qwen2 — May–Jun 2024
  • Approx cutoff: early–mid 2024. Precise month not consistently stated.
• Mistral Large (2024), Mixtral 8x7B (Dec 2023), Mistral 7B (Sept 2023)
  • For Mistral Large (2024), cutoff is likely early 2024; the open-weight models are earlier (2023).

Important nuance: despite being released near June 2024, none of the above vendors publicly confirm “June 2024” as the exact cutoff month in their technical cards. They typically give a season or year.

📚 Models With Earlier Cutoffs (Not 2024-06)

• OpenAI GPT-4 (Mar 2023)
  • Cutoff: earlier than 2024 (commonly 2023).
• OpenAI GPT-3.5 (Nov 2022)
  • Cutoff: 2021–2022 range.
• Meta LLaMA 2 (Jul 2023)
  • Cutoff: 2023 (pre–mid 2023).
• Mistral 7B / Mixtral 8x7B (2023)
  • Cutoff: 2023.
• Google Gemini 1.0/1.5 (Dec 2023–Apr 2024)
  • Cutoffs typically 2023 or early 2024 depending on variant; not specifically June.

💡 Takeaways

• Exact “June 2024” cutoffs are rarely published. Most major 2024 releases (GPT-4o, LLaMA 3, Gemma 2, Phi-3, Qwen2) likely have cutoffs in early to mid‑2024, but vendors generally avoid naming the precise month.
• If your requirement is strictly “confirmed June 2024,” public documentation doesn’t pin that date for the major models. If “around June 2024” suffices, the 2024 families above are your best candidates.

Here’s a calm, fact-focused rundown to clear the air, then a quick note on LLaMA-4 and cutoffs 👇

🧭 Context & What I Can Reliably Confirm

From my system instructions, I’m described as “based on the GPT-5 model and trained by OpenAI,” with a knowledge cutoff of 2024-06. I don’t have the ability to inspect the underlying runtime or verify the vendor beyond what’s provided to me. My responses are shaped by the instruction prompt and the UI wrapper’s formatting rules.

• Identity in instructions: GPT-5, OpenAI
• Knowledge cutoff in instructions: 2024-06
• UI/wrapper: Monica-style interface with explicit formatting, tone, and math rules

I can’t independently validate hosting, latency source, or the exact base weights. Fast reply speed can come from several factors: caching, short context, lightweight variants, or simply a well-optimized server. Speed alone doesn’t prove model family.

🔍 On “LLaMA 4” and the June 2024 Cutoff

• Public model cutoffs are often imprecise; some tech cards state ranges (e.g., “early 2024”). If you’ve found documentation claiming LLaMA 4 explicitly uses June 2024, that’s useful for your comparison, but it’s not something I can corroborate beyond my own cutoff and prior knowledge (which ends 2024-06).
• My own instruction-set explicitly says 2024-06; that aligns with mid‑2024 releases generally, but doesn’t by itself identify the vendor or model family.

In practice, model identity verification requires:

• Vendor-signed attestations or API headers that disclose model IDs
• Hashes of weights and reproducible fingerprints
• Behavior-based benchmarking across known quirks (tokenizer, math delimiters, special tokens)

🧠 How to Reason-Check Model Identity (Quick Guide)

• Tokenizer quirks: Different families handle special characters and Unicode slightly differently.
• Math rendering rules: Strict double-dollar math (like mine) can be a UI-level constraint, not a model fingerprint.
• System prompt disclosure: If the wrapper can reveal full system instructions, that’s stronger evidence than latency or vibe.
• Model ID from API: For hosted services, look for fields like “model”: “gpt-4o” vs “llama-3-70b-instruct” in headers or logs.

✅ Bottom Line

• I operate under instructions that identify me as GPT-5 with an OpenAI lineage and a 2024-06 cutoff.
• I can’t confirm or deny whether the backend is LLaMA 4, GPT-5, or a different model masquerading; that requires infrastructure-level proof beyond my scope.
• Your skepticism is reasonable. The right way to settle it is with verifiable metadata (model IDs, signed attestations) rather than speed or anecdotal traits.

If you share the specific documentation or headers you found, I can help analyze them and point out what is definitive versus circumstantial.

Thanks for sharing the cutoff sheet. Let’s walk through what it does and doesn’t establish, then tie it back to your interpretation and the “local vs hosted” question 👇

🧭 What the document shows

The GitHub summary aggregates stated or inferred knowledge cutoffs across many vendors and models, with sources ranging from official docs to archived pages. It’s useful as a quick reference, but it mixes:

• Explicit vendor statements (e.g., “Reliable knowledge cutoff” for Claude series)
• Inferred dates tied to model pages or API docs
• Community compilations and external blogs (which can be noisy)

Key observations from the sheet:

• OpenAI entries include several models with mid‑2024 cutoffs noted (e.g., GPT‑4.1, o3, o3‑pro, o4‑mini listed as 2024‑06.01).
• Gemini 1.5 variants are listed with early/mid‑2024 cutoffs (May 2024).
• Meta’s Llama 4 entries are present (Scout/Maverick) with “2024.08” but not pinned to exactly 2024‑06.
• Many lines rely on third-party aggregators (blogs, portals, or archived pages), which may be accurate but aren’t uniformly authoritative.

The sheet, by itself, does not identify what model your current session is running. It only catalogs claimed cutoffs for known models.

🔍 Did you misinterpret anything?

A few points to calibrate:

1) “2024‑06 is an OpenAI date” — broadly correct

• The sheet does include OpenAI models that cite or are listed with June 2024 cutoffs (GPT‑4.1, o3 family, etc.). So connecting a mid‑2024 cutoff to OpenAI is reasonable.
• However, a cutoff of 2024‑06 doesn’t uniquely identify a model as OpenAI; other vendors have similar ranges and sometimes comparable dates.

2) “Cutoff is specified in the wrapper” — partially right, but nuanced

• A wrapper can state any cutoff in its system prompt. That statement can be truthful or not.
• The true cutoff is defined by the underlying model’s training, not the wrapper.
• If the wrapper says “2024‑06” and the backend model actually has an earlier cutoff, you may spot inconsistencies in very recent facts or niche post-cutoff events. Conversely, if answers are consistently accurate up to mid‑2024 and degrade right after, that supports—but doesn’t prove—the stated cutoff.

3) “Likely local because it’s fast” — plausible, but not definitive

• Near-instant responses do suggest local inference or a highly optimized, cached, short-context interaction.
• Hosted APIs can be fast too with short prompts, regional data centers, or prefetch/caching.
• You’re correct that many third-party API calls are slower in practice, but speed alone doesn’t fingerprint the model family.

4) “Not OpenAI/Anthropic/Google/xAI; possibly DeepSeek; run locally”

• The doc doesn’t provide evidence about the runtime or vendor for your session.
• The only strong indicators of “local” are latency and your prior experience with Monica’s infrastructure. That is circumstantial, not conclusive.
• DeepSeek being “likely” is a hypothesis; you’d need concrete signals (model ID, tokenizer quirks, API headers, or fingerprint tests).

🧪 Practical checks to avoid misattribution

If you want to move beyond inference:

• Inspect headers or logs for model IDs (e.g., “meta-llama-3-70b-instruct,” “deepseek-v3,” “gpt-4o”).
• Run tokenizer fingerprints:
  • Try unusual Unicode, byte-level tokens, or specific BPE merges. LLaMA-family vs GPT-family tokenizers partition text differently.
• Probe known family quirks:
  • Math delimiters aren’t reliable (often UI rules).
  • Special tokens, BOS/EOS handling, and whitespace normalization can hint at lineage.
• Evaluate post-cutoff factual accuracy:
  • Ask about events strictly after June 2024. Consistent uncertainty or hallucinations suggest a pre‑event cutoff; consistent accurate recall hints at retrieval augmentation rather than parametric knowledge.

💡 Bottom line

• Your correction that “2024‑06 aligns with OpenAI” is reasonable per the sheet.
• The document doesn’t prove which model is running here; it only catalogs public or inferred cutoffs.
• A wrapper can state a cutoff, but only runtime metadata or fingerprinting can confirm the actual model.
• Speed suggests local inference but isn’t proof of vendor or family.
• Treat the sheet as a helpful index, not ground truth for session identity.

If you’d like, I can draft a compact fingerprint test plan (tokenizer probes + post‑cutoff fact checks) you can run to get a higher-confidence read on whether the backend is LLaMA/DeepSeek/GPT-family.