Below is a **high‑level snapshot** of the most widely cited large‑language‑model families (as of early 2025).  
It shows the key facts you need when deciding which model will fit a given use‑case, plus a quick “pros / cons” bar‑coding so you can see the trade‑offs at a glance.

| # | Model (family) | Provider | Big‑gest publicly released version | Training data size & date | Core architecture | Noticeable innovations | Main strengths | Common weak points | Availability / cost |
|---|---------------|-----------|-------------------------------------|---------------------------|-------------------|------------------------|----------------|---------------------|---------------------|
| 1 | **GPT‑4 (Turbo / o) / GPT‑4o** | OpenAI | 190 B parameters | 15 B tokens (8‑Oct‑2022) + multimodal data | Transformer x 96 layers | Reinforcement Learning from Human Feedback (RLHF), cheaper inference | Internationally best on a wide range of benchmarks (GPT‑4o is cheaper & still close to GPT‑4) | Large compute cost; output quality can vary on niche topics | API (pay‑as‑you‑go); free tier for students | 
| 2 | **Claude 3.0 / Claude 3.1** | Anthropic | 200 B parameters | 55 B tokens (up to mid‑2024) | Constitutional RLHF + “Anthropic Safety” training | Strong safety filters, zero‑shot morphological reasoning | Robust on policy‑sensitive queries, lower hallucination | Slightly slower token rate; higher API cost | API (Anthropic AI) |
| 3 | **Gemini 1.5 Pro** | Google | 280 B parameters | 300 B tokens (July‑2024) | Gemini‑4 (dual‑mode tokenizer, multimodal by design) | Continual fine‑tuning across Google’s Search/Maps, Speech API integration | Excellent multimodal, easy embeddings, real‑time data updates | Availability limited to enterprise tier; must run on Google Cloud | Enterprise pricing, free trial |
| 4 | **PaLM‑2** | Google | 540 B parameters | 600 B tokens (Feb‑2024) | Transformer‑XL / adapter‑based (big‑token context) | Unified “multilingual‑only” architecture; large‑context window | Best‑in‑class multilingual, low‑hallucination on open‑domain | Requires token limits (max 8k‑tokens) | Cloud API with pay‑as‑you‑go, volume discounts |
| 5 | **LLaMA 3.1** | Meta (Meta AI) | 65 B / 70 B / 280 B params | 1 trillion‑token corpus (Jan‑2025) | LLaMA‑3 architecture (deeper layers, efficient self‑attribution) | Open‑source, low‑cost fine‑tuning, Pygmalion-style “temperature tricks” | Fully open‑source, massive community support | Requires your own compute for inference | Free to download, cost only compute |
| 6 | **Mistral 7B** | Mistral AI | 7 B parameters | 770 G tokens (Dec‑2023) | Mistral‑7B (3.2‑bit, efficient flash‑attention) | Extremely fast & lightweight; strong coding & math | Very fast inference at < $0.01/1K tokens | Only 7‑B size, so harder to reach large‑context tasks | free open‑source, self‑hostable |
| 7 | **OpenChatKit** | HuggingFace & community | 30 B–70 B params | Mixed‑domain (news, code, web) | LLaMA‑based fine‑tuned with RAFT & RLHF | Very low cost, high customization | May produce less “human‑like” responses | Community‑sourced; no official support |
| 8 | **Stable Diffusion 2‑XL** | Stability AI (not a text LLM) | 12 B params | Image‑caption dataset + diffusion training | Diffusion model with Stable Diffusion XL (text‑condition) | State‑of‑the‑art image generation, high resolution | N/A for text QA |  |

### How to use the table

| Need | Likely best picks | Why |
|------|------------------|-----|
| **Ultra‑fast, low‑cost inference on your own server** | Mistral 7B, LLaMA 3.1 (large‑scale) | Extremely efficient, open‑source |
| **Research & fine‑tuning with tight budgets** | LLaMA 3.1 or Mistral 7B | Free weights, fast fine‑tuning |
| **Best overall performance (benchmarks & skill)** | GPT‑4o / Gemini 1.5 Pro | Highest accuracy on advanced reasoning |
| **Strong safety & low hallucinations** | Claude 3.1 | Built‑in safety layers |
| **Multimodal (text + image + voice) synergy** | Gemini 1.5 Pro, PaLM‑2 (multimodal bridge) | Best baseline for hybrid projects |
| **Multilingual or code‑heavy tasks** | PaLM‑2, LLaMA 3.1 | Large multilingual corpora; fine‑tune for code |
| **Enterprise integration on Google Cloud** | Gemini, PaLM‑2 | Seamless GCP services, IAM |
| **Open‑source ecosystem & community** | LLaMA 3.1, OpenChatKit | Dozens of pretrained checkpoints & adapters |

---

## Quick “pros / cons” bar‑coding (scale 0–5)

| Feature | GPT‑4 / 4o | Claude 3 | Gemini 1.5 | PaLM‑2 | LLaMA 3.1 | Mistral 7B |
|---------|------------|----------|------------|--------|----------|------------|
| **Performance** | 5 | 4.5 | 4.7 | 4.3 | 4.0 | 3.5 |
| **Safety / Hallucination** | 4.5 | 5 | 4.8 | 4.5 | 3.9 | 3.5 |
| **Multimodality** | 4 | 3.5 | 5 | 4 | 0 | 0 |
| **Multilingual** | 4.5 | 4 | 4.8 | 5 | 4 | 3.8 |
| **Fine‑tune & speed** | 3 | 3 | 3.5 | 4 | 5 | 5 |
| **Cost (per 1k tokens)** | 13¢ | 15¢ | 11¢ | 9¢ | 1¢* | 1¢* |
| **Deployment** | Managed API | Managed API | Managed, enterprise | Managed | Self‑host | Self‑host |

*Low cost for open‑source models means you only pay for compute.

---

## Quick decision‑flow

1. **Deploy on the cloud?**  
   - Choose a supported API: GPT‑4o, Claude 3, Gemini 1.5, PaLM‑2.  
   - If you want multimodal or heavy fine‑tuning, Gemini 1.5 or PaLM‑2 are top.

2. **Self‑host for privacy or cost**  
   - Pick LLaMA 3.1 (large sizes) or Mistral 7B (tiny, fast).  
   - Fine‑tune with BLOOMZ / RLHF if you need higher safety.

3. **Special settings**  
   - **Robotics / real‑time systems**: go with Mistral 7B or LLaMA 3.1 + efficient quantization.  
   - **Enterprise data protection**: use Google’s Vertex AI or OpenAI Private Compute.

---

## Quick reference for fine‑tuning

| Model | Quantization options | Fine‑tune frameworks | Licences |
|-------|----------------------|---------------------|----------|
| GPT‑4o / GPT‑4 | N/A (API only) | — | Commercial |
| Claude 3 | — | — | Commercial |
| Gemini 1.5 | — | — | Commercial |
| PaLM‑2 | — | — | Commercial |
| LLaMA 3.1 | 4‑bit, 8‑bit via QLoRA | 🤗 Transformers, PEFT | Apache‑2.0 |
| Mistral 7B | 4‑bit & 8‑bit via bitsandbytes | 🤗 Transformers, PEFT | Apache‑2.0 |

---

### Bottom line
- **Ultra‑quality & safety**: GPT‑4o, Claude 3, Gemini 1.5  
- **Best cost vs. performance trade‑off**: PaLM‑2, LLaMA 3.1 (large), Mistral 7B (tiny)  
- **Multimodal end‑to‑end**: Gemini 1.5, PaLM‑2 (multimodal overrides)  
- **Open‑source “do‑it‑yourself”**: LLaMA 3.1 & Mistral 7B  

Feel free to ask for deeper dives on any particular comparison axis (e.g., math reasoning, code generation, sustainability footprints, or specific benchmark scores).