Prompt Engineering Tutorial and Prompt PracticeModels
Model Collection
Model overview and selection guide
CHAPTER PROMPT DECISION
01Prompt problem
Model overview and selection guide
02Reviewable output
A reusable prompt, example set, evaluation record or safety rule with its task boundary preserved.
03Definition of done
Run at least one representative case, inspect the result and record what still requires human review.
TL;DR
- This page is a foundational LLM index: it helps you quickly build a mental map of "which models exist and what category they fall into," covering GPT-5.1 / GPT-4.1 / o1, Claude 4.5, Gemini 3, Llama 3.1, Grok-2, and more.
- Don't pick models based on parameters/leaderboards alone: what matters more is latency, cost, context length, tool support (e.g., Tool Calling), and your own evaluation results.
- In real projects, you'll typically use a "capability model + fast model" combo: a stronger model for complex planning/reasoning, a faster and cheaper model for routine steps and batch processing.
2024-2025 Quick Reference
| Vendor | Capability Tier (multimodal/tools) | Fast/Cheap Tier | Notes |
|---|---|---|---|
| OpenAI | ChatGPT 5.1 (GPT-5 series) / GPT-4.1 | GPT-4o-mini | Great vision/tools; Mini for batch/automation |
| OpenAI (reasoning) | o1 / o1-mini | -- | Stronger long-chain reasoning/planning, higher cost |
| Anthropic | Claude 4.5 Sonnet | Claude 3.5/4.5 Haiku | Strong long doc/table capabilities, high safety |
| Gemini 3 Pro | Gemini 3 Flash / Flash-Lite | 1M tokens context, multimodal/tools; Flash series is fast | |
| Meta (open-source) | Llama 3/3.1 70B | Llama 3.1 8B | Good for private deployment, rich ecosystem |
| Mistral | Mistral Large | Mistral Small | Cost-effective, strong multilingual |
| xAI | Grok-2 | Grok-2 mini | For time-sensitive/web-connected scenarios |
Selection advice: start with "small model to get it working -> big model to improve quality -> eval regression." For multimodal/screenshots, prefer ChatGPT 5.1 / GPT-4o / Gemini 3; for long documents/tables, prefer Claude 4.5 or Gemini 3 Pro.
Last updated: 2025-02
Reading Guide
This list leans toward "foundational + notable" historical context. It doesn't cover every latest model or version. Use it for:
- Backtracking: when a paper/blog mentions a model, quickly locate its origin and era
- Selection: build your candidate set before running evaluation
For engineering selection, answer at least these questions:
- Is the task more like chat, coding, reasoning, or RAG?
- Do you need long context? How long? (context length)
- Do you need Tool Calling? Structured output (JSON schema)?
- Can you run a small evaluation set for regression (10-50 samples is enough to start)?
Data adopted from Papers with Code and Zhao et al. (2023).
Models
| Model | Release Date | Description |
|---|---|---|
| BERT | 2018 | Bidirectional Encoder Representations from Transformers |
| GPT | 2018 | Improving Language Understanding by Generative Pre-Training |
| RoBERTa | 2019 | A Robustly Optimized BERT Pretraining Approach |
| GPT-2 | 2019 | Language Models are Unsupervised Multitask Learners |
| T5 | 2019 | Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer |
| BART | 2019 | Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension |
| ALBERT | 2019 | A Lite BERT for Self-supervised Learning of Language Representations |
| XLNet | 2019 | Generalized Autoregressive Pretraining for Language Understanding and Generation |
| CTRL | 2019 | CTRL: A Conditional Transformer Language Model for Controllable Generation |
| ERNIE | 2019 | ERNIE: Enhanced Representation through Knowledge Integration |
| GShard | 2020 | GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding |
| GPT-3 | 2020 | Language Models are Few-Shot Learners |
| LaMDA | 2021 | LaMDA: Language Models for Dialog Applications |
| PanGu-α | 2021 | PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation |
| mT5 | 2021 | mT5: A massively multilingual pre-trained text-to-text transformer |
| CPM-2 | 2021 | CPM-2: Large-scale Cost-effective Pre-trained Language Models |
| T0 | 2021 | Multitask Prompted Training Enables Zero-Shot Task Generalization |
| HyperCLOVA | 2021 | What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers |
| Codex | 2021 | Evaluating Large Language Models Trained on Code |
| ERNIE 3.0 | 2021 | ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation |
| Jurassic-1 | 2021 | Jurassic-1: Technical Details and Evaluation |
| FLAN | 2021 | Finetuned Language Models Are Zero-Shot Learners |
| MT-NLG | 2021 | Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model |
| Yuan 1.0 | 2021 | Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning |
| WebGPT | 2021 | WebGPT: Browser-assisted question-answering with human feedback |
| Gopher | 2021 | Scaling Language Models: Methods, Analysis & Insights from Training Gopher |
| ERNIE 3.0 Titan | 2021 | ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation |
| GLaM | 2021 | GLaM: Efficient Scaling of Language Models with Mixture-of-Experts |
| InstructGPT | 2022 | Training language models to follow instructions with human feedback |
| GPT-NeoX-20B | 2022 | GPT-NeoX-20B: An Open-Source Autoregressive Language Model |
| AlphaCode | 2022 | Competition-Level Code Generation with AlphaCode |
| CodeGen | 2022 | CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis |
| Chinchilla | 2022 | Shows that for a compute budget, the best performances are not achieved by the largest models but by smaller models trained on more data. |
| Tk-Instruct | 2022 | Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks |
| UL2 | 2022 | UL2: Unifying Language Learning Paradigms |
| PaLM | 2022 | PaLM: Scaling Language Modeling with Pathways |
| OPT | 2022 | OPT: Open Pre-trained Transformer Language Models |
| BLOOM | 2022 | BLOOM: A 176B-Parameter Open-Access Multilingual Language Model |
| GLM-130B | 2022 | GLM-130B: An Open Bilingual Pre-trained Model |
| AlexaTM | 2022 | AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model |
| Flan-T5 | 2022 | Scaling Instruction-Finetuned Language Models |
| Sparrow | 2022 | Improving alignment of dialogue agents via targeted human judgements |
| U-PaLM | 2022 | Transcending Scaling Laws with 0.1% Extra Compute |
| mT0 | 2022 | Crosslingual Generalization through Multitask Finetuning |
| Galactica | 2022 | Galactica: A Large Language Model for Science |
| OPT-IML | 2022 | OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization |
| LLaMA | 2023 | LLaMA: Open and Efficient Foundation Language Models |
| GPT-4 | 2023 | GPT-4 Technical Report |
| PanGu-Σ | 2023 | PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing |
| BloombergGPT | 2023 | BloombergGPT: A Large Language Model for Finance |
| PaLM 2 | 2023 | A Language Model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. |
| Claude 2 | 2023 | Anthropic’s second-gen assistant, improved writing/code quality and safety |
| Llama 2 | 2023 | Open-weight chat models (7B-70B) widely used for private deployment |
| Mixtral 8x7B | 2023 | Sparse Mixture-of-Experts open-source model, great cost-effectiveness |
| Gemini 1.0 | 2023 | Google's multimodal model (Ultra/Pro/Nano), first release of the Gemini series |
| Claude 3 (Opus/Sonnet/Haiku) | 2024 | Next-gen multimodal models, strong long doc/table extraction and safety |
| Gemini 1.5 Pro | 2024 | Up to 1M+ tokens long context, multimodal |
| Gemini 1.5 Flash | 2024 | Cheap and fast multimodal model, good for batch and real-time interaction |
| Mistral Large | 2024 | Multilingual large model, supports function calling and long context |
| Grok-1.5 | 2024 | xAI's long context model, focused on real-time use |
| GPT-4o | 2024 | OpenAI's omni-modal flagship, faster than GPT-4, supports voice/image/video |
| GPT-4o mini | 2024 | Low-cost small model with strong tool capabilities |
| Llama 3 | 2024 | 8B/70B open-source, strong in English and multilingual performance |
| Llama 3.1 | 2024 | 8B/70B/405B, upgraded reasoning and 128k context |
| Claude 3.5 Sonnet | 2024 | Claude 3.5 primary model, strong at code and tool calling |
| Claude 3.5 Haiku | 2024 | Lightweight fast variant, retains high safety and multimodal capabilities |
| o1 | 2024 | OpenAI reasoning model, focused on chain-of-thought and planning |
| o1-mini | 2024 | Cheaper/faster version of o1 |
| GPT-4.1 | 2024 | “All-in-one” model, unifies text/image/voice, upgraded reasoning and tool calling |
| Grok-2 | 2024 | xAI next-gen model, improved code and web-connected QA |
| Gemini 2.0 Flash (Exp) | 2024 | Gemini 2.0 preview for real-time/tool scenarios |
| Gemini 2.0 Pro (Exp) | 2024 | Gemini 2.0 preview flagship, multimodal and long context |
| DeepSeek-R1 | 2025 | Open-source model focused on reasoning efficiency, supports long context and math/code |
| Claude 4.5 Sonnet | 2025 | Anthropic 2025 flagship, long context (200K-1M beta), stronger code and table retrieval |
| ChatGPT 5.1 (GPT-5 series) | 2025 | Latest OpenAI product for Responses API, controllable reasoning depth and multimodal |
| Gemini 3 Pro | 2025 | Google's ultra-long context flagship (1,048,576 tokens), multimodal + tool chain optimization |
| Gemini 3 Flash / Flash-Lite | 2025 | Fast/low-cost multimodal models, good for in-product real-time interaction and batch processing |