AI Models Guide
Side-by-side look at the major AI models — Claude Opus 4.7, Sonnet 4.6, Haiku 4.5, GPT-5, GPT-4o, Gemini 2.5 Pro, Llama 3.3, Mistral Large, DeepSeek V3 — with context windows, pricing, and what each is best at.
Pricing verified May 7, 2026 · source
Information Accuracy Notice
This guide contains verified information about current AI models. Some specifications (parameters, benchmarks, context windows) are marked as "Unknown" when we cannot verify the accuracy from official sources. We prioritize accuracy over completeness and update information as it becomes publicly available.
AI Model Types and Architectures
AI models are built upon a variety of architectures, each suited to distinct tasks and applications. Here's a comprehensive breakdown of the major types and leading models available today.
By Learning Approach
Supervised Learning Models
Trained with labeled data for specific tasks
- • Speech recognition
- • Text classification
- • Fraud detection
- • Regression analysis
- • KNN, K-means, Random Forest
Unsupervised Learning Models
Discover patterns in unlabeled data
- • Trend analysis
- • Clustering algorithms
- • Traffic pattern recognition
- • Anomaly detection
- • Dimensionality reduction
Reinforcement Learning Models
Learn by trial-and-error, goal-oriented
- • Robotics control
- • Stock trading strategies
- • Gaming AI
- • Autonomous systems
- • Resource optimization
By Model Architecture
| Category | Key Models & Architectures | Main Applications |
|---|---|---|
| Rule-Based Systems | Static decision trees, Expert systems | Simple chatbots, automation, business rules |
| Machine Learning | Linear/Logistic Regression, Decision Trees, Random Forest | Spam filters, prediction, classification, recommendation systems |
| Deep Learning | CNNs, RNNs, LSTMs, GRUs | Image recognition, time series, language modeling, speech processing |
| Transformer Models | BERT, GPT, T5, RoBERTa | NLP, text generation, translation, question answering |
| Generative Models | GANs, VAEs, Diffusion, Stable Diffusion | Synthetic data/images, video synthesis, 3D scene creation |
| Large Language Models | Claude Opus 4.7, Claude Sonnet 4.6, GPT-5, Gemini 2.5 Pro, Llama 3.3 | Chatbots, research, text generation, code generation |
| Multimodal Models | GPT-4o, Gemini 2.5 Pro, Claude Sonnet 4.6 | Text + images + audio, cross-modal understanding, content creation |
| 3D Generation Models | NeRFs, Stable Virtual Camera, Luma AI | 3D environments from images, virtual reality, gaming assets |
Notable Flagship AI Models
Text & Multimodal
- Claude Opus 4.7 (Anthropic): Agentic coding + long-horizon reasoning, 1M context
- Claude Sonnet 4.6 (Anthropic): Best price-performance for day-to-day, 1M context
- GPT-5 (OpenAI): Flagship multimodal model
- Gemini 2.5 Pro (Google): 1M+ token context window
Specialized & Open Source
- Llama 3.3 70B (Meta): Open weights, 128K context
- Mistral Large 2: EU-hosted option for data residency
- DeepSeek V3: Open-weights MoE with strong coding performance
- Claude Haiku 4.5 (Anthropic): Fast, cheap, still strong on extraction
Key Takeaways
- • AI models range from classic ML approaches to cutting-edge deep learning architectures
- • Large Language Models and multimodal models dominate current innovation
- • Generative models enable rich creation of synthetic data, images, and videos
- • Transformer-based models power most language and content generation tasks
- • Open-source projects are democratizing access to cutting-edge capabilities
- • Model selection depends on the specific task requirements and constraints
Claude Haiku 4.5
Anthropic
Anthropic's small, fast, cheap model — the right default for background agents and high-volume jobs.
Benchmark Scores
Key Features
- •Fastest Claude model at the lowest price point
- •Good for high-volume classification and extraction
- •Vision input support
Claude Opus 4.7
Anthropic
Anthropic's flagship model for long-horizon agentic work, complex coding, and research-grade analysis.
Benchmark Scores
Key Features
- •Anthropic's most capable model for complex reasoning
- •Strong performance on agentic coding tasks
- •Adaptive thinking mode for deliberate reasoning
Claude Sonnet 4.6
Anthropic
Anthropic's mainstream workhorse — the default for Claude.ai, API workloads, and Claude Code day-to-day.
Benchmark Scores
Key Features
- •Balanced speed, cost, and capability
- •Default model for most Claude Code workflows
- •Strong coding and tool-use performance
DeepSeek V3
DeepSeek
DeepSeek's flagship open-weights MoE model. Chosen when price and open weights matter more than vendor reputation.
Benchmark Scores
Key Features
- •Open weights
- •Mixture-of-Experts — large total / small active
- •Very competitive on coding and math benchmarks
Gemini 2.5 Flash
Gemini's low-cost tier. Strong choice for high-volume, long-context workloads where Flash quality is good enough.
Benchmark Scores
Key Features
- •Cheap and fast sibling of Gemini 2.5 Pro
- •Long context window
- •Good price-performance for high-volume tasks
Gemini 2.5 Pro
Google's Gemini Pro line — the go-to when you need to stuff a whole codebase or long video into a single prompt.
Benchmark Scores
Key Features
- •Very long context window (1M+ tokens)
- •Native multimodal: text, image, audio, video
- •Strong performance on long-document and codebase tasks
GPT-4o
OpenAI
OpenAI's omni model — good multimodal default when latency and cost matter more than absolute reasoning quality.
Benchmark Scores
Key Features
- •Omni-modal: text, vision, and audio in one model
- •Realtime audio via the Realtime API
- •Cheaper and faster than GPT-4
GPT-4o mini
OpenAI
OpenAI's small, cheap multimodal sibling of GPT-4o — strong default for high-volume tasks where latency and cost dominate.
Benchmark Scores
Key Features
- •Lowest-cost multimodal OpenAI model
- •Vision input support
- •Function calling and structured outputs
GPT-5
OpenAI
OpenAI's current flagship model. Check openai.com for up-to-date capability and pricing details before production use.
Benchmark Scores
Key Features
- •OpenAI's current flagship
- •Multimodal (text, image, audio)
- •Strong reasoning and coding performance
Grok 3
xAI
xAI's flagship. Relevant mainly if you need real-time X data or a less filtered default tone.
Benchmark Scores
Key Features
- •Access to real-time X (Twitter) data
- •Less restrictive content policy than peers
- •Reasoning mode available
Llama 3.3 70B
Meta
Meta's open-weight workhorse — the default choice when you need an open model you can host, fine-tune, or air-gap.
Benchmark Scores
Key Features
- •Open weights — run on your own hardware
- •Claimed performance close to Llama 3.1 405B
- •128K context window
Mistral Large 2
Mistral AI
Mistral's flagship. Common pick for EU customers that want non-US-hosted inference for GDPR and sovereignty reasons.
Benchmark Scores
Key Features
- •European (France) model — data residency option
- •Strong multilingual and code performance
- •Function calling and JSON mode
Current AI Model Landscape
Key Insights
What's changed
- • Claude, GPT, and Gemini families all now ship tiered lineups (flagship + mid + small)
- • Long context windows (200K–1M+ tokens) are now table stakes on flagship models
- • Multimodal (text + vision, and sometimes audio/video) is baseline, not a premium feature
- • Agentic tool use + computer use is pushing model choice toward Claude for coding workflows
- • Reasoning/thinking modes are a separate purchase decision from raw model size
Cost efficiency
- • Open-weight models (Llama 3.3, DeepSeek V3) are close to proprietary on many tasks
- • Mid-tier models (Sonnet, GPT-4o, Gemini Flash) handle 80%+ of real workloads
- • Small models (Haiku, Gemini Flash Lite) shine in high-volume pipelines
- • Prompt caching and batch APIs materially cut cost on repeated-context workloads
Frequently Asked Questions
What are supervised learning models?
Supervised learning models are trained with labeled data for specific tasks. They are used for speech recognition, text classification, fraud detection, regression analysis, and include algorithms like KNN, K-means, and Random Forest.
What are unsupervised learning models?
Unsupervised learning models discover patterns in unlabeled data. They are used for trend analysis, clustering algorithms, traffic pattern recognition, anomaly detection, and dimensionality reduction.
What are reinforcement learning models?
Reinforcement learning models learn by trial-and-error and are goal-oriented. They are used in robotics control, stock trading strategies, gaming AI, autonomous systems, and resource optimization.
What are the notable flagship text and multimodal AI models?
Claude Opus 4.7 (Anthropic) for agentic coding and long-horizon reasoning with a 1M context window; Claude Sonnet 4.6 (Anthropic) for the best price-performance day-to-day with a 1M context window; GPT-5 (OpenAI) as a flagship multimodal model; and Gemini 2.5 Pro (Google) with a 1M+ token context window.
What are the notable specialized and open-source AI models?
Llama 3.3 70B (Meta) with open weights and a 128K context window; Mistral Large 2 as an EU-hosted option for data residency; DeepSeek V3, an open-weights MoE with strong coding performance; and Claude Haiku 4.5 (Anthropic), which is fast, cheap, and still strong on extraction.
What's changed in the AI model landscape?
Claude, GPT, and Gemini families all now ship tiered lineups (flagship, mid, and small). Long context windows (200K–1M+ tokens) are now table stakes on flagship models. Multimodal (text plus vision, and sometimes audio/video) is baseline, not a premium feature. Agentic tool use and computer use is pushing model choice toward Claude for coding workflows. Reasoning and thinking modes are a separate purchase decision from raw model size.
How do AI models compare on cost efficiency?
Open-weight models (Llama 3.3, DeepSeek V3) are close to proprietary on many tasks. Mid-tier models (Sonnet, GPT-4o, Gemini Flash) handle 80%+ of real workloads. Small models (Haiku, Gemini Flash Lite) shine in high-volume pipelines. Prompt caching and batch APIs materially cut cost on repeated-context workloads.