AI Models Guide
Side-by-side look at the major AI models — Claude Opus 4.8, 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 June 9, 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 4.8, 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 4.8 (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
| Model ↑ | |
|---|---|
| Claude Fable 5 Anthropic's most capable widely released model — built for the most demanding reasoning and long-horizon agentic work. Text GenerationAdvanced ReasoningCode Generation | |
| Claude Haiku 4.5 Anthropic's small, fast, cheap model — the right default for background agents and high-volume jobs. Text GenerationAdvanced ReasoningCode Generation | |
| Claude Opus 4.8 Anthropic's most capable Opus-tier model for complex reasoning, agentic coding, and high-autonomy work. Text GenerationAdvanced ReasoningCode Generation | |
| Claude Sonnet 4.6 Anthropic's mainstream workhorse — the default for Claude.ai, API workloads, and Claude Code day-to-day. Text GenerationAdvanced ReasoningCode Generation | |
| DeepSeek V3 DeepSeek's flagship open-weights MoE model. Chosen when price and open weights matter more than vendor reputation. Text GenerationCode GenerationAdvanced Reasoning | |
| Gemini 2.5 Flash Gemini's low-cost tier. Strong choice for high-volume, long-context workloads where Flash quality is good enough. Text GenerationMultimodalAdvanced Reasoning | |
| 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. Text GenerationMultimodalAdvanced Reasoning | |
| GPT-4o OpenAI's omni model — good multimodal default when latency and cost matter more than absolute reasoning quality. Text GenerationMultimodalAdvanced Reasoning | |
| GPT-4o mini OpenAI's small, cheap multimodal sibling of GPT-4o — strong default for high-volume tasks where latency and cost dominate. Text GenerationMultimodalCode Generation | |
| GPT-5 OpenAI's current flagship model. Check openai.com for up-to-date capability and pricing details before production use. Text GenerationMultimodalAdvanced Reasoning | |
| Grok 3 xAI's flagship. Relevant mainly if you need real-time X data or a less filtered default tone. Text GenerationAdvanced Reasoning | |
| Llama 3.3 70B Meta's open-weight workhorse — the default choice when you need an open model you can host, fine-tune, or air-gap. Text GenerationCode GenerationAdvanced Reasoning | |
| Mistral Large 2 Mistral's flagship. Common pick for EU customers that want non-US-hosted inference for GDPR and sovereignty reasons. Text GenerationCode GenerationAdvanced Reasoning |
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
AI Model Market Share
Snapshot: May 2026 • Source data: 2026-05-01Directional share-of-usage estimates blended from three incommensurate developer-usage signals (OpenRouter API calls, Hugging Face downloads, Stack Overflow self-reports). Read as a normalized index of developer-facing usage among the tracked models in this snapshot, not a precise market-share figure - see methodology below. Click any column header to sort.
| GPT-4o | OpenAI | 26% | -2pp |
| GPT-4o mini | OpenAI | 18% | -1pp |
| Claude Sonnet 4.6 | Anthropic | 14% | +3pp |
| Gemini 2.5 Flash | 12% | +2pp | |
| Llama 3.3 70B | Meta | 10% | +1pp |
| GPT-5 | OpenAI | 6% | +3pp |
| Claude Opus 4.7 | Anthropic | 5% | +2pp |
| Gemini 2.5 Pro | 4% | +1pp | |
| DeepSeek V3 | DeepSeek | 3% | 0pp |
| Claude Haiku 4.5 | Anthropic | 2% | 0pp |
Methodology
Share figures are directional estimates blended from three publicly available developer-usage signals that measure different things: OpenRouter's public router leaderboard tracks API call volumes, Hugging Face counts downloads of open-weight checkpoints, and the Stack Overflow 2025 Developer Survey records self-reported usage. Because these axes are incommensurate (an API call is not a download is not a survey response), the resulting figure is best read as a normalized index of developer-facing usage rather than a precise market-share percentage. Coverage is restricted to the 10 tracked models in this snapshot; long-tail and niche models, private self-hosted enterprise deployments, and consumer-app usage without an API equivalent are not included. Numbers are rounded to whole percentages; the visible sum approximates 100% of tracked usage rather than 100% of the broader AI model market.
Sources: OpenRouter public router leaderboard (proxied API call volumes) | Hugging Face open-weight checkpoint downloads | Stack Overflow 2025 Developer Survey (self-reported usage) | Trend months: Dec 2025, Jan 2026, Feb 2026, Mar 2026, Apr 2026, May 2026
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 4.8 (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.