The AI landscape changes too quickly for a static list of model numbers. This module gives you a current map of the leading labs, how their model families fit together, and a durable way to choose between them.
Current snapshot — verified 26 July 2026
Model names, access tiers, and preview status can change within weeks. Use this page as a map, then check the linked official model index before making a purchasing or architecture decision.
The Major Players
No single ranking captures the whole market. These selected labs are influential across frontier capability, open-weight releases, developer adoption, and product reach.
OpenAI
Hosted / API
GPT-5.6 Sol · GPT-5.6 Terra · GPT-5.6 Luna
The GPT-5.6 family now uses named tiers: Sol is the flagship for the hardest work, Terra balances capability and throughput, and Luna is the efficient tier for speed-sensitive workloads.
Key strength:Agentic coding, long-context knowledge work, multimodality, and a broad product ecosystem.
Claude Fable 5 · Mythos 5 (limited) · Opus 5 · Sonnet 5 · Haiku 4.5
Anthropic has expanded Claude beyond the familiar Opus, Sonnet, and Haiku ladder. Fable 5 is its top broadly available model, while Mythos 5 is a restricted research deployment.
Key strength:Long-running agents, coding and analysis, safety research, and the Model Context Protocol ecosystem.
Gemini spans experimental Pro models, high-capability Flash models, and lower-cost Flash-Lite models. The family is multimodal and tightly connected to Google products and developer tooling.
Key strength:Multimodal systems, scientific research, broad infrastructure, and on-device deployment.
Kimi K3 is Moonshot's 2.8-trillion-parameter flagship for long-horizon coding, knowledge work, and reasoning. It supports native vision and up to a one-million-token context window.
Key strength:Long-context agentic work, coding, native vision, and a fast-moving open-model programme.
GLM-5.2 is Z.ai's current flagship for long-horizon agent tasks. It combines a one-million-token context window with an MIT-licensed open-weight release.
Key strength:Open frontier models, agentic coding, long context, and multilingual capability.
Mistral Medium 3.5 · Small 4 · Large 3 · Devstral 2
Mistral combines efficient general-purpose models with specialised coding and reasoning families. Its catalogue mixes downloadable models with managed commercial offerings.
Key strength:Efficient architectures, multilingual performance, specialised models, and European deployment options.
Meta's Llama family remains a major foundation for self-hosted and community-built AI. Llama 4 introduced natively multimodal mixture-of-experts models.
Key strength:Open-weight distribution, research, community adoption, and a broad deployment ecosystem.
Model names differ by vendor, but most families still serve four practical needs. Choose the least expensive tier that reliably completes your task, then evaluate with your own examples.
A newer or more expensive model is not automatically better for your work. Compare quality, latency, cost, privacy, tool use, and failure rate on a small evaluation set drawn from real tasks.
Open-Weight vs. Hosted Models
"Open source" is often used loosely in AI. A more precise distinction is whether you can download the model weights or must access the model through a provider.
Open-weight models
Llama 4, GLM-5.2, Mistral models, Qwen, and Kimi K3 (full weights announced for 27 July 2026)
Can be self-hosted when hardware and licence permit
Can be fine-tuned and adapted to specialised tasks
Offers more control over data location and deployment
Requires infrastructure, security, and model operations
Usage rights vary significantly by licence
Hosted / API models
GPT-5.6, Claude 5, Gemini 3, and Grok 4.5
Managed infrastructure with a faster path to production
Provider-managed updates, scaling, and safety systems
Often includes proprietary tools and product integrations
Creates dependency on provider pricing and availability
Requires careful review of data handling and retention
Read the licence, not the label
Open weights do not automatically grant unrestricted commercial use, reveal the training data, or make a model inexpensive to operate. Check the licence, hosting cost, and governance needs separately.
The AI Ecosystem Beyond Models
Foundation models are one layer in a much larger stack. Real-world systems also depend on compute, orchestration, evaluation, data infrastructure, and user-facing applications.
Compute / CloudNVIDIA, AWS, Azure, Google Cloud, CoreWeave, Lambda
Agent & Dev ToolsOpenAI Codex, Claude Code, Kimi Code, Cursor, Vercel AI SDK, LangChain