AI Models Directory
The foundation models we deploy in production in 2026 — current variants, real strengths and trade-offs, deployment options, and where each one fits.
Categories
GPT-4o
Multimodal LLM
What is the AI Models Directory?
A curated reference of the foundation AI models we deploy in production for clients — large language models, image generation, and speech recognition. Each entry covers the current model variants in 2026, what each is genuinely best at, deployment options (closed API vs open-weight, on-prem vs cloud), pricing posture, and the trade-offs that matter when picking one.
We do not bet our clients' products on a single vendor. Most production systems we ship route across multiple models — Claude for long-context legal analysis, GPT-5 for general assistants, Llama for on-prem regulated workloads, Whisper for transcription, Flux for marketing imagery. The right answer is almost always a portfolio.
Which AI model should you pick?
A short comparison of the leading LLM families. Most production workloads use two or more.
| Model family | Provider | Best for | Deployment | Context window |
|---|---|---|---|---|
| Claude 4.x | Anthropic | Long-context reasoning, agentic coding, regulated industries | Closed API (Anthropic, AWS, GCP) | 200K – 1M tokens |
| GPT-5 | OpenAI | Broad tooling ecosystem, multimodality, default product LLM | Closed API (OpenAI, Azure) | 128K – 400K+ tokens |
| Gemini 2.5 | Google DeepMind | Long context, video, BigQuery / Workspace integration | Closed API (Google Cloud, Vertex AI) | 1M – 2M tokens |
| Llama 4 | Meta | On-prem, fine-tuning, low TCO at scale, sovereign AI | Open-weight (on-prem or any cloud) | Up to 1M tokens |
LLM models
Claude
Anthropic
Anthropic's Claude family of large language models — built on Constitutional AI for strong reasoning, long-context analysis, agentic tool use, and safety-critical deployments.
Gemini
Google DeepMind
Google DeepMind's Gemini family of multimodal LLMs — natively built for text, image, audio, and video, with the deepest integration into Google Cloud, Workspace, and BigQuery.
GPT-5
OpenAI
OpenAI's GPT-5 family of large language models — the most widely deployed LLMs in the world, with native multimodality, integrated reasoning, and the broadest tooling ecosystem.
Llama
Meta
Meta's Llama family of open-weight large language models — the de facto standard for self-hosted, fine-tunable, on-prem LLM deployments.
Image Generation models
Flux
Black Forest Labs
Black Forest Labs' Flux family — currently the highest-quality image generation model on prompt adherence and photo realism, available as both open-weight and managed API tiers.
Stable Diffusion
Stability AI
The leading open-source image generation model family — full ownership, on-prem deployment, and a massive ecosystem of fine-tunes, ControlNets, and LoRAs.
AI models: frequently asked questions
What is the Clearframe Labs AI Models Directory?
It is a curated reference of the foundation AI models we deploy in production for our clients in 2026 — large language models (Claude, GPT-5, Gemini, Llama), image generation (Stable Diffusion, Flux), and speech recognition (Whisper). Each entry covers current variants, real strengths and trade-offs, deployment options, pricing posture, and the use cases each model is genuinely best at.
What are the leading AI models in 2026?
For LLMs: Claude Opus 4.7 / Sonnet 4.6 / Haiku 4.5 (Anthropic), GPT-5 / GPT-5 mini (OpenAI), Gemini 2.5 Pro / Flash (Google), and Llama 4 Maverick / Scout (Meta, open-weight). For image generation: Stable Diffusion 3.5, Flux 1.1 Pro, Midjourney v7, DALL-E 3 (via GPT-5), and Imagen 4. For speech: Whisper Large-v3-turbo plus commercial ASR vendors like Deepgram and AssemblyAI.
How should I choose between Claude, GPT-5, Gemini, and Llama?
Claude wins on long-document analysis, careful reasoning, and agentic coding — the default in regulated industries. GPT-5 has the broadest tooling ecosystem and is the safest default when you need many capabilities behind one API. Gemini wins when you live in Google Cloud or need 2M-token context and long-video understanding. Llama is the right answer when you need on-prem deployment, fine-tuning on proprietary data, or low total cost of ownership at scale.
Should I use a closed API or open-weight model?
Closed APIs (GPT-5, Claude, Gemini, Flux Pro) win on raw capability and zero infrastructure burden — the right default for most products. Open-weight models (Llama, Mistral, Stable Diffusion, Flux Schnell) win when you need on-prem deployment, fine-tuning on proprietary data, regulatory compliance that prohibits sending data to third parties, or lower unit cost at high volume. Most production systems combine both, routing per task.
What does it cost to run AI models in production?
Closed-API costs are token- or call-based and scale linearly with usage — typical production LLM workloads land between $0.50 and $15 per 1M tokens depending on model tier. Self-hosted open-weight models trade per-call cost for fixed infrastructure (a single H100 runs $2–$4/hour on cloud providers). Crossover usually happens between 100M and 1B tokens per month — below that, closed APIs are cheaper; above that, self-hosted Llama or DeepSeek wins.
How do you help clients deploy these models?
We scope the model selection (closed API vs open-weight, which provider, which variant), design the architecture (RAG, fine-tuning, agent frameworks, evaluation), build the production system, and operate it. Our engineering team has shipped AI systems on every model in this directory, and we route per task rather than betting an entire product on one vendor.