AI Models & Vendors Landscape (2026)
The market reality. Before you pick IChatClient and start writing code, you need an opinion on which model for which workload — and that requires knowing what's available, what each vendor's strengths are, what they cost, and where they lock you in.
Topics (canonical order)
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Overview — The 2026 Model Market
High-level landscape: frontier closed-source vs open-weights vs edge SLMs
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GPT-4o family, GPT-5, o-series reasoning, embeddings, Whisper, capabilities, pricing tiers
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Claude 4 Opus/Sonnet/Haiku, 1M context, computer use, long-context strengths
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Gemini 2.5 Pro/Flash, native multimodal, 2M context, Vertex AI
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Llama 3.x/4, Mistral, DeepSeek (R1), Qwen, self-hosting trade-offs
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Phi-3/Phi-4 SLMs for edge/on-device, ONNX inference
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Embeddings (text-embedding-3, voyage-3), rerankers (Cohere Rerank), vision (GPT-4V, Claude vision), audio
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Model Selection — Decision Matrix
Which model for which workload — table of criteria
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Token economics, prompt caching, batch APIs, fine-tune vs RAG cost, real $/MTok
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MMLU, HumanEval, SWE-bench, GPQA, MATH — what they measure and don't
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AI Coding Tools — Copilot, Cursor, etc.
GitHub Copilot, Cursor, Windsurf, Cody, Continue, JetBrains AI, Claude Code, Codex CLI
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ChatGPT, Claude.ai, Gemini, Perplexity, agent platforms — buy-vs-build
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.NET SDKs and IChatClient implementations per provider
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Multi-provider strategies, fallback/routing, regional availability, data residency, BAAs
Why this order
Overview → individual vendors (closed → open → SLM) → specialty → decision matrix (synthesis) → pricing (cost lens) → benchmarks (capability lens) → coding tools → agent products → .NET connectivity → lock-in strategy. Each topic informs the next; the decision matrix and pricing topics are the "now what" capstones for the vendor section.
Cross-references
- Connectivity references IChatClient & Pipeline.
- Cost engineering references Telemetry & Caching.
- Phi/SLM references ONNX Runtime GenAI.