The AI Model Landscape: Choosing Among Claude, ChatGPT, Gemini & Open Models
A durable framework for picking the right AI model for a task — across Claude, ChatGPT, Gemini, Llama, Mistral, Grok and the open/local models — plus what skills transfer across all of them.
Claude Fable 5 & Mythos 5: The Flagship Field Guide
Fable 5 is Anthropic's most capable model. Its API refuses in-band (HTTP 200, stop_reason: refusal), has adaptive thinking only, and never returns raw thinking. If you're pinning a top-tier model, this changes how you write integrations. When to use it vs Opus 4.8, the fallback pattern, the prompting shifts.
Claude Opus 5: The Field Guide
Opus 5 shipped 24 July 2026 as Anthropic's fourth model in two months — same $5/$25 pricing as Opus 4.8 but more than double the Frontier-Bench score, 3× ARC-AGI-3 vs the next-best model, and a new xhigh/max effort tier. The benchmarks that matter, the two 400-errors that break naive migrations, and when Opus 5 replaces Fable 5 in your stack.
Claude Sonnet 5: The Field Guide
Sonnet 5 is Claude Code's new default and Sonnet 4.6's drop-in successor — but three API constraints will 400 you on migration and a new tokenizer produces ~30% more tokens for the same text. The pricing math, the effort re-mapping (Sonnet 5 medium ≈ Sonnet 4.6 high), and the prompting shifts that catch teams out.
ChatGPT for Claude Users
Fluent in Claude, need to use ChatGPT? The differences that matter, what transfers, and when to reach for which.
GPT-5.6 & ChatGPT Work for Claude Users
OpenAI shipped the GPT-5.6 family (Sol, Terra, Luna) and ChatGPT Work on 9 July 2026. What actually changed for a Claude user — 1.05M context, a $1 tier, an agent that runs for hours, and the parts of the launch nobody is writing about.
OpenAI Astra: The Preview Field Note
OpenAI's 'next major model' Astra was previewed on 1 August 2026 with ten Lean-verified math proofs and almost no product specs. What is actually confirmed, what is still unknown, and whether a Claude user should wait — the honest, sourced take.
Sign in with ChatGPT
OpenAI's new identity layer, three days old. What data actually crosses the line, what enterprise admins can do about it, and how to accept it in your app without giving OpenAI a foothold you didn't intend.
Run AI Models Locally with Ollama
Run open-weight models (Llama, Mistral, Qwen…) on your own machine — private, offline, free to run — with Ollama. Setup, CLI, and calling it from code.
Claude vs GPT vs Gemini for Coding
A framework (not a leaderboard) for choosing a frontier model for coding — the factors that matter, and why an eval on your own repo beats every benchmark.
Coding Agent CLIs Compared
Claude Code, Codex CLI, Gemini CLI and the open-source terminal agents — how the harness (not just the model) decides your results, and how to keep your setup portable with AGENTS.md.
Supabase Evals — Real-Backend Agent Benchmark
Supabase open-sourced a benchmark that runs Claude Code, Codex and OpenCode against real containerized Supabase stacks — not mocks. Here's what it measures, what it found about how models actually use docs and skills, and how to run it locally in an evening.
Gemini for Claude Users
Fluent in Claude, need Google's Gemini? The differences that matter — Gems, Workspace integration, huge context, multimodal — and what transfers.
Gemini 3.6 Flash, Flash-Lite & Flash Cyber for Claude Users
On 21 July 2026 Google skipped Gemini 3.5 Pro entirely and shipped three Flash-tier models instead. Real prices, real benchmarks, the migration gotchas, and the parts most write-ups miss — including a cybersecurity fine-tune that beat Claude Opus 4.6 at V8 bug-hunting.
Grok for Claude Users
Fluent in Claude, need xAI's Grok? The differences that matter — real-time X/web search, an OpenAI-compatible API, and the Grok Build coding agent — plus everything that transfers.
Muse Code + Muse Spark 1.2: Meta's Terminal Coding Agent
On 5 August 2026 Meta shipped Muse Code, a terminal coding agent, and Muse Spark 1.2, the model co-trained with it. What the append-only event log actually buys you, why the contributor tier is 12.5× cheaper than standard, how it stacks up against Claude Code on Terminal-Bench 2.1, and the three bundled skills that are worth stealing regardless of which agent you use.
Open-Source AI Agent Frameworks
A provider-neutral map of the main open frameworks for building LLM agents — LangGraph, LlamaIndex, AutoGen, CrewAI and the minimal-loop approach — and how to choose.
Native Multi-Agent APIs: OpenAI's Responses Multi-Agent Beta vs Building It Yourself
On 9 July 2026 OpenAI shipped the Responses API multi-agent beta and Sol Ultra Mode — the first frontier vendor to make 'the model spawns and synthesises N subagents in one HTTP request' a first-class primitive. What actually ships, the API shape, the cost math, what Anthropic does instead, and when the native primitive beats a DIY fan-out.
A2A: The Agent-to-Agent Protocol
MCP connects agents to tools. A2A connects agents to other agents — across frameworks, clouds, and companies. Learn the Agent Card, tasks, the eight lifecycle states, streaming vs. webhooks, and how A2A composes with MCP.
Porting Prompts Across Models
Move a prompt between Claude, GPT, Gemini and open models — what transfers cleanly, what to adjust per model, and a migration workflow.
DeepSeek, Qwen & the Open-Weight Wave
Strong open-weight models (DeepSeek, Qwen, Llama, Mistral) closed much of the gap. What 'open weight' means, the trade-offs vs closed frontier, and how to use them.
Kimi K2 for Claude Users
Moonshot's Kimi K2 is an open-weight, trillion-parameter agent built for long tool-use chains. What actually makes it different from Claude — native INT4 weights, 200–300 sequential tool calls, a permissive license — and when it's worth reaching for.
GLM-5.2: Open-Weight Frontier Coding Model
Z.ai's 753B MoE (~40B active), 1M-token context, MIT-licensed — closes to within a point of Claude Opus 4.8 on long-horizon coding, at roughly one-sixth the API cost. What IndexShare actually does, how to run it locally, and where it beats Claude Code in the wild.
Inkling: Thinking Machines' Open-Weights Model
Mira Murati's Thinking Machines Lab shipped Inkling — a 975B-parameter Apache 2.0 multimodal MoE with a continuous thinking-effort dial. What the launch coverage got wrong, why it deliberately loses benchmarks, and what the effort knob actually does.
Kimi K3: World's Largest Open-Weight Model
Moonshot AI's 2.8-trillion-parameter open-weight model beats Claude Opus 4.8 on many benchmarks and tops the Frontend Code Arena. What makes it architecturally different, when the cache-hit pricing flips the cost math, and whether it's worth reaching for.
Running Kimi K3 Locally: vLLM, DSpark & the Real Hardware Bill
The definitive practical guide to self-hosting Moonshot's 2.8T open-weight Kimi K3 in 2026. What DSpark speculative decoding actually does, minimum hardware (1× DGX B300 or 16× DGX Spark), the exact vLLM serve commands, the prefill/decode asymmetry nobody warns you about, and when hosted inference undercuts your own rack.
Inside Grok Build: Reading an Open Agent Harness
xAI published the full Rust source of its coding agent under Apache 2.0 — the agent loop, tool layer, TUI and extension system. What the crate map teaches you about building agents, why 'open source' here means source-available, and what the data-collector code still in the tree tells you.
Poolside Laguna: Open-Weight Coding Models That Punch Above Their Class
Poolside AI's Laguna family (XS 2.1, S 2.1, M.1) — MoE coding models with 3B–23B active params, 1M-token context, OpenMDW-1.1 license. What the 8B-active S 2.1 actually beats, why the 3B-active XS 2.1 runs on a MacBook, the first-known RL-in-FP8 training pipeline, and when to reach for it over Claude, GLM-5.2 or Kimi K3.
Generative Media: Image, Audio & Video AI
Beyond text chat — a map of AI for images, video, voice and music: the major tools, the durable skills, and the rights/ethics that matter.
What AI Actually Costs (Across Providers)
A durable framework for comparing AI cost across providers — the pricing archetypes, how to estimate a workload, the levers to cut the bill, and when open/local wins.
Why AI Agents Burn Tokens (and How to Cap the Bill)
Agentic runs cost 50–1000x a chat — not because the model is dumber, but because of the context snowball. The mechanism, the surprising numbers, and the four levers that actually cut the bill across Claude, GPT, Gemini, and open models.
Building Local AI Agents
Autonomous agents that run entirely on your machine with a local open-weight model — private, offline, free to run. The architecture, the trade-offs, and how to start.
Claude + Local Models: Hybrid Patterns
Make Claude and local open-weight models work in synergy — router, draft-then-refine, privacy redaction, bulk pre-processing — for the reasoning of frontier with the privacy and cost of local.
AI Gateways: LiteLLM, OpenRouter, Portkey, Vercel
A production AI gateway is the missing router between your app and every model — Claude, GPT, Gemini, Llama. Compare LiteLLM, OpenRouter, Portkey, and Vercel AI Gateway, then wire Claude Code through your own proxy.
Local Coding Agents (and How They Pair with Claude)
Coding agents that run on your machine and edit your repo — Aider, Cline, Continue, OpenHands, Claude Code — powered by Claude for quality or a local model for privacy.
Connect Claude to Local Tools & Agents with MCP
The Model Context Protocol lets Claude orchestrate tools, data and agents running privately on your own machine — the Claude-as-brain, local-capabilities synergy. Add or build a local MCP server.
Build a Private Local AI Stack (End-to-End)
Tie it together: a local model (Ollama) + a model-agnostic agent + tools via a local MCP server — a private assistant on your own machine, with Claude as an optional smart layer.
Local Agent or Claude? A Decision Guide
Fully-local agent, Claude-powered, or hybrid? The factors that decide it — privacy, task difficulty, cost, latency, reliability — and why hybrid often wins.
Securing Local & Hybrid Agents
An agent that can edit files and run commands is powerful and dangerous. Least privilege, sandboxing, human approval, prompt-injection defense, budget caps — how to run agents safely.
Reasoning Models Compared
Thinking budgets and reasoning effort across Claude, GPT, Gemini, DeepSeek, and Qwen — when to spend tokens on thinking, and when not to.
Full-Duplex Voice AI: Why Voice Agents Suddenly Got Real
GPT-Live and the new speech-native models listen and talk at the same time. What full-duplex changes, how it works, and the voice-AI landscape now.
Claude Voice with Opus & Sonnet (July 2026): Reasoning-First Voice vs GPT-Live
Anthropic opened Claude voice to Opus, Sonnet, and connected apps on July 23, 2026 — but kept the turn-based stack. What actually changed, when to switch models mid-call, and how the design bet differs from OpenAI's full-duplex GPT-Live.
Token Speed: Why AI Inference Suddenly Got 10-15× Faster
Wafer-scale chips and LPUs are serving frontier models at hundreds of tokens per second. What actually limits inference speed, the new hardware race, and why fast tokens change agents, voice and reasoning.
Computer-Use Agents Compared
Claude, GPT and Gemini can all drive a screen now — but each exposes a different action space, coordinate contract and safety gate. What actually breaks, and how to build a loop that survives it.
Shared-Login Browsers for AI Agents: The ego lite Pattern
A new class of browser — best exemplified by ego lite, which hit #1 on GitHub Trending on July 24, 2026 — lets Claude Code, Codex, Cursor, and any other coding agent drive an isolated Chromium session that inherits your real logins. What Spaces and semantic snapshots actually solve, why it undercuts Playwright for agent work, and the security cost of sharing session cookies with an autonomous process.
SKILL.md: The Cross-Agent Open Standard
One SKILL.md file, thirty-two agents. What the Agent Skills open standard actually is, how progressive disclosure keeps context cheap, what breaks portability, and how to write a skill that runs unchanged in Claude Code, Codex, Gemini CLI and Cursor.