I wanted different LLM providers behind one easy OpenCode setup: a truly sovereign European option, a powerful cheap one that is trustworthy enough for open-source work, and — in the future — a confidential one. The result is a gist I maintain: dhcgn/206f9adc367b6203c42841e12b726203.

Problem

Switching models between providers means editing config files, managing keys, and re-registering MCP servers per machine. I wanted one portable opencode.jsonc I can install with a single command and that covers my three trust tiers.

Prerequisites

• OpenCode installed (opencode.ai)
• PowerShell on Windows (one-line installer below; curl variant in the gist for bash)
• API keys, set as user environment variables (never stored in the config file):
  • LYCEUM_API_KEY, META_API_KEY
  • CONTEXT7_API_KEY, BRAVE_API_KEY, GITHUB_MCP_PAT
• Node.js with npx (required by the local Brave search MCP server)

Steps

Install with cache-busting so you always get the latest gist revision:

irm "https://gist.githubusercontent.com/dhcgn/206f9adc367b6203c42841e12b726203/raw/Install-OpenCodeConfig.ps1?prevent_cache=$([guid]::NewGuid())" | iex

The script downloads opencode.jsonc, .env.example, and Set-OpenCodeEnvironment.ps1 to ~/.config/opencode/, then checks your user environment variables and prints SET / MISSING per name (never a value). Fill the missing ones into .env and apply with Set-OpenCodeEnvironment.ps1.

What the config contains

General settings: lsp enabled, the built-in opencode provider disabled, and the @dietrichgebert/ponytail plugin loaded.

Providers

Tier	Provider	Models	Note
🟢 Sovereign EU	Lyceum (German-based, OpenAI-compatible)	moonshotai/kimi-k3, z-ai/glm-5.3, z-ai/glm-5.3-flash	High data privacy from an EU perspective; 1M context / 131k output per model; low/high/max reasoning variants
☢️ Cheap, OSS-safe	Meta Model API (US hyperscaler, not sovereign)	muse-spark-1.3-contributor, muse-spark-1.3	Text+image+PDF+video input; use the contributor model only with public code and prompts — its lower price is tied to data being usable for learning
🔜 Confidential	privatemode.ai, by Edgeless Systems (planned)	glm-5.3, kimi-k2.6, gpt-oss-120b	TEE-based inference with remote attestation and operator exclusion (Betreiberausschluss); OpenAI-compatible endpoints; not in the config yet — see below

MCP servers and extras

Server	Type	Purpose
context7	remote	Library documentation
brave-search	local (npx)	Web search
github	remote	GitHub Copilot MCP

Plus Export-OpenCodePricing.ps1, which exports the config’s cost fields to pricing.csv.

Why privatemode.ai is next

Privatemode (docs v1.55, Sep 2026) runs inference inside attested confidential VMs: data stays encrypted in transit, at rest, and during use, and remote attestation lets the client verify what it talks to — so even the operator is technically excluded. That is the tier for confidential code, above sovereign (Lyceum) and public-only cheap (Meta contributor).

The candidate chat models, all with streaming, tool calling, and structured outputs (what an agent loop needs) on OpenAI-compatible endpoints (/v1/chat/completions etc.), so wiring follows the same pattern as the Lyceum entry:

Model ID	Input	Context	Note
glm-5.3 (glm-latest)	text	256k tokens	Reasoning always on; low/high/max effort
kimi-k2.6 (kimi-latest)	text, image	256k tokens	Reasoning can be disabled via thinking: false
gpt-oss-120b	text	128k tokens	Smallest input price of the three

Also available on the same platform: qwen3-embedding-4b embeddings and whisper-large-v3 / voxtral-mini-3b speech-to-text (the pair my hushscribe already uses).

Verification

1. Confirm ~/.config/opencode/opencode.jsonc exists after install.
2. Re-run the installer — all variables should print SET.
3. Start OpenCode and check the model picker shows the 🟢/⚠️/☢️ entries.

Costs

Relative prices are recorded in the config’s cost fields (state: Sep 2026): on Lyceum, glm-5.3-flash is cheapest and kimi-k3 highest; on Meta, the contributor model is a fraction of the regular one. Check the provider dashboards for current billing — the Lyceum pricing link is in the config comments.

Privatemode list prices for comparison (per 1M tokens, +VAT, Sep 2026): gpt-oss-120b €0.43 in / €1.70 out, glm-5.3 and kimi-k2.6 €1.55 / €7.74 with €0.15 cached input. So confidential does not mean expensive here — gpt-oss-120b undercuts the regular Meta model.

Intelligence vs. cost per task (13 Sep 2026, Artificial Analysis coding index) for the models in this post:

Source: Artificial Analysis intelligence vs. cost comparison — screenshot dated 13 Sep 2026, values rot fast.

Limitations

• The contributor model must never see private code or prompts.
• Meta is a US hyperscaler: cheap and multimodal, but not a sovereignty answer.
• Lyceum prices and model availability can change; the gist pins what I use, not a price guarantee.
• privatemode.ai support is outlook only — planned for confidential work where even the operator must be technically excluded.