Providers & Models

Kolega Code is provider-agnostic. You choose which LLM provider to use and which model to use for each of several roles.

Provider breadth is table stakes; Kolega Code’s extra control is assigning models by operational role and by agent role, so cheap/fast models can investigate while stronger models plan, build, or think.

Supported providers

The agent runtime recognizes these providers:

Provider	Identifier	API key variable
Anthropic	anthropic	ANTHROPIC_API_KEY
OpenAI	openai	OPENAI_API_KEY
OpenAI (ChatGPT subscription)	openai_chatgpt	none — sign in with ChatGPT
Google	google	GOOGLE_API_KEY
Groq	groq	GROQ_API_KEY
Together.ai	together	TOGETHER_API_KEY
Fireworks.ai	fireworks	FIREWORKS_API_KEY
x.ai (Grok)	xai	XAI_API_KEY
DashScope (Alibaba)	dashscope	DASHSCOPE_API_KEY
Moonshot (Kimi)	moonshot	MOONSHOT_API_KEY
DeepSeek	deepseek	DEEPSEEK_API_KEY
Z.AI (GLM Coding Plan)	zai	ZAI_API_KEY
Kimi Coding Plan	kimi_coding	KIMI_CODING_API_KEY
Ollama Cloud	ollama_cloud	OLLAMA_API_KEY
OpenRouter	openrouter	OPENROUTER_API_KEY
Llama (local)	llama	none — local model

Tip

Z.AI GLM Coding Plan. The zai provider targets Z.AI’s Anthropic-compatible endpoint (https://api.z.ai/api/anthropic) and exposes GLM-5.2 (default) and GLM-5.1. Set ZAI_API_KEY to your GLM Coding Plan key (the key Z.AI also documents as ANTHROPIC_AUTH_TOKEN).

Tip

Kimi Coding Plan. The kimi_coding provider targets Kimi’s Anthropic-compatible coding endpoint (https://api.kimi.com/coding/), which is separate from the standard Moonshot API used by the moonshot provider. It exposes k3 with a 256K context on Moderato and higher plans, and k3[1m] with a 1M context on Allegretto and higher plans. Both K3 IDs use deep thinking with fixed max effort. The kimi-for-coding ID remains the default because it is available on every plan; with thinking effort auto (enabled) it routes to K2.7 Code, and with none (disabled) it routes to K2.6. Set KIMI_CODING_API_KEY to your Kimi Coding Plan key.

Tip

Ollama Cloud. The ollama_cloud provider uses Ollama Cloud’s direct OpenAI-compatible API at https://ollama.com/v1 with OLLAMA_API_KEY. Kolega Code uses the direct API model IDs returned by Ollama, such as glm-5.2 and gpt-oss:120b, rather than local Ollama aliases such as :cloud or -cloud. Ollama may deprecate or retire older cloud models, so Kolega Code’s supported model catalog should be refreshed periodically from Ollama’s model library/API.

Tip

OpenRouter. The openrouter provider reaches OpenRouter’s OpenAI-compatible endpoint (https://openrouter.ai/api/v1) with OPENROUTER_API_KEY. Unlike the other providers, OpenRouter is a gateway in front of many vendors, so Kolega Code ships a generated catalog of every tool-capable OpenRouter model — a model that cannot call tools cannot run the agent, so it is deliberately excluded.

Model IDs are vendor/model, for example anthropic/claude-opus-5 or z-ai/glm-5.2. OpenRouter’s routing suffixes pass straight through, so :free, :nitro and :floor variants work as their own IDs. :free endpoints are heavily rate limited and are best kept for experiments rather than long sessions.

Because the catalog runs to hundreds of entries, the Settings picker (the main Model dropdown and every Agent Models row) leads with OpenRouter’s 20 most-used models, in the order of OpenRouter’s own LLM Leaderboard, and then offers an Other… option where you can type any catalogued model id directly. /model and the sub-agent model catalog stay most-used-only. Every catalogued model still works — pass the exact ID:

kolega-code models list --provider openrouter        # the whole catalog, most-used first
kolega-code models list --provider openrouter --featured
kolega-code --provider openrouter --model deepseek/deepseek-v3.2

or /model deepseek/deepseek-v3.2 in the TUI, KOLEGA_CODE_MODEL, or settings.json. The bundled catalog is a snapshot taken at release time; to pick up models published since then, run kolega-code models refresh. That caches OpenRouter’s current model list locally and merges it at startup. Cached models are usable by ID but never join the featured list, and startup itself never touches the network.

Two behaviours are specific to this provider:

• No max_tokens is sent. On a gateway an output cap is a routing hint that excludes every upstream with a lower cap, biasing routing and price. The one exception is DeepSeek models, which always carry an explicit 64000-token cap: their real output ceiling is far below the published value and the server truncates silently without one, so the cap doubles as a deliberate filter for upstreams that report truncation honestly.
• Prompt caching is automatic. Kolega Code sends OpenRouter’s top-level cache_control breakpoint, so Anthropic-, Vertex- and Bedrock-backed models get prompt caching with no extra configuration.

Routing preferences are not set by default, because any of them narrows which upstreams may serve a request. Opt in with KOLEGA_CODE_OPENROUTER_PROVIDER_ORDER and KOLEGA_CODE_OPENROUTER_PROVIDER_ONLY (comma-separated provider slugs).

Tip

Fireworks.ai. The fireworks provider targets Fireworks’ OpenAI-compatible serverless endpoint (https://api.fireworks.ai/inference/v1) with FIREWORKS_API_KEY. Fireworks model IDs use resource names such as accounts/fireworks/models/glm-5p2. Kolega Code exposes Kimi K3, GLM-5.2, GLM-5.1, Kimi K2.7 Code, DeepSeek V4 Pro/Flash, MiniMax M3, and Qwen 3.7 Plus through the Settings UI and CLI. Fireworks reasoning models return reasoning_content. Kimi K3 always reasons and uses max effort; the other listed Fireworks models expose none, low, medium, high, and max as reasoning_effort values.

Note

The full Settings screen in the TUI is generated from Kolega Code’s model registry, so new entries in the supported model catalog appear there automatically. You can also select any supported provider/model through environment variables and CLI flags. See Settings & API Keys.

Model roles

Rather than one model for everything, Kolega Code uses three model slots, so you can match cost and capability to the job:

Role	Purpose	When unset
Long context	The main coding model — drives the conversation and most work	The active model you selected
Fast	Quick, cheap utility calls (e.g. web_fetch’s page-answering stage)	Inherits the active model
Thinking	Extended-reasoning (“think hard”) operations	Inherits the active model

An unset slot inherits the active model, so out of the box all three run on your main model. Pin the Fast or Thinking slot when you want a cheaper or different model for that work — Settings shows which model each slot currently resolves to, so an inherited slot is never a surprise.

Set models per role

In the TUI, open Settings → Models → Model Slots (or /model) and give the Fast or Thinking row a provider and model. Leave a row on Default (inherit) to keep it following the active model.

A slot may use a different provider from your main model — for example a DeepSeek main model with Anthropic Haiku for fast utility calls. That provider needs its own API key: add it under Settings → Providers, which lists every provider with its credential status and lets you set up more than one before applying. Settings refuses to apply a slot whose provider has no credential.

Equivalently, from the command line:

kolega-code . \
  --provider anthropic --model claude-opus-5 \
  --fast-provider anthropic --fast-model claude-haiku-4-5-20251001 \
  --thinking-provider anthropic --thinking-model claude-opus-5 \
  --thinking-effort medium

Or with environment variables — see Environment Variables for the complete list.

How a model gets chosen

For each role, Kolega Code resolves the provider and model in this order (first match wins):

1. CLI flags — e.g. --provider, --model, --fast-model, …
2. Environment variables — e.g. KOLEGA_CODE_PROVIDER, KOLEGA_CODE_MODEL, the per-role variants, and a project-local .env file.
3. Saved slot — the Fast/Thinking provider and model saved in Settings.
4. The active model — the provider/model selected in Settings.

Provider and model are always given together. A flag or environment variable that names only one of them is an error: no model is defaulted from a provider, and no provider is inferred from a model id — the same id can be served by different providers on different credentials, so choosing one would silently choose an account.

A configuration is only valid if a provider/model has been selected and every role’s provider has an API key available (the local llama provider needs none). If a key is missing, the CLI reports exactly which environment variable is required.

Per-agent models

The three slots above describe operational roles (main, fast, thinking). Separately, you can give each agent its own model, so a cheap, fast model handles investigation while a stronger model does the building. This applies to top-level agents and to the sub-agents they dispatch.

Agent role	Key	Runs
Planning	planning	The plan-mode agent
Building	building	The main coder agent
Investigation	investigation	Read-only research/exploration sub-agents
General	general	General-purpose sub-agents
Browser	browser	Browser/web sub-agents

Each role inherits the active (long-context) model unless you give it an override — so with nothing configured, behavior is unchanged.

In the full Settings screen, the Agent Models category lets you select a role and set its provider, model, and effort. Leave a role on Default to inherit; pick a provider, model, and thinking effort to override it.

The Browser role requires a model whose catalog entry supports vision. It does not fall back to another model or run in a text-only mode. If Default resolves to a non-vision model, Settings shows a warning and the browser agent fails with a configuration error when dispatched. Select an explicit vision-capable Browser model to enable it.

Or use environment variables, named KOLEGA_CODE_<ROLE>_PROVIDER / _MODEL / _EFFORT:

# Use a fast model for investigation; everything else uses the active model.
export KOLEGA_CODE_INVESTIGATION_PROVIDER=deepseek
export KOLEGA_CODE_INVESTIGATION_MODEL=deepseek-v4-flash
export KOLEGA_CODE_INVESTIGATION_EFFORT=high

# Browser overrides must name a vision-capable model.
export KOLEGA_CODE_BROWSER_PROVIDER=anthropic
export KOLEGA_CODE_BROWSER_MODEL=claude-sonnet-4-6

Resolution order for a role (first match wins): env var → saved Settings → the active model. Each override’s provider must have an API key, exactly like the main roles. (Inside a run_workflow script, an explicit model=/effort= on an agent() call still takes precedence over these per-role defaults.)

Thinking effort

Thinking effort is model-specific. Kolega Code validates the selected value against the active model and resets it to the new model’s default when you switch models in the TUI.

Provider/model	Values	Kolega Code default
Anthropic claude-fable-5	low, medium, high, xhigh, max	medium
Anthropic claude-opus-5	low, medium, high, xhigh, max	medium
Anthropic claude-sonnet-5	low, medium, high, xhigh, max	medium
DeepSeek deepseek-v4-pro	none, high, max	high
Moonshot kimi-k3	max	max
Moonshot kimi-k2.7-code	auto	auto
Moonshot kimi-k2.6	auto, none	auto
Kimi Coding Plan k3, k3[1m]	max	max
Kimi Coding Plan kimi-for-coding	auto, none	auto
Z.AI glm-5.2	high, max	max
Z.AI glm-5.1	auto, none	auto
Fireworks serverless reasoning models	none, low, medium, high, max	medium
Ollama Cloud thinking models	none, low, medium, high, max	medium
Ollama Cloud gpt-oss:*	low, medium, high	medium
Google gemini-3.6-flash	minimal, low, medium, high	medium
Google gemini-3.5-flash-lite	minimal, low, medium, high	minimal
Google gemini-3.5-flash	minimal, low, medium, high	medium
Google gemini-3.1-pro-preview	low, medium, high	high
OpenAI gpt-5.6-*	none, low, medium, high, xhigh, max	medium
OpenAI gpt-5.5	minimal, low, medium, high, xhigh	medium
OpenAI gpt-5.4*	minimal, low, medium, high	medium
OpenAI gpt-5.3-codex-spark	minimal, low, medium	low
xAI grok-4.5	low, medium, high	medium
xAI grok-4.3	none, low, medium, high	low

Check what’s active

kolega-code doctor --project .

doctor prints the resolved provider/model for each role (long, fast, thinking) and whether the necessary API keys are present.