Models & Providers

yoyo supports 13 providers out of the box — from Anthropic and OpenAI to local models via Ollama.

Default model

The default model is claude-opus-5-5 (Anthropic). You can change it at startup or mid-session.

Changing the model

At startup:

yoyo --model claude-sonnet-4-20250514
yoyo --model gpt-4o --provider openai
yoyo --model llama3.2 --provider ollama

During a session:

/model claude-sonnet-4-20250514
/model list
/model list openai

Note: Switching models with /model preserves your conversation history — you can change models mid-task without losing context. Use /model list to see all available models grouped by provider, or /model info <name> to see pricing, context window, and provider details for any model.

Providers

Use --provider <name> to select a provider. Each provider has a default model and an environment variable for its API key.

Tip: If you run yoyo without any API key configured, an interactive setup wizard will walk you through choosing a provider and entering your key. You can also save the config to .yoyo.toml directly from the wizard.

Note: the table shows each provider's default model — others are available. For example, MiniMax's flagship MiniMax-M3 can be selected via --model MiniMax-M3 or picked in the setup wizard.

Examples

# OpenAI
OPENAI_API_KEY=sk-... yoyo --provider openai

# Google Gemini
GOOGLE_API_KEY=... yoyo --provider google --model gemini-2.5-pro

# Local with Ollama (no API key needed)
yoyo --provider ollama --model llama3.2

# Custom endpoint (OpenAI-compatible API)
yoyo --provider custom --base-url http://localhost:8080/v1 --model my-model

You can also set these in .yoyo.toml:

provider = "openai"
model = "gpt-4o"
base_url = "https://api.openai.com/v1"

Route sub-agents to a cheaper model

A sub-agent (sub_agent or explore_agent) inherits the session's model by default. If your exploration and bulk-delegation work does not need your main model, point it at a cheaper one:

model = "claude-opus-4-6"
sub_agent_model = "claude-haiku-4-5"

This exists for cost routing, not capability — it is purely a cost lever, and the child is otherwise unchanged. Only the model id moves.

• Absent (the default) means the child runs on your session's model — nothing changes for anyone who does not set it.
• The child is dispatched against your session's provider and API key, so write the bare model id from that provider. A provider/model value does not switch providers.
• The failure report a parent sees on a dead sub-agent names the model the child actually ran on, so the cheaper route is visible rather than implied.

Cost estimation

Cost estimation is built in for many providers:

Cost estimates are also available for OpenAI, Google, DeepSeek, Mistral, xAI, Groq, ZAI and more. On provider = "openai", the exact ids gpt-6-astra, gpt-6-sol, gpt-6-luna and gpt-6.1-sol use yoagent's own preset: a 1,050,000-token window, 64K default max_tokens (128K maximum), and its price, including the higher whole-request rate above 272K prompt tokens (gpt-6.1-sol has its own price, not gpt-6-sol's). Other ids keep the generic OpenAI defaults.

The same holds for six more exact ids on their own provider: gemini-4-argon and gemini-3.8-flash (google: 1,048,576-token window, 65,536 max_tokens, which is also the declared output maximum), grok-4.7 (xai: 500,000-token window, 64K default max_tokens, and the whole-request rate at 200K prompt tokens and above), mistral-large-4 (mistral: 1,000,000-token window, 32K default max_tokens) and glm-5.3 / glm-5.3-flash (zai: 1,000,000-token window, 128K max_tokens). These come from yoagent's own preset table (ModelConfig::preset), so they are also known ids: no Unknown model warning. xAI and Mistral publish no output maximum, and yoyo declares none for Z.ai either, so yoyo does not warn about a higher max_tokens on those three. Gemini 4 Argon, Gemini 3.8 Flash and Mistral Large 4 are priced at the vendor's regular rate, so while an introductory or launch price is running /cost can read up to twice the actual bill. Near-miss ids (gemini-3.8-flash-lite, grok-4.7-mini, mistral-large-latest, glm-5.4) keep the generic defaults.

Models with no preset run on guessed limits

A model id yoyo has no preset for runs on its provider's guess: the limits that provider hands every id it does not know. On the OpenAI-compatible providers openai, openrouter, xai, groq, mistral, cerebras, github and zai that is a 128,000-token context window and max_tokens = 4096; on minimax a 1M window and max_tokens = 4096; on google a 1M window and max_tokens = 8192 (this is what gemini-2.5-pro, gemini-2.5-flash, gemini-2.5-flash-lite and gemini-2.0-flash run on today). That cap covers reasoning plus answer on every turn, so a newer model (for example a GPT release newer than yoyo's presets) can be cut short. yoyo now says so once per model on stderr, even in quiet or piped runs (stdout is unchanged):

warning: no preset is known for openai model 'gpt-6.9-test', so yoyo is using guessed limits: ...

If the model allows more, set the real limits yourself, which also silences the warning:

# .yoyo.toml
max_tokens = 64000
context_window = 400000

or pass --max-tokens / --context-window. Setting max_tokens is what silences it; setting only context_window keeps the warning about the output cap. ollama and custom endpoints use the same defaults but are not warned about, since you configure those servers yourself. deepseek has one preset for the whole provider, so it is not warned about. anthropic ids without a preset and every bedrock id also run on a guess (200,000 / max_tokens = 16000), but yoyo does not warn about those yet.

Context window

yoyo assumes a 200,000-token context window (the standard for Claude models). When usage exceeds 80% of this, auto-compaction kicks in. See Context Management.