Models and providers¶

MCParser talks to models through pydantic-ai, so any model it supports can fill any role. The defaults are Anthropic’s:

Tier	Roles	Default
strong	segmentation, lemma, syntax (HEAD and DEPREL), deps	`anthropic:claude-opus-5-5`
efficient	upos, feats, scorer	`anthropic:claude-sonnet-5-5`
light	xpos	`anthropic:claude-haiku-5-5`

Model names are provider:model. Only the Anthropic models have been tested in real runs; the other providers work through pydantic-ai but are untested here, so try a few sentences with --sentence or --max-sentences before a long run.

Installing a provider¶

The base install includes Anthropic. Add others with an extra:

pip install "mcparser[openai]"
pip install "mcparser[google,bedrock]"
pip install "mcparser[all-providers]"

Extras exist for openai, google, bedrock, groq, mistral, xai and openrouter. For anything else pydantic-ai supports (Ollama, vLLM, DeepSeek, Azure, …), install the matching pydantic-ai-slim extra yourself.

Credentials¶

Providers read their usual environment variables:

Provider	Model prefix	Environment
Anthropic	`anthropic:`	`ANTHROPIC_API_KEY`
OpenAI	`openai:`	`OPENAI_API_KEY`
Google (Gemini API)	`google-gla:`	`GEMINI_API_KEY` or `GOOGLE_API_KEY`
Google (Vertex AI)	`google-vertex:`	Google Cloud application default credentials
AWS Bedrock	`bedrock:`	AWS credentials and region, or `AWS_BEARER_TOKEN_BEDROCK`
Groq	`groq:`	`GROQ_API_KEY`
Mistral	`mistral:`	`MISTRAL_API_KEY`
xAI	`xai:`	`XAI_API_KEY`
OpenRouter	`openrouter:`	`OPENROUTER_API_KEY`
Ollama	`ollama:`	`OLLAMA_BASE_URL` (and `OLLAMA_API_KEY` if needed)

A missing key stops the run at the first model request with a message naming the variable. The rules run first, so cells they settle without a model may already be written to the prediction file; running again continues from there.

Choosing models¶

# One model for every role
mcparser run pred.conllu -s train.conllu --model openai:gpt-5.6

# Different tiers
mcparser run pred.conllu -s train.conllu \
    --strong-model google-gla:gemini-2.5-pro \
    --efficient-model google-gla:gemini-2.5-flash \
    --light-model google-gla:gemini-2.5-flash

# One role on its own model
mcparser run pred.conllu -s train.conllu --model-for lemma=anthropic:claude-opus-5-5

The same keys can be set in a settings file (mcparser settings > settings.yaml, then --config settings.yaml). The strong tier does the hardest work (syntax and segmentation), so spend there first. See The agents for reasoning (“thinking”) settings. The model names above are examples; use whichever current model your provider offers. A model needs reliable tool calling and enough context for a task prompt of a few thousand tokens.

Prompt caching¶

Most of the input cost is the task prompt: tool definitions, instructions and evidence that repeat across requests. cache: true (the default) asks pydantic-ai to place the provider’s cache markers, so repeated prefixes are read at a fraction of the input price (in a real run about 74% of input tokens were cache reads).

Provider	Behaviour
Anthropic	explicit breakpoints on tools, instructions and the conversation (5-minute cache)
AWS Bedrock	explicit cache points for Anthropic and Amazon Nova models
OpenRouter	explicit breakpoints for Anthropic and Gemini models
OpenAI	prompt caching for GPT-5.6 and later on the OpenAI API
Gemini, older OpenAI models, DeepSeek, xAI, Groq and others	cached implicitly by the provider; the setting changes nothing

Prompts below a provider’s minimum length (512 tokens for the current Anthropic models) are simply not cached. Set cache: false to switch it off.

Cost estimates and `--max-cost`¶

Each run prints token counts and an estimated price per model. Anthropic prices come from a table in mcparser.pricing; other providers’ prices come from the genai-prices database, including their cache read and write rates. A model with no known price is shown without a cost, and --max-cost cannot count it. Estimates use list prices, ignore discounts, and run a little low when cache writes are not 5-minute writes. Check your provider’s dashboard for the real bill.