Add New Model

Wire a newly-released provider model into pydantic-ai. Optimized for the common case (mirror an existing sibling); flags the cases where it's not a mirror and needs deeper work.

Reference docs (read once before scoping)

• agent_docs/pydantic-ai-slim.md — the Ownership section, plus pydantic_ai_slim/pydantic_ai/native_tools/AGENTS.md, for the user-visible surface this model needs to land on.
• pydantic_ai_slim/pydantic_ai/profiles/AGENTS.md, providers/AGENTS.md, models/AGENTS.md, and pydantic_ai_slim/pydantic_ai/AGENTS.md (the capability-flag and Provider.model_profile() rules), plus the Design Rules section of agent_docs/pydantic-ai-slim.md. These tell you where capability facts belong (profile vs. provider vs. model class) when the new id has non-mirror behavior.

Inputs

User invokes with provider + model id (e.g. openai gpt-5.6). If missing, ask via AskUserQuestion.

Step 1 — Verify the model exists at the provider

Never trust marketing names, news articles, or guesses. Hit the provider's model-listing endpoint:

Load credentials from the repo-root .env with source .env && <cmd>. list-foundation-models is region-scoped, so query the region your models are actually deployed in (not a hard-coded default). List every id the provider exposes for this release — base, dated snapshot, -pro, -mini, -nano, -codex, -chat-latest. Add only what actually exists; do not extrapolate sibling variants.

If the user-given id is not in the listing, stop and confirm with the user before proceeding.

Step 2 — Mirror the most recent add-model PR for this provider

git log --all --oneline --grep="<previous-version-pattern>" -20
# e.g. for openai: --grep="gpt-5\.4\|gpt-5\.3"
# e.g. for anthropic: --grep="claude-opus-4\|claude-sonnet-4"

Pick the smallest, most recent "add model X" PR for the same provider. Pull its file list with gh pr view <num> --json files --jq '.files[].path'. That file list is the floor of what you'll touch. It is rarely the ceiling.

Step 3 — Enumerate (load-bearing step)

For every variable, tuple, and literal you're about to touch, grep its readers across the repo. This step is what catches the snapshot/enumeration tests that ratchet on every model add. Skipping it pushes work onto CI and produces broken PRs.

Specifically, for a typical model add, grep for:

• The previous model id literal you're mirroring (e.g. gpt-5.4, claude-opus-4-5) — rg '<prev-id>' --glob '!**/*.yaml' --glob '!**/cassettes/**'
• Every prefix/membership key in the profile module you're editing (e.g. OpenAI's _REASONING_SUPPORT_BY_PREFIX keys, Anthropic's inline model_name.startswith((...)) tuples, xAI's _GROK_43_REASONING_MODELS)
• KnownModelName and its provider-block neighbours
• Snapshot test files: tests/models/test_model_names.py, tests/test_capabilities.py

Classify each hit:

• must update — model-name lists, dispatch tuples
• snapshot to refresh — inline_snapshot blocks needing pytest --inline-snapshot=fix
• skip — VCR cassettes, docs about an unrelated model

If rg output looks mangled (unicode/regex artifacts), drop to grep -n — don't push past garbled output.

Step 4 — SDK pin check

Snapshot/enumeration tests in this repo often tie KnownModelName to a literal set defined in the provider SDK. The provider SDK frequently lags the model release by days.

For OpenAI, check the broad union the repo actually consumes (OpenAIModelName = str | AllModels), not the chat-only ChatModel Literal — AllModels also carries Responses-API-only and embeddings ids that the enumeration test walks:

uv run python -c "from openai.types import AllModels; from typing import get_args; print([m for m in get_args(AllModels) if '<new-version>' in m])"

Anthropic and xAI do not follow this OpenAI flow — the repo bridges their SDK lag with a local Literal and lands green immediately, no split. See the SDK-lag bridge notes in their landmine sections below (Anthropic checks ModelParam, not Model).

If a provider with no bridge (e.g. OpenAI) doesn't yet list the new id, the literals PR cannot land green on CI. Surface this to the user with the choice:

1. Split the PR — land the profile/handler change now (capability flip is harmless without KnownModelName literals because runtime accepts plain strings). Open a separate draft PR for the literals; promote it once the SDK ships and the pin is bumped.
2. Hold the whole PR — wait for SDK release, bump pin, refresh snapshots with pytest --inline-snapshot=fix, push.
3. Bump SDK pin now — only if the new SDK is already released.

Default recommendation: option 1 (split). Use AskUserQuestion.

Step 5 — Probe capabilities (only if not a pure mirror)

If the new model is just another sibling in an existing family (e.g. gpt-5.5 after gpt-5.4), skip to Step 6 — the existing profile branch covers it once you add the prefix to the dispatch tuple.

If the model is a new family or has unclear capabilities, write a small comparison script (local-notes/probe_<model>.py) that hits the new model AND its closest neighbour with:

• temperature / top_p (does the API reject sampling params?)
• reasoning.effort values (none, low, medium, high, xhigh) — note which the API accepts
• New parameters mentioned in the release notes
• Streaming / tool calls if the family is new

Diff the responses. Anything that diverges from the neighbour belongs in the profile.

Step 6 — Edit (minimal diff matching the mirrored PR)

Make only the changes the enumeration step surfaced. Resist scope creep. If you discover a pre-existing bug in a sibling model's profile, flag it in the PR description; do not fix it in this PR.

After edits:

make format && make lint
PYRIGHT_PYTHON_IGNORE_WARNINGS=1 uv run pyright <changed-python-files>

Run the tests directly touching the changed surface — the profile test plus any enumeration tests you updated. CI is the safety net for the long tail; locally you only need to verify the surface area of your change.

If snapshot tests changed: uv run pytest <file> --inline-snapshot=fix then verify the diff is the expected literal addition only.

Step 7 — VCR / integration tests

Default for mirror-only adds: skip recording a new VCR. Repo convention uses one representative model per family for VCR (e.g. gpt-5.2 covers the gpt-5.x reasoning family). The profile unit test added in Step 6 is sufficient.

When the new model introduces meaningful changes to pydantic_ai_slim/pydantic_ai/models/<provider>.py (new request shape, new response field, new handler branch):

1. Look for an existing parametrized VCR test that covers the changed feature. rg -l '<feature-name>' tests/models/. If one exists and it parametrizes over model ids, tag the new id onto the parametrize list rather than writing a new test.
2. If no parametrized coverage exists and you need a new VCR test, place it:
   • Prefer tests/models/<provider>/test_<feature>.py only if the file already exists (e.g. tests/models/anthropic/test_output.py).
   • Otherwise add it to tests/models/test_<provider>.py. Do not create a new tests/models/<provider>/ subdirectory if one doesn't already exist for this provider.
3. Record using the testing-skill skill workflow.

Step 8 — PR

Terse, not botty. Structure:

• One sentence: what model(s) were added.
• Bulleted file list with one-line "what changed" per file.
• "Verified via probe / mirror of #NNNN" — explicit about which changes were API-verified vs assumed-by-mirror.
• Flag pre-existing latent bugs found but deliberately not fixed.
• Link the prior add-model PR for context.

Include the PR template, fill in the issue number, and check the "AI generated code" box in the GitHub UI yourself — gh pr create cannot set it.

Provider-specific landmines

OpenAI

• _REASONING_SUPPORT_BY_PREFIX in pydantic_ai_slim/pydantic_ai/profiles/openai.py — a dict keyed by model-name prefix ('gpt-5.6', 'gpt-5.3-chat', 'gpt-5', 'o', …) → _ReasoningSupport(enabled_by_default, can_be_disabled, supports_mode), resolved first-match-wins by _reasoning_support(). A new gpt-5.N family MUST be added here, and ordering matters: a more specific prefix ('gpt-5.3-chat') must precede the broader one it would otherwise shadow ('gpt-5.3'), and every newer gpt-5.x family must precede the plain 'gpt-5' catch-all. Miss it and the model falls through to the _NO_REASONING default (thinking_always_enabled=False, openai_supports_reasoning_effort_none=False) — wrong defaults, no error. The resolved matrix is pinned in tests/profiles/test_openai.py.
• KnownModelName lives in pydantic_ai_slim/pydantic_ai/models/_known_model_names.py (a TypeAliasType), not models/__init__.py. It has split openai: and gateway/openai: blocks. Don't assume the gateway block omits -pro/-chat-latest — for the gpt-5.x series it enumerates them (gateway/openai:gpt-5.2-pro, gateway/openai:gpt-5.3-chat-latest, …). Mirror the exact enumeration of the most recent series across both blocks rather than guessing a convention.
• Most gpt-5.x-chat variants DO reason (_ALWAYS_ON_REASONING: reason at a fixed effort, reject reasoning_effort='none' and sampling parameters). The non-reasoning exception is the original gpt-5-chat/gpt-5-chat-latest (_NO_REASONING). Verify each -chat/-chat-latest variant against the live Responses API; don't copy a sibling's reasoning class blindly.
• -pro variants map to _ALWAYS_ON_REASONING (gpt-5.2-pro, gpt-5.4-pro, gpt-5.5-pro) — they reason and reject effort='none'. The three-fact _ReasoningSupport model doesn't encode per-effort-value rejection, so if a new -pro rejects a specific value (e.g. 'low'), flag it rather than assuming the enum covers it.
• tests/models/test_model_names.py::test_known_model_names asserts known_model_names() equals the set generated from _PROVIDER_TO_MODEL_NAMES['openai'], i.e. OpenAIModelName = str | AllModels (the broad union, not the chat-only ChatModel). A literal missing from AllModels fails this test — Step 4's SDK check is mandatory and must query AllModels.
• tests/test_capabilities.py::test_model_json_schema_with_capabilities is a snapshot test enumerating every KnownModelName. Refresh with --inline-snapshot=fix.

Anthropic

• TWO literal lists, not one. Add the id to BOTH:
  1. pydantic_ai_slim/pydantic_ai/models/_known_model_names.py — the anthropic: AND gateway/anthropic: blocks (the KnownModelName alias moved here from models/__init__.py in #5803; older add-model PR diffs that edit __init__.py are stale on this point).
  2. AnthropicModelName in models/anthropic.py — see the SDK-lag bridge below.
• Anthropic names ARE enumeration-tested, unlike what you might assume from the hand-maintained look of the list. tests/models/test_model_names.py::test_known_model_names asserts known_model_names() (i.e. KnownModelName) equals the set generated from _PROVIDER_TO_MODEL_NAMES['anthropic'], which is AnthropicModelName = ModelParam (from the installed anthropic SDK) | Literal[...bridge...]. A new id missing from BOTH the SDK's ModelParam and the local bridge fails this test with Extra names: {...}.
• SDK-lag bridge (the Step 4 mechanism for Anthropic). When the installed SDK's anthropic.types.model_param.ModelParam doesn't yet list the new id (check: get_args it and grep), bridge it with a local Literal:
  pythonCopy

  AnthropicModelName = LatestAnthropicModelNames | Literal['claude-fable-5']
  

  plus a docstring note to drop the literal once the anthropic pin is bumped past the release that adds it. This is the in-repo pattern (commit 87e7ccf39, PR #5849, added the claude-fable-5 bridge; 526b065e2 later dropped it and bumped the floor to anthropic>=0.108.0). The bridge lands green immediately — no need to split the PR for Anthropic. NOTE: ModelParam ≠ anthropic.types.model.Model; check ModelParam (it's the superset the repo actually consumes, and may carry ids Model doesn't).
• Capability flags live as startswith prefix tuples in profiles/anthropic.py inside anthropic_model_profile() (+ the module-level _ANTHROPIC_CODE_EXECUTION_20260120_MODEL_PREFIXES). A new family is NOT a literal-only add — it almost always needs at least one profile override (a literal-only add is only right when the family truly inherits every default branch, which is rare). Probe and set each independently: models_that_support_json_schema_output, supports_adaptive, supports_effort, supports_xhigh_effort, disallows_budget_thinking, disallows_sampling_settings, supports_task_budgets, supports_tool_search, code-exec version, anthropic_supports_fast_speed. Default-False flags (e.g. fast speed) are subtractive — just omit the id from that tuple.
• Forced tool_choice is a real per-model divergence worth probing. Most Anthropic models accept tool_choice {'type':'any'}/{'type':'tool'} and only reject forcing alongside thinking; some (Claude Fable 5) reject it unconditionally (400 tool_choice forces tool use is not compatible with this model). That's modeled by AnthropicModelProfile.anthropic_supports_forced_tool_choice (default True) threaded into _support_tool_forcing in models/anthropic.py. Probe tool_choice={'type':'any'} against the new id AND its neighbour to tell a genuine divergence from a thinking-only constraint.
• Tests: profile-flag unit tests go in tests/profiles/test_anthropic.py (NOT tests/models/test_anthropic.py). Forced-tool-choice / _prepare_tools_and_tool_choice fallback tests go in tests/models/test_tool_choice_unit.py. The capability behaviors keyed on shared flags (sampling drop, budget-thinking reject, xhigh) are already covered by the opus-4-7/4-8 parametrized tests — adding the new id to those lists is redundant once a dedicated profile test asserts the flags.
• tests/test_capabilities.py::test_model_json_schema_with_capabilities snapshots the whole KnownModelName enum. Refresh it by running THAT TEST ALONE with --inline-snapshot=fix — running the whole file can pull in unrelated snapshot() blocks and abort the fix.
• providers/bedrock.py bedrock_structured_output_unsupported: only relevant if the new id is actually served on Bedrock. A direct-API-only model (not in Bedrock's foundation-model list) doesn't belong there; don't add it speculatively just because the mirrored PR did.

xAI (Grok)

• Strict enumeration despite XaiModelName = str | ChatModel. The str arm looks permissive but the enumeration test's get_model_names recurses into the union and yields nothing for a bare str type — so KnownModelName's xai: block is strictly enforced against the SDK's ChatModel Literal, exactly like OpenAI. tests/models/test_model_names.py::test_known_model_names fails with "Extra/Missing names" on any mismatch. Confirm parity: xai: + get_args(ChatModel) must equal the xai: entries in models/_known_model_names.py.
• SDK-lag bridge (Anthropic-style, and it's needed for xAI too). xai_sdk's ChatModel frequently lags a release — as of 1.17.0 it still lacked grok-4.5, so bumping the floor won't help (check newer wheels first: download from PyPI and grep xai_sdk/types/model.py for ChatModel: TypeAlias = Literal[). Bridge with a local Literal: XaiModelName = str | ChatModel | Literal['grok-4.5', 'grok-4.5-latest'], docstring-note to drop it when the floor is bumped past the release that adds the id. This makes the enumeration test's generated side include the new id, matching the hand-added _known_model_names.py literal — lands green immediately. (Historically xAI bumped the SDK floor — commits e3f6e3c54/58f394aea — but that only works when the SDK already ships the id.)
• A new grok-4.x is NOT a pure mirror. Reasoning-effort support lives in profiles/grok.py as membership sets (_GROK_43_REASONING_MODELS + a per-family effort frozenset), not startswith prefixes. The grok-4 prefix auto-grants grok_supports_builtin_tools=True but leaves grok_reasoning_efforts empty (→ supports_thinking=False) unless you add the id to a reasoning-models set. Forgetting this silently ships a reasoning model with thinking off. Add a _GROK_<ver>_REASONING_MODELS set + effort frozenset and an elif branch in grok_model_profile.
• Probe reasoning efforts via the OpenAI-compatible REST endpoint, comparing against the closest neighbour: POST https://api.x.ai/v1/chat/completions with {"model":..., "reasoning_effort": <val>, "max_tokens":1}. A rejected value returns 400 This model does not support 'reasoning_effort' value '<val>'. Whether none is accepted decides thinking_always_enabled (rejected → always-on). CAVEAT: REST silently accepts xhigh/minimal even though the gRPC ReasoningEffort (in xai_sdk/types/chat.py) is Literal['none','low','medium','high'] — don't over-read REST acceptance; GrokReasoningEffort is those four and _map_reasoning_effort collapses xhigh→high, minimal→low. Grok 4.5 example: accepts low/medium/high, rejects none → always-on; Grok 4.3 accepts none too.
• Floating aliases (grok-latest, grok-build-latest) go in the profile reasoning-models set (so passing them resolves the right behavior) but are NOT added as KnownModelName literals — mirror the SDK, which lists only stable ids like grok-4.3/grok-4.3-latest.
• xai is NOT a gateway provider ('xai' absent from providers/gateway.py's ModelProvider) — no gateway/xai: entries in _known_model_names.py.
• Snapshot that ratchets: tests/test_capabilities.py::test_model_json_schema_with_capabilities embeds the full KnownModelName enum. It's a plain sorted string list — hand-add the new ids in sorted position (deterministic, no need for --inline-snapshot=fix). Profile-flag tests go in tests/providers/test_xai.py (see test_xai_model_profile); the parametrized tests/test_thinking.py::test_grok_43_profile_thinking_support asserts the 4.3 effort set specifically — don't add a different-effort model to it.
• env / probing: XAI_API_KEY lives in the repo-root .env (not in every worktree). Run probes with source .env && <script> so $XAI_API_KEY is exported; put any curl referencing it in a script file rather than passing the key inline. Verify enumeration/profile logic with a plain uv run python snippet (recurse get_args(XaiModelName), compare to known_model_names(); call grok_model_profile(...) directly) rather than a full uv run pytest tests/ run.

Google, Bedrock, others

Not yet documented here. When you add the next model for one of these providers, add the landmines you encountered to this section before closing the session (see Step 9).

Step 9 — Update this skill

After completing the model-add, before closing the session: if anything came up that isn't already documented in this skill — a new test that ratcheted, a provider-specific dispatch tuple, a misleading SDK behavior, a corrected misconception, an iteration the user had to walk you through — add it to this SKILL.md.

Specifically:

• Provider-specific landmines → the matching subsection (or create it).
• Generic process gaps → the relevant numbered step.
• Workflow shape errors → restructure the steps.

This skill exists to compound learnings. A model-add that surfaced new friction and didn't update this file wasted that friction.