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fix(utils): resolve bedrock regional inference profiles to regional pricing in get_model_info (LIT-4056) - #32389

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fix(utils): resolve bedrock regional inference profiles to regional pricing in get_model_info (LIT-4056)#32389
mateo-berri merged 2 commits into
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litellm_lit4056_bedrock_regional_model_info

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@mateo-berri mateo-berri commented Jul 7, 2026

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Relevant issues

Linear ticket

Resolves LIT-4056

Pre-Submission checklist

Please complete all items before asking a LiteLLM maintainer to review your PR

  • I have added meaningful tests
  • My PR passes all CI/CD checks (e.g., lint, format, unit tests)
  • My PR's scope is as isolated as possible; it only solves 1 specific problem
  • I have received a Greptile Confidence Score of at least 4/5 before requesting a maintainer review (Greptile reviews automatically once the PR is opened; only comment @greptileai to re-request a review after pushing changes)

Screenshots / Proof of Fix

Live proxy on localhost with no mocks and no database, started from a clean worktree venv with LITELLM_LOCAL_MODEL_COST_MAP=True and LITELLM_MASTER_KEY=sk-qa-lit4056, using this qa_config.yaml, which mirrors the deployment shape from the ticket:

model_list:
  - model_name: claude-opus-4-8
    litellm_params:
      model: bedrock/au.anthropic.claude-opus-4-8
      aws_region_name: ap-southeast-2

general_settings:
  master_key: os.environ/LITELLM_MASTER_KEY

Both runs use the exact same startup and curl commands on the same port; the only difference is the checked-out commit

.venv/bin/python litellm/proxy/proxy_cli.py --config qa_config.yaml --port 59668
curl -s "http://127.0.0.1:59668/v1/model/info" -H "Authorization: Bearer sk-qa-lit4056" | jq '.data[] | {model_name, litellm_model: .litellm_params.model, key: .model_info.key, input_cost_per_token: .model_info.input_cost_per_token, output_cost_per_token: .model_info.output_cost_per_token}'

Before (base commit ff6dc33)

The AU regional inference profile resolves to the region-stripped base cost-map entry and shows base pricing

{
  "model_name": "claude-opus-4-8",
  "litellm_model": "bedrock/au.anthropic.claude-opus-4-8",
  "key": "anthropic.claude-opus-4-8",
  "input_cost_per_token": 0.000005,
  "output_cost_per_token": 0.000025
}

After (PR head commit dadf926)

The same deployment resolves to the exact regional cost-map entry with the AU premium pricing

{
  "model_name": "claude-opus-4-8",
  "litellm_model": "bedrock/au.anthropic.claude-opus-4-8",
  "key": "au.anthropic.claude-opus-4-8",
  "input_cost_per_token": 0.0000055,
  "output_cost_per_token": 0.0000275
}

Type

🐛 Bug Fix

Changes

get_model_info resolved Bedrock regional inference profiles configured with the standard bedrock/ routing prefix (including bedrock/converse/ and bedrock/invoke/) to the region-stripped base cost-map entry instead of the exact regional entry. For example bedrock/au.anthropic.claude-opus-4-8 returned key anthropic.claude-opus-4-8 at $5.00/$25.00 per 1M tokens instead of au.anthropic.claude-opus-4-8 at $5.50/$27.50, so /v1/model/info and the Admin UI model overview showed base pricing for premium-priced regional profiles. The unprefixed form (model: au.anthropic.claude-opus-4-8 with custom_llm_provider: bedrock) already resolved correctly, so the two config spellings of the same deployment disagreed

Root cause: _get_model_info_helper tries lookup candidates in a fixed order and placed split_model (the exact name after the provider prefix) last, after the region-stripped candidates produced by _strip_model_name. For Bedrock, stripping removes the cross-region prefix (us., eu., au., and so on), so the base entry always matched first whenever the model carried the bedrock/ prefix. For the converse/ and invoke/ route forms, split_model additionally kept the route prefix, so the exact regional key was never even a candidate

The fix tries the exact split_model candidate before the stripped fallbacks in both _get_model_info_helper and _get_model_info_from_generalization, and normalizes Bedrock route prefixes off split_model in _get_potential_model_names. The existing provider-match guard keeps split_model hits from leaking across providers, and the region-stripped fallback still applies for regional profiles that have no dedicated cost-map entry, for example bedrock/jp.anthropic.claude-opus-4-8 still resolves to the base entry. Runtime spend tracking through cost_per_token was already correct because completion flows pass the unprefixed model plus provider; this change aligns get_model_info with it

Regression tests in tests/test_litellm/test_utils.py cover the plain, converse/, and invoke/ prefixed forms resolving to the regional entry, the unprefixed control form, and the base-entry fallback for a regional profile without a dedicated entry

One intended side effect: a doubled provider prefix such as bedrock/bedrock/us.anthropic.claude-sonnet-4-6 now resolves too, because the second bedrock/ is treated as the routing prefix it already is at request time (strip_bedrock_routing_prefix removes it before the call is sent), so model info now matches what the request actually bills as. test_register_model_inherits_builtin_cache_pricing_for_unmapped_key relied on that shape being unresolvable to exercise the register_model fallback path, so its fixture moved to a triple prefix, which still cannot resolve; the new resolution behavior is locked in by test_get_model_info_bedrock_double_provider_prefix_resolves

@greptile-apps

greptile-apps Bot commented Jul 7, 2026

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Greptile Summary

This PR fixes get_model_info incorrectly resolving Bedrock regional inference profiles (e.g. bedrock/au.anthropic.claude-opus-4-8) to the region-stripped base cost-map entry instead of the exact regional entry, causing /v1/model/info and the Admin UI to show base pricing when premium-priced AU/EU/US regional profiles were configured with the bedrock/ routing prefix.

  • The lookup order in both _get_model_info_helper and _get_model_info_from_generalization is changed so that split_model (the exact name after stripping the provider prefix) is tried before the stripped fallbacks.
  • A new Bedrock-specific block in _get_potential_model_names calls strip_bedrock_routing_prefix to normalize routing sub-prefixes (converse/, invoke/) off split_model, ensuring all three call forms resolve to the same entry.
  • The fixture in test_register_model_inherits_builtin_cache_pricing_for_unmapped_key is updated from a double-prefix to a triple-prefix shape because double-prefix keys now resolve correctly, and the test needs a genuinely unresolvable key to exercise the fallback path; the new resolution behavior is locked in by an added test.

Confidence Score: 5/5

Safe to merge — the change only affects the model-info lookup order and does not touch any request-execution or billing paths at runtime.

The fix is narrowly scoped to get_model_info resolution order. The fallback chain is preserved (unmapped regional profiles still fall through to the base entry), the provider-match guard prevents cross-provider leakage, and the modified test fixture is demonstrably necessary because the double-prefix shape now resolves by design. The new tests cover the three routing-prefix forms, the fallback case, and the double-prefix case.

No files require special attention.

Important Files Changed

Filename Overview
litellm/utils.py Lookup order in _get_model_info_helper and _get_model_info_from_generalization reordered to try split_model before stripped fallbacks; Bedrock routing-prefix normalization added to _get_potential_model_names. Logic is correct and follows the existing dispatch pattern.
tests/test_litellm/test_utils.py Three new regression tests added covering plain, converse/, and invoke/ prefixed forms, the fallback for unmapped regional profiles, and the double-prefix resolution case. All use local_model_cost_map fixture — no real network calls.
tests/test_litellm/test_register_model_custom_pricing.py Fixture key bumped from bedrock/bedrock/ to bedrock/bedrock/bedrock/ so the test still exercises the unresolvable-key fallback path; docstring updated to match. The change is intentional and the new behavior is locked in by a separate added test.

Reviews (2): Last reviewed commit: "test(register_model): use a triple provi..." | Re-trigger Greptile

Comment thread litellm/utils.py
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Codecov Report

✅ All modified and coverable lines are covered by tests.

📢 Thoughts on this report? Let us know!

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Merging this PR will not alter performance

✅ 30 untouched benchmarks


Comparing litellm_lit4056_bedrock_regional_model_info (a4cee67) with litellm_internal_staging (4b0ac8b)

Open in CodSpeed

…e-key fixture

get_model_info now resolves bedrock/bedrock/... like a routing prefix, so the
double-prefix fixture stopped exercising the register_model fallback path.
Lock the new double-prefix resolution in as a model-info regression test
@mateo-berri

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@greptileai

@mateo-berri
mateo-berri merged commit 734fd29 into litellm_internal_staging Jul 8, 2026
125 checks passed
@mateo-berri
mateo-berri deleted the litellm_lit4056_bedrock_regional_model_info branch July 8, 2026 03:49
yuneng-berri added a commit that referenced this pull request Jul 11, 2026
…1.92.0 stable cut (#32959)

* fix(utils): resolve bedrock regional inference profiles to regional pricing in get_model_info (LIT-4056) (#32389)

* fix(utils): resolve bedrock regional inference profiles to regional pricing in get_model_info (LIT-4056)

* test(register_model): use a triple provider prefix as the unresolvable-key fixture

get_model_info now resolves bedrock/bedrock/... like a routing prefix, so the
double-prefix fixture stopped exercising the register_model fallback path.
Lock the new double-prefix resolution in as a model-info regression test

(cherry picked from commit 734fd29)

* fix(guardrails): walk Responses-API text taxonomy in shared content helpers (#32542)

* fix(guardrails): walk Responses-API text taxonomy in shared content helpers

Every guardrail sharing litellm/proxy/guardrails/_content_utils.py silently
drops all text on the /v1/responses path. AIM turns it into a loud 422 (
{"error":"No messages in the request"}); every other guardrail (Lakera v2,
Cato, Lasso, Repello, IBM, Azure Content Safety, enterprise secret
detection) scans an empty payload and lets the request through unscanned.

Three defects, all in _content_utils.py:

1. _iter_text_parts_in_content recognised only part.type == "text", but the
   Responses API uses input_text (request) and output_text (assistant).
2. _coerce_input_to_messages gated on "every item has a role key"; any
   Responses input list containing a function_call or function_call_output
   item failed the check and was wrapped as one opaque blob.
3. build_inspection_messages forwarded any role through, including a bare
   tool role missing tool_call_id, which validators like AIM's /fw/v1/analyze
   reject with a schema error.

Fix walks the actual Responses item taxonomy (message, function_call,
function_call_output, bare content parts and strings), recognises
{text, input_text, output_text} everywhere, and coerces any role outside
{system, user, assistant} to user in the outbound inspection payload.

* style: ruff-format changed guardrail files

* test(guardrails): cover function_call_output string form; drop em-dash in new docstring

* fix(guardrails): map function_call_output straight to user role

Avoids ever materialising a schema-invalid bare tool message. The
downstream role-safety coercion in build_inspection_messages still
guards genuinely caller-supplied non-standard roles (developer,
function, custom values); add a regression test covering that path
so the coercion has real coverage after this simplification.

* test(guardrails): pin chat-completions tool-role coercion in build_inspection_messages

* docs(test): soften AIM-specific claims in LIT-4294 test docstrings

Ryan's review flagged that several test docstrings assert AIM's
/fw/v1/analyze validates + rejects specific schema violations. That
behavior is customer-reported in the LIT-4294 writeup, not directly
verified by us. Rephrase to attribute the AIM 422 to the customer's
writeup and describe the underlying constraint as the OpenAI chat
schema; any downstream API that validates against that schema rejects
the same shape.

* refactor(guardrails): move unsupported-role coercion into AIM only

The generic coercion in build_inspection_messages collapsed any role
outside {system, user, assistant} to user for every caller of the
helper. Combined with the pre-existing apply_redacted_messages_back
write-back behavior in Lakera/AIM/Cato, that turned a loud OpenAI 400
on chat-completions tool-message masking into a silent semantic
corruption of the outbound request (role tool with tool_call_id got
rewritten to bare role user, dropping the assistant + tool_calls
sibling).

AIM specifically requires the coercion because its /fw/v1/analyze
validates the payload against the OpenAI chat schema; other guardrails
either do not validate roles or do their own reconstruction. Move the
coercion to AimGuardrail._build_aim_inspection_messages so the shared
helper keeps caller roles intact and no new cross-guardrail role
corruption is introduced. The pre-existing apply_redacted_messages_back
structural flatten remains as separate follow-up work.

function_call_output items still synthesise role user in the shared
helper because they have no natural role field, which is a different
concern from coercing a caller-supplied role.

* refactor(guardrails): preserve role fidelity in shared _content_utils

Shared inspection helpers should extract text and preserve semantic
role signals; role coercion for third-party schema safety stays inside
the guardrail that needs it (AIM).

Three shared-helper changes:
- Bare content-part dicts (input_text/output_text) with an explicit role
  keep it; only role-less parts default to user.
- Responses message items already had their role preserved; the
  behavior is now covered by an explicit test.
- function_call_output items default to role tool (semantic equivalent
  of the chat-completions tool message shape) instead of role user, so
  Responses and chat completions produce symmetric inspection payloads.
  A caller-supplied role on the item is still preserved.

AIM's schema-safe coercion in _build_aim_inspection_messages already
handles the resulting role tool: it collapses to user before the POST
to /fw/v1/analyze so AIM's OpenAI-schema validator does not reject the
bare tool message (no tool_call_id can survive the flatten). Added a
regression test in test_aim.py covering that path.

(cherry picked from commit e84a19a)

* feat: add Meta Model API provider and muse-spark-1.1 (day-0) (#32701)

(cherry picked from commit d82645d)

* fix(bedrock): keep mid-conversation system messages in place for Claude Invoke (#32578)

Hoisting every role system entry into the top-level system field mutates
the cache prefix whenever a client such as Claude Code appends a new
mid-conversation system message, invalidating the prompt cache for the
entire message history on Bedrock Invoke. Bedrock only rejects a system
entry at messages.0, so hoist just the leading run and forward the rest
in place

(cherry picked from commit cc36d54)

* feat(otel): emit the gen_ai.client.operation.exception event on failed LLM calls (#32655)

* feat(otel): emit the gen_ai.client.operation.exception event on failed LLM calls

The GenAI semantic conventions record failures of a GenAI client operation as
a log-based event named gen_ai.client.operation.exception, carrying the
exception.type / exception.message / exception.stacktrace trio at severity
WARN and correlated to the failed span. OTel v2 never emitted it: a failed LLM
call produced only the deprecated error.* span attributes, a generic exception
span event without a stacktrace, and the stacktrace under the vendor key
litellm.provider.error.stack_trace.

Build the logs pipeline (LoggerProvider + console/OTLP log exporters mirroring
the metrics plumbing) and record the event behind the enable_events flag, which
until now was defined but consumed nowhere. An operator-configured LoggerProvider
global is reused so the events ride their existing logs pipeline; an explicit
NoOpLoggerProvider global is honored as an opt-out and builds no recorder at all.

The existing span-side error surface (error.type, error.message, the exception
span event, and the litellm.provider.error.* detail keys) is untouched for
backwards compatibility.

* fix(otel): always ride the semconv-required exception pair on the GenAI event

Filtering the event attributes on truthiness conflated "absent" with "empty",
so an empty exception.type or exception.message would have been dropped, leaving
an event with neither semconv-required field. Build the attributes so the pair is
unconditional and only the recommended stacktrace is omitted when the payload
carries none.

* docs(otel): document the events plumbing module in the package README

* test(otel): cover the log exporter selection and logs endpoint normalization

The new logs plumbing had no coverage for exporter-kind selection, the
console fallback for an unrecognized kind, the /v1/logs signal-path rewriting
that lets one OTEL_ENDPOINT serve every signal, or the simple-vs-batch
processor split.

(cherry picked from commit 99b4c5e)

* fix(bedrock): gate in-place system role messages on model support for Claude Invoke (#32831)

* fix(bedrock): gate in-place system role messages on model support for Claude Invoke

* feat(bedrock): default unmapped Claude 4.8+ to in-place system role handling via fallback rule

(cherry picked from commit 5e23a5a)

* fix(anthropic): translate adaptive thinking/effort to pre-4.6 model support (#32867)

* fix(anthropic): translate adaptive thinking/effort to pre-4.6 model support

AnthropicMessagesConfig now reshapes the 4.6+ adaptive-thinking interface
(thinking:{type:adaptive} + output_config:{effort:...}) to whatever the routed
model supports. Thinking-capable non-adaptive models (e.g. Haiku 4.5, Sonnet 4.5)
get the effort translated to a legacy thinking budget_tokens. Models with no
reasoning support have thinking/effort dropped under drop_params. And because
adaptive thinking carries no budget while the legacy form must satisfy Anthropic's
max_tokens > budget_tokens rule, the translated budget is capped below max_tokens,
dropping thinking when max_tokens can't fit the minimum budget. 4.6+ models pass
through untouched.

This matters because clients like Claude Code speak native Anthropic /v1/messages
and send the adaptive interface unconditionally, regardless of the routed model.
The native passthrough previously only capability-gated the OpenAI-style
reasoning_effort alias and forwarded native output_config/adaptive thinking raw, so
a pre-4.6 model rejected it with "This model does not support the effort parameter"
and the request failed. Claude Code already gets drop_params auto-set, so its
requests now succeed.

* test(anthropic): gate undersized-max_tokens thinking drop on drop_params; add edge tests

Addresses review feedback on the max_tokens-too-small branch. Previously a
thinking-capable model whose max_tokens could not fit the minimum thinking budget
had thinking silently dropped regardless of drop_params, while a residual
output_config field in the same call still raised when drop_params was off. Gate
both consistently on drop_params: raise a clear error (naming max_tokens for the
undersized case) when drop_params is off, drop otherwise. Claude Code gets
drop_params auto-set, so it still succeeds.

Adds tests for the undersized-max_tokens raise, the residual output_config raise,
and the no-adaptive-interface passthrough on a non-adaptive model.

* fix(anthropic): make adaptive-effort translation silent to avoid breaking provider strip contracts

The previous raise-when-not-drop_params behavior broke existing bedrock and vertex
messages tests: those providers already silently strip unsupported output_config
for pre-4.6 models (issue #22797) with no drop_params required, and the shared
parent transform raising pre-empted that. It also conflicted with the goal of
keeping requests working rather than failing them.

Make the reshape silent: translate effort to legacy thinking for thinking-capable
models, drop thinking for non-reasoning models, and remove only the consumed effort
key from output_config, leaving any residual (e.g. format) for provider subclasses
(bedrock/vertex) to handle. No raise, no drop_params gating. This also resolves the
review note about inconsistent drop_params handling by making every path uniform.

Updates the tests to assert the silent behavior and residual output_config
preservation.

* fix(anthropic): handle output_config-capable but non-adaptive models (Opus 4.5)

Greptile caught a real bug: the early-return guard treated supports_output_config
as equivalent to supporting adaptive thinking. Claude Opus 4.5 advertises
supports_output_config (it accepts output_config.effort) but is not adaptive, so it
rejects thinking:{type:adaptive} with "adaptive thinking is not supported on this
model". The guard early-returned for Opus 4.5 and forwarded the adaptive thinking
block raw, reproducing the exact failure the fix is meant to prevent.

thinking:{type:adaptive} and output_config.effort are independent capabilities.
Only early-return for adaptive-thinking models. For a model that supports
output_config.effort but is not adaptive, keep the native effort and drop only the
unsupported adaptive thinking block. Verified live against Opus 4.5: the Claude Code
payload now returns 200 instead of 400.

Adds regression tests for Opus 4.5 with and without adaptive thinking.

* fix(anthropic): translate adaptive thinking for effort-capable pre-4.6 models

Claude Opus 4.5 advertises supports_output_config but not adaptive thinking,
so the early-return guard forwarded thinking.type=adaptive raw and Anthropic
rejected it. The guard now only skips true adaptive models; effort-only
requests on effort-capable models still pass through untouched. The
_map_reasoning_effort call is wrapped to surface unrecognized effort values
as a clean 400, matching _translate_reasoning_effort_to_anthropic

* fix(anthropic): fall back to legacy thinking when effort level unsupported

Opus 4.5 accepts output_config.effort but only low/medium/high; Claude Code
defaults to xhigh on newer models, so preserving that level raw gets rejected
by Anthropic. Gate the native-effort passthrough on _validate_effort_for_model
and fall through to the budget translation for unsupported levels

* fix(anthropic): keep effort-only requests untouched for provider normalization

The xhigh fall-through consumed effort-only requests on effort-capable
models, breaking bedrock invoke's own normalization which clamps xhigh to
the model's ceiling after the base transform runs
(test_bedrock_messages_normalizes_output_config_effort_for_opus). Restrict
the fall-through to requests that carry adaptive thinking; effort-only
requests pass through so provider subclasses keep owning level clamping

---------

Co-authored-by: Abhimanyu Kapur <38531241+akapur99@users.noreply.github.com>
(cherry picked from commit 3a62e54)

* fix(bedrock): flag mapped Claude 4.8+ entries with supports_mid_conversation_system (#32882)

Exact cost-map hits resolve before fallback-generalization rules, so the
mapped Sonnet 5, Fable 5 and jp Opus 4.8 Bedrock entries bypassed the
bedrock-anthropic-claude-mid-conversation-system rule and hoisted
mid-conversation system messages, invalidating the prompt cache.

(cherry picked from commit c15891f)

* Merge pull request #32873 from BerriAI/litellm_fallback_rules_routing_split

refactor(fallback-generalizations): split rules into routing and provider-neutral capability kinds

(cherry picked from commit 45d3644)

* Merge pull request #32874 from BerriAI/litellm_thread_provider_capability_probes

fix(anthropic): thread real provider through capability probes instead of pinning anthropic

(cherry picked from commit ead7ad3)

* test: add /v1/messages to supported_endpoints schema enum (#32739)

(cherry picked from commit bf02a4a)

---------

Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: yucheng-berri <yucheng@berri.ai>
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
Co-authored-by: Abhimanyu Kapur <38531241+akapur99@users.noreply.github.com>
Co-authored-by: tin-berri <tin@berri.ai>
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