From ed43c74bfa9ad8367e274fc5ee1b150d9219c77e Mon Sep 17 00:00:00 2001
From: Priyanka Sadam <priyasadam1218@gmail.com>
Date: Mon, 10 Aug 2026 04:20:36 +0000
Subject: [PATCH] fix(cost_calculator): return (0.0, 0.0) for unknown openai
 model instead of crashing

When cost_per_token() is called with custom_llm_provider='openai' and an
unrecognized model name, openai_cost_per_token() raises an Exception instead
of gracefully returning (0.0, 0.0).

Wraps the openai_cost_per_token() call in a try/except so unknown models
fall back to (0.0, 0.0), consistent with how other branches handle this.

Adds a regression test to confirm the behavior.

Ref: https://github.com/BerriAI/litellm/pull/27587
---
 litellm/cost_calculator.py                 | 19 +++++++++++++------
 tests/test_litellm/test_cost_calculator.py | 19 +++++++++++++++++++
 2 files changed, 32 insertions(+), 6 deletions(-)

diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index 6b6653c5646..1ebfe4b8bdf 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -599,12 +599,19 @@ def cost_per_token(
     elif custom_llm_provider == "bedrock":
         return bedrock_cost_per_token(model=model, usage=usage_block, service_tier=service_tier)
     elif custom_llm_provider == "openai":
-        return openai_cost_per_token(
-            model=model,
-            usage=usage_block,
-            service_tier=service_tier,
-            data_residency=data_residency,
-        )
+        try:
+            return openai_cost_per_token(
+                model=model,
+                usage=usage_block,
+                service_tier=service_tier,
+                data_residency=data_residency,
+            )
+        except Exception:
+            verbose_logger.debug(
+                "cost_per_token: unknown model=%s, custom_llm_provider=openai. Returning 0.0, 0.0",
+                model,
+            )
+            return 0.0, 0.0
     elif custom_llm_provider == "databricks":
         return databricks_cost_per_token(model=model, usage=usage_block)
     elif custom_llm_provider == "fireworks_ai":
diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py
index 3f024e2fd03..6eb3b6ea9ed 100644
--- a/tests/test_litellm/test_cost_calculator.py
+++ b/tests/test_litellm/test_cost_calculator.py
@@ -3530,3 +3530,22 @@ def test_completion_cost_prices_anthropic_shaped_cache_read_tokens():
     )
 
     assert cost == pytest.approx(3 * 5e-6 + 4014 * 5e-7 + 5 * 3e-5, rel=1e-9)
+def test_cost_per_token_unknown_model_with_explicit_provider():
+    """
+    Test: cost_per_token() with a known provider but unknown model.
+    This is the second guard point flagged in PR #27587 review.
+    A fake model name with explicit custom_llm_provider="openai"
+    should not crash — it should return (0.0, 0.0).
+    
+    Ref: https://github.com/BerriAI/litellm/pull/27587
+    """
+    import litellm
+    from litellm import cost_per_token
+
+    result = cost_per_token(
+        model="fake-model-xyz-123",
+        custom_llm_provider="openai",
+        prompt_tokens=10,
+        completion_tokens=5,
+    )
+    assert result == (0.0, 0.0), f"Expected (0.0, 0.0) for unknown model, got {result}"