diff --git a/litellm/llms/fireworks_ai/cost_calculator.py b/litellm/llms/fireworks_ai/cost_calculator.py
index ed936f6233a..682adf5a8ff 100644
--- a/litellm/llms/fireworks_ai/cost_calculator.py
+++ b/litellm/llms/fireworks_ai/cost_calculator.py
@@ -75,10 +75,23 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]:
         model_info = get_model_info(model=base_model, custom_llm_provider="fireworks_ai")
 
     ## CALCULATE INPUT COST
+    prompt_tokens_details = usage.prompt_tokens_details
+    cached_tokens: int = (
+        prompt_tokens_details.cached_tokens
+        if prompt_tokens_details is not None and prompt_tokens_details.cached_tokens is not None
+        else 0
+    )
+    input_cost_per_token: float = model_info["input_cost_per_token"] or 0.0
+    cache_read_input_token_cost = model_info.get("cache_read_input_token_cost")
+    cache_read_cost_per_token: float = (
+        cache_read_input_token_cost if cache_read_input_token_cost is not None else input_cost_per_token
+    )
+    non_cached_prompt_tokens: int = max(usage.prompt_tokens - cached_tokens, 0)
 
-    prompt_cost: float = usage["prompt_tokens"] * model_info["input_cost_per_token"]
+    prompt_cost: float = non_cached_prompt_tokens * input_cost_per_token + cached_tokens * cache_read_cost_per_token
 
     ## CALCULATE OUTPUT COST
-    completion_cost = usage["completion_tokens"] * model_info["output_cost_per_token"]
+    output_cost_per_token: float = model_info["output_cost_per_token"] or 0.0
+    completion_cost: float = usage.completion_tokens * output_cost_per_token
 
     return prompt_cost, completion_cost
diff --git a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py
new file mode 100644
index 00000000000..99dcaa36c75
--- /dev/null
+++ b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py
@@ -0,0 +1,66 @@
+import os
+import sys
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../../../../.."))
+
+from litellm.llms.fireworks_ai.cost_calculator import cost_per_token
+from litellm.types.utils import PromptTokensDetailsWrapper, Usage
+
+MODEL = "accounts/fireworks/models/glm-5p2"
+INPUT_COST = 1.4e-06
+CACHE_READ_COST = 2.6e-07
+OUTPUT_COST = 4.4e-06
+
+
+def _usage(prompt_tokens: int, cached_tokens: int, completion_tokens: int) -> Usage:
+    return Usage(
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+        total_tokens=prompt_tokens + completion_tokens,
+        prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=cached_tokens),
+    )
+
+
+def test_cached_prompt_tokens_billed_at_cache_read_rate():
+    prompt_tokens = 7036
+    cached_tokens = 7020
+    completion_tokens = 8
+
+    prompt_cost, completion_cost = cost_per_token(
+        model=MODEL, usage=_usage(prompt_tokens, cached_tokens, completion_tokens)
+    )
+
+    expected_prompt_cost = (prompt_tokens - cached_tokens) * INPUT_COST + cached_tokens * CACHE_READ_COST
+    assert prompt_cost == pytest.approx(expected_prompt_cost)
+    assert completion_cost == pytest.approx(completion_tokens * OUTPUT_COST)
+
+    full_rate_cost = prompt_tokens * INPUT_COST
+    assert prompt_cost < full_rate_cost
+
+
+def test_warm_call_cheaper_than_cold_call():
+    prompt_tokens = 7036
+    completion_tokens = 8
+
+    cold_prompt_cost, _ = cost_per_token(
+        model=MODEL, usage=_usage(prompt_tokens, 16, completion_tokens)
+    )
+    warm_prompt_cost, _ = cost_per_token(
+        model=MODEL, usage=_usage(prompt_tokens, 7020, completion_tokens)
+    )
+
+    assert warm_prompt_cost < cold_prompt_cost
+
+
+def test_no_cached_tokens_matches_full_input_rate():
+    prompt_tokens = 100
+    completion_tokens = 10
+
+    prompt_cost, completion_cost = cost_per_token(
+        model=MODEL, usage=_usage(prompt_tokens, 0, completion_tokens)
+    )
+
+    assert prompt_cost == pytest.approx(prompt_tokens * INPUT_COST)
+    assert completion_cost == pytest.approx(completion_tokens * OUTPUT_COST)