diff --git a/src/strands/models/bedrock.py b/src/strands/models/bedrock.py
index d535bbc51..7444cfc70 100644
--- a/src/strands/models/bedrock.py
+++ b/src/strands/models/bedrock.py
@@ -54,6 +54,15 @@
     "anthropic.claude",
 ]
 
+# Cache of model IDs that do not support the CountTokens API.
+_UNSUPPORTED_COUNT_TOKENS_MODELS: set[str] = set()
+
+
+def _clear_unsupported_count_tokens_cache() -> None:
+    """Clear the cache of model IDs that do not support the CountTokens API."""
+    _UNSUPPORTED_COUNT_TOKENS_MODELS.clear()
+
+
 T = TypeVar("T", bound=BaseModel)
 
 DEFAULT_READ_TIMEOUT = 120
@@ -784,6 +793,11 @@ async def count_tokens(
         Returns:
             Total input token count.
         """
+        model_id: str = self.config["model_id"]
+
+        if model_id in _UNSUPPORTED_COUNT_TOKENS_MODELS:
+            return await super().count_tokens(messages, tool_specs, system_prompt, system_prompt_content)
+
         try:
             if system_prompt and system_prompt_content is None:
                 system_prompt_content = [{"text": system_prompt}]
@@ -810,11 +824,23 @@ async def count_tokens(
             logger.debug("model_id=<%s>, total_tokens=<%d> | native token count", self.config["model_id"], total_tokens)
             return total_tokens
         except Exception as e:
-            logger.debug(
-                "model_id=<%s>, error=<%s> | native token counting failed, falling back to estimation",
-                self.config["model_id"],
-                e,
-            )
+            if (
+                isinstance(e, ClientError)
+                and e.response.get("Error", {}).get("Code") == "ValidationException"
+                and "doesn't support counting tokens" in str(e)
+            ):
+                logger.debug(
+                    "model_id=<%s> | model does not support CountTokens, caching for future calls,"
+                    " falling back to estimation",
+                    model_id,
+                )
+                _UNSUPPORTED_COUNT_TOKENS_MODELS.add(model_id)
+            else:
+                logger.debug(
+                    "model_id=<%s>, error=<%s> | native token counting failed, falling back to estimation",
+                    model_id,
+                    e,
+                )
             return await super().count_tokens(messages, tool_specs, system_prompt, system_prompt_content)
 
     @override
diff --git a/tests/strands/models/test_bedrock.py b/tests/strands/models/test_bedrock.py
index a80ca091e..5da399f40 100644
--- a/tests/strands/models/test_bedrock.py
+++ b/tests/strands/models/test_bedrock.py
@@ -19,6 +19,7 @@
     DEFAULT_BEDROCK_MODEL_ID,
     DEFAULT_BEDROCK_REGION,
     DEFAULT_READ_TIMEOUT,
+    _clear_unsupported_count_tokens_cache,
 )
 from strands.types.exceptions import ContextWindowOverflowException, ModelThrottledException
 from strands.types.tools import ToolSpec
@@ -3293,6 +3294,12 @@ async def test_non_streaming_citations_with_only_location(bedrock_client, model,
 class TestCountTokens:
     """Tests for BedrockModel.count_tokens native token counting."""
 
+    @pytest.fixture(autouse=True)
+    def clean_cache(self):
+        _clear_unsupported_count_tokens_cache()
+        yield
+        _clear_unsupported_count_tokens_cache()
+
     @pytest.fixture
     def model_with_client(self, bedrock_client, model_id):
         _ = bedrock_client
@@ -3409,3 +3416,31 @@ async def test_fallback_logs_debug(self, model_with_client, bedrock_client, mess
             await model_with_client.count_tokens(messages=messages)
 
         assert any("native token counting failed" in record.message for record in caplog.records)
+
+    @pytest.mark.asyncio
+    async def test_caches_model_id_when_count_tokens_unsupported(self, bedrock_client, messages):
+        model = BedrockModel(model_id="unsupported-cache-test-model")
+        bedrock_client.count_tokens.side_effect = ClientError(
+            {"Error": {"Code": "ValidationException", "Message": "The provided model doesn't support counting tokens"}},
+            "CountTokens",
+        )
+
+        # First call: hits API, gets error, caches
+        await model.count_tokens(messages=messages)
+        assert bedrock_client.count_tokens.call_count == 1
+
+        # Second call: skips API entirely
+        await model.count_tokens(messages=messages)
+        assert bedrock_client.count_tokens.call_count == 1
+
+    @pytest.mark.asyncio
+    async def test_does_not_cache_model_id_for_other_errors(self, bedrock_client, messages):
+        model = BedrockModel(model_id="transient-error-test-model")
+        bedrock_client.count_tokens.side_effect = RuntimeError("Transient network error")
+
+        await model.count_tokens(messages=messages)
+        assert bedrock_client.count_tokens.call_count == 1
+
+        # Second call should still attempt the API
+        await model.count_tokens(messages=messages)
+        assert bedrock_client.count_tokens.call_count == 2