fix: set use_native_token_count default to false

src/strands/models/anthropic.py

@@ -58,8 +58,8 @@ class AnthropicConfig(BaseModelConfig, total=False):
             params: Additional model parameters (e.g., temperature).
                 For a complete list of supported parameters, see https://docs.anthropic.com/en/api/messages.
             use_native_token_count: Whether to use the native Anthropic count_tokens API.
-                When True (default), count_tokens() calls the Anthropic API for accurate counts.
-                When False, skips the API call and uses the local estimator.
+                When True, count_tokens() calls the Anthropic API for accurate counts.
+                When False (default), skips the API call and uses the local estimator.
         """
 
         max_tokens: Required[int]
@@ -398,7 +398,7 @@ async def count_tokens(
         Returns:
             Total input token count.
         """
-        if self.config.get("use_native_token_count") is False:
+        if self.config.get("use_native_token_count") is not True:
             return await super().count_tokens(messages, tool_specs, system_prompt, system_prompt_content)
 
         try:

src/strands/models/bedrock.py

@@ -118,8 +118,8 @@ class BedrockConfig(BaseModelConfig, total=False):
             temperature: Controls randomness in generation (higher = more random)
             top_p: Controls diversity via nucleus sampling (alternative to temperature)
             use_native_token_count: Whether to use the native Bedrock CountTokens API.
-                When True (default), count_tokens() calls the Bedrock API for accurate counts.
-                When False, skips the API call and uses the local estimator.
+                When True, count_tokens() calls the Bedrock API for accurate counts.
+                When False (default), skips the API call and uses the local estimator.
         """
 
         additional_args: dict[str, Any] | None
@@ -798,7 +798,7 @@ async def count_tokens(
         Returns:
             Total input token count.
         """
-        if self.config.get("use_native_token_count") is False:
+        if self.config.get("use_native_token_count") is not True:
             return await super().count_tokens(messages, tool_specs, system_prompt, system_prompt_content)
 
         model_id: str = self.config["model_id"]

src/strands/models/gemini.py

@@ -50,8 +50,8 @@ class GeminiConfig(BaseModelConfig, total=False):
                 For a complete list of supported tools, see
                 https://ai.google.dev/api/caching#Tool
             use_native_token_count: Whether to use the native Gemini count_tokens API.
-                When True (default), count_tokens() calls the Gemini API for accurate counts.
-                When False, skips the API call and uses the local estimator.
+                When True, count_tokens() calls the Gemini API for accurate counts.
+                When False (default), skips the API call and uses the local estimator.
         """
 
         model_id: Required[str]
@@ -461,7 +461,7 @@ async def count_tokens(
         Returns:
             Total input token count.
         """
-        if self.config.get("use_native_token_count") is False:
+        if self.config.get("use_native_token_count") is not True:
             return await super().count_tokens(messages, tool_specs, system_prompt, system_prompt_content)
 
         try:

src/strands/models/llamacpp.py

@@ -126,8 +126,8 @@ class LlamaCppConfig(BaseModelConfig, total=False):
                 - slot_id: Slot ID for parallel inference
                 - samplers: Custom sampler order
             use_native_token_count: Whether to use the native llama.cpp /tokenize endpoint.
-                When True (default), count_tokens() calls the server's tokenize endpoint for accurate counts.
-                When False, skips the API call and uses the local estimator.
+                When True, count_tokens() calls the server's tokenize endpoint for accurate counts.
+                When False (default), skips the API call and uses the local estimator.
         """
 
         model_id: str
@@ -537,7 +537,7 @@ async def count_tokens(
         Returns:
             Total input token count.
         """
-        if self.config.get("use_native_token_count") is False:
+        if self.config.get("use_native_token_count") is not True:
             return await super().count_tokens(messages, tool_specs, system_prompt, system_prompt_content)
 
         try:

src/strands/models/openai_responses.py

@@ -137,8 +137,8 @@ class OpenAIResponsesConfig(BaseModelConfig, total=False):
                 When True, the server stores conversation history and the client does not need to
                 send the full message history with each request. Defaults to False.
             use_native_token_count: Whether to use the native OpenAI input_tokens.count API.
-                When True (default), count_tokens() calls the OpenAI API for accurate counts.
-                When False, skips the API call and uses the local estimator.
+                When True, count_tokens() calls the OpenAI API for accurate counts.
+                When False (default), skips the API call and uses the local estimator.
         """
 
         model_id: str
@@ -242,7 +242,7 @@ async def count_tokens(
         Returns:
             Total input token count.
         """
-        if self.config.get("use_native_token_count") is False:
+        if self.config.get("use_native_token_count") is not True:
             return await super().count_tokens(messages, tool_specs, system_prompt, system_prompt_content)
 
         try:

tests/strands/models/test_anthropic.py

@@ -1072,7 +1072,7 @@ class TestCountTokens:
     @pytest.fixture
     def model_with_client(self, anthropic_client, model_id, max_tokens):
         _ = anthropic_client
-        return AnthropicModel(model_id=model_id, max_tokens=max_tokens)
+        return AnthropicModel(model_id=model_id, max_tokens=max_tokens, use_native_token_count=True)
 
     @pytest.fixture
     def messages(self):
@@ -1175,3 +1175,14 @@ async def test_skip_native_api_when_use_native_token_count_false(
         anthropic_client.messages.count_tokens.assert_not_called()
         assert isinstance(result, int)
         assert result >= 0
+
+    @pytest.mark.asyncio
+    async def test_skip_native_api_by_default(self, anthropic_client, model_id, max_tokens, messages):
+        _ = anthropic_client
+        model = AnthropicModel(model_id=model_id, max_tokens=max_tokens)
+
+        result = await model.count_tokens(messages=messages)
+
+        anthropic_client.messages.count_tokens.assert_not_called()
+        assert isinstance(result, int)
+        assert result >= 0

tests/strands/models/test_bedrock.py

@@ -3343,7 +3343,7 @@ def clean_cache(self):
     @pytest.fixture
     def model_with_client(self, bedrock_client, model_id):
         _ = bedrock_client
-        return BedrockModel(model_id=model_id)
+        return BedrockModel(model_id=model_id, use_native_token_count=True)
 
     @pytest.fixture
     def messages(self):
@@ -3459,7 +3459,7 @@ async def test_fallback_logs_debug(self, model_with_client, bedrock_client, mess
 
     @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")
+        model = BedrockModel(model_id="unsupported-cache-test-model", use_native_token_count=True)
         bedrock_client.count_tokens.side_effect = ClientError(
             {"Error": {"Code": "ValidationException", "Message": "The provided model doesn't support counting tokens"}},
             "CountTokens",
@@ -3475,7 +3475,7 @@ async def test_caches_model_id_when_count_tokens_unsupported(self, bedrock_clien
 
     @pytest.mark.asyncio
     async def test_caches_model_id_when_access_denied(self, bedrock_client, messages):
-        model = BedrockModel(model_id="access-denied-cache-test-model")
+        model = BedrockModel(model_id="access-denied-cache-test-model", use_native_token_count=True)
         bedrock_client.count_tokens.side_effect = ClientError(
             {
                 "Error": {
@@ -3523,7 +3523,7 @@ async def test_access_denied_logs_warning_with_full_error(
 
     @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")
+        model = BedrockModel(model_id="transient-error-test-model", use_native_token_count=True)
         bedrock_client.count_tokens.side_effect = RuntimeError("Transient network error")
 
         await model.count_tokens(messages=messages)
@@ -3543,3 +3543,14 @@ async def test_skip_native_api_when_use_native_token_count_false(self, bedrock_c
         bedrock_client.count_tokens.assert_not_called()
         assert isinstance(result, int)
         assert result >= 0
+
+    @pytest.mark.asyncio
+    async def test_skip_native_api_by_default(self, bedrock_client, model_id, messages):
+        _ = bedrock_client
+        model = BedrockModel(model_id=model_id)
+
+        result = await model.count_tokens(messages=messages)
+
+        bedrock_client.count_tokens.assert_not_called()
+        assert isinstance(result, int)
+        assert result >= 0

tests/strands/models/test_gemini.py

@@ -1144,7 +1144,7 @@ def gemini_client(self):
     @pytest.fixture
     def model(self, gemini_client):
         _ = gemini_client
-        return GeminiModel(model_id="m1")
+        return GeminiModel(model_id="m1", use_native_token_count=True)
 
     @pytest.fixture
     def messages(self):
@@ -1239,3 +1239,14 @@ async def test_skip_native_api_when_use_native_token_count_false(self, gemini_cl
         gemini_client.aio.models.count_tokens.assert_not_called()
         assert isinstance(result, int)
         assert result >= 0
+
+    @pytest.mark.asyncio
+    async def test_skip_native_api_by_default(self, gemini_client, messages):
+        _ = gemini_client
+        model = GeminiModel(model_id="m1")
+
+        result = await model.count_tokens(messages=messages)
+
+        gemini_client.aio.models.count_tokens.assert_not_called()
+        assert isinstance(result, int)
+        assert result >= 0

tests/strands/models/test_llamacpp.py

@@ -713,7 +713,7 @@ class TestCountTokens:
 
     @pytest.fixture
     def model(self):
-        return LlamaCppModel(base_url="http://localhost:8080")
+        return LlamaCppModel(base_url="http://localhost:8080", use_native_token_count=True)
 
     @pytest.fixture
     def messages(self):
@@ -814,3 +814,14 @@ async def test_skip_native_api_when_use_native_token_count_false(self, messages)
         model.client.post.assert_not_called()
         assert isinstance(result, int)
         assert result >= 0
+
+    @pytest.mark.asyncio
+    async def test_skip_native_api_by_default(self, messages):
+        model = LlamaCppModel(base_url="http://localhost:8080")
+        model.client.post = AsyncMock()
+
+        result = await model.count_tokens(messages=messages)
+
+        model.client.post.assert_not_called()
+        assert isinstance(result, int)
+        assert result >= 0

tests/strands/models/test_openai_responses.py

@@ -1224,7 +1224,7 @@ def openai_client(self):
     @pytest.fixture
     def model(self, openai_client):
         _ = openai_client
-        return OpenAIResponsesModel(model_id="gpt-4o")
+        return OpenAIResponsesModel(model_id="gpt-4o", use_native_token_count=True)
 
     @pytest.fixture
     def messages(self):
@@ -1329,6 +1329,17 @@ async def test_skip_native_api_when_use_native_token_count_false(self, openai_cl
         assert isinstance(result, int)
         assert result >= 0
 
+    @pytest.mark.asyncio
+    async def test_skip_native_api_by_default(self, openai_client, messages):
+        _ = openai_client
+        model = OpenAIResponsesModel(model_id="gpt-4o")
+
+        result = await model.count_tokens(messages=messages)
+
+        openai_client.responses.input_tokens.count.assert_not_called()
+        assert isinstance(result, int)
+        assert result >= 0
+
 
 # =============================================================================
 # Bedrock Mantle (bedrock_mantle_config) integration with OpenAIResponsesModel