refactor: removed method from provider, should use model capabilities instead

refactor: cleanup temperature factory method

docs/adding_providers.md

@@ -15,7 +15,7 @@ Each provider:
 **Option A: Full Provider (`ModelProvider`)**
 - For APIs with unique features or custom authentication
 - Complete control over API calls and response handling
-- Required methods: `generate_content()`, `count_tokens()`, `get_capabilities()`, `validate_model_name()`, `supports_thinking_mode()`, `get_provider_type()`
+- Required methods: `generate_content()`, `count_tokens()`, `get_capabilities()`, `validate_model_name()`, `get_provider_type()`
 
 **Option B: OpenAI-Compatible (`OpenAICompatibleProvider`)**
 - For APIs that follow OpenAI's chat completion format
@@ -130,10 +130,6 @@ class ExampleModelProvider(ModelProvider):
     def validate_model_name(self, model_name: str) -> bool:
         resolved_name = self._resolve_model_name(model_name)
         return resolved_name in self.MODEL_CAPABILITIES
-    
-    def supports_thinking_mode(self, model_name: str) -> bool:
-        capabilities = self.get_capabilities(model_name)
-        return capabilities.supports_extended_thinking
 ```
 
 #### Option B: OpenAI-Compatible Provider (Simplified)

providers/base.py

@@ -109,11 +109,6 @@ class ModelProvider(ABC):
             constraint_desc = capabilities.temperature_constraint.get_description()
             raise ValueError(f"Temperature {temperature} is invalid for model {model_name}. {constraint_desc}")
 
-    @abstractmethod
-    def supports_thinking_mode(self, model_name: str) -> bool:
-        """Check if the model supports extended thinking mode."""
-        pass
-
     def get_model_configurations(self) -> dict[str, ModelCapabilities]:
         """Get model configurations for this provider.
 

providers/custom.py

@@ -284,24 +284,6 @@ class CustomProvider(OpenAICompatibleProvider):
             **kwargs,
         )
 
-    def supports_thinking_mode(self, model_name: str) -> bool:
-        """Check if the model supports extended thinking mode.
-
-        Args:
-            model_name: Model to check
-
-        Returns:
-            True if model supports thinking mode, False otherwise
-        """
-        # Check if model is in registry
-        config = self._registry.resolve(model_name) if self._registry else None
-        if config and config.is_custom:
-            # Trust the config from custom_models.json
-            return config.supports_extended_thinking
-
-        # Default to False for unknown models
-        return False
-
     def get_model_configurations(self) -> dict[str, ModelCapabilities]:
         """Get model configurations from the registry.
 

providers/dial.py

@@ -7,12 +7,7 @@ import time
 from typing import Optional
 
 from .openai_compatible import OpenAICompatibleProvider
-from .shared import (
+from .shared import ModelCapabilities, ModelResponse, ProviderType, TemperatureConstraint
-    ModelCapabilities,
-    ModelResponse,
-    ProviderType,
-    create_temperature_constraint,
-)
 
 logger = logging.getLogger(__name__)
 
@@ -48,7 +43,7 @@ class DIALModelProvider(OpenAICompatibleProvider):
             supports_images=True,
             max_image_size_mb=20.0,
             supports_temperature=False,  # O3 models don't accept temperature
-            temperature_constraint=create_temperature_constraint("fixed"),
+            temperature_constraint=TemperatureConstraint.create("fixed"),
             description="OpenAI O3 via DIAL - Strong reasoning model",
             aliases=["o3"],
         ),
@@ -66,7 +61,7 @@ class DIALModelProvider(OpenAICompatibleProvider):
             supports_images=True,
             max_image_size_mb=20.0,
             supports_temperature=False,  # O4 models don't accept temperature
-            temperature_constraint=create_temperature_constraint("fixed"),
+            temperature_constraint=TemperatureConstraint.create("fixed"),
             description="OpenAI O4-mini via DIAL - Fast reasoning model",
             aliases=["o4-mini"],
         ),
@@ -84,7 +79,7 @@ class DIALModelProvider(OpenAICompatibleProvider):
             supports_images=True,
             max_image_size_mb=5.0,
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="Claude Sonnet 4.1 via DIAL - Balanced performance",
             aliases=["sonnet-4.1", "sonnet-4"],
         ),
@@ -102,7 +97,7 @@ class DIALModelProvider(OpenAICompatibleProvider):
             supports_images=True,
             max_image_size_mb=5.0,
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="Claude Sonnet 4.1 with thinking mode via DIAL",
             aliases=["sonnet-4.1-thinking", "sonnet-4-thinking"],
         ),
@@ -120,7 +115,7 @@ class DIALModelProvider(OpenAICompatibleProvider):
             supports_images=True,
             max_image_size_mb=5.0,
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="Claude Opus 4.1 via DIAL - Most capable Claude model",
             aliases=["opus-4.1", "opus-4"],
         ),
@@ -138,7 +133,7 @@ class DIALModelProvider(OpenAICompatibleProvider):
             supports_images=True,
             max_image_size_mb=5.0,
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="Claude Opus 4.1 with thinking mode via DIAL",
             aliases=["opus-4.1-thinking", "opus-4-thinking"],
         ),
@@ -156,7 +151,7 @@ class DIALModelProvider(OpenAICompatibleProvider):
             supports_images=True,
             max_image_size_mb=20.0,
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="Gemini 2.5 Pro with Google Search via DIAL",
             aliases=["gemini-2.5-pro-search"],
         ),
@@ -174,7 +169,7 @@ class DIALModelProvider(OpenAICompatibleProvider):
             supports_images=True,
             max_image_size_mb=20.0,
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="Gemini 2.5 Pro via DIAL - Deep reasoning",
             aliases=["gemini-2.5-pro"],
         ),
@@ -192,7 +187,7 @@ class DIALModelProvider(OpenAICompatibleProvider):
             supports_images=True,
             max_image_size_mb=20.0,
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="Gemini 2.5 Flash via DIAL - Ultra-fast",
             aliases=["gemini-2.5-flash"],
         ),

providers/gemini.py

@@ -12,12 +12,7 @@ from google import genai
 from google.genai import types
 
 from .base import ModelProvider
-from .shared import (
+from .shared import ModelCapabilities, ModelResponse, ProviderType, TemperatureConstraint
-    ModelCapabilities,
-    ModelResponse,
-    ProviderType,
-    create_temperature_constraint,
-)
 
 logger = logging.getLogger(__name__)
 
@@ -46,7 +41,7 @@ class GeminiModelProvider(ModelProvider):
             supports_images=True,  # Vision capability
             max_image_size_mb=32.0,  # Higher limit for Pro model
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             max_thinking_tokens=32768,  # Max thinking tokens for Pro model
             description="Deep reasoning + thinking mode (1M context) - Complex problems, architecture, deep analysis",
             aliases=["pro", "gemini pro", "gemini-pro"],
@@ -65,7 +60,7 @@ class GeminiModelProvider(ModelProvider):
             supports_images=True,  # Vision capability
             max_image_size_mb=20.0,  # Conservative 20MB limit for reliability
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             max_thinking_tokens=24576,  # Same as 2.5 flash for consistency
             description="Gemini 2.0 Flash (1M context) - Latest fast model with experimental thinking, supports audio/video input",
             aliases=["flash-2.0", "flash2"],
@@ -84,7 +79,7 @@ class GeminiModelProvider(ModelProvider):
             supports_images=False,  # Does not support images
             max_image_size_mb=0.0,  # No image support
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="Gemini 2.0 Flash Lite (1M context) - Lightweight fast model, text-only",
             aliases=["flashlite", "flash-lite"],
         ),
@@ -102,7 +97,7 @@ class GeminiModelProvider(ModelProvider):
             supports_images=True,  # Vision capability
             max_image_size_mb=20.0,  # Conservative 20MB limit for reliability
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             max_thinking_tokens=24576,  # Flash 2.5 thinking budget limit
             description="Ultra-fast (1M context) - Quick analysis, simple queries, rapid iterations",
             aliases=["flash", "flash2.5"],
@@ -397,11 +392,6 @@ class GeminiModelProvider(ModelProvider):
 
         return True
 
-    def supports_thinking_mode(self, model_name: str) -> bool:
-        """Check if the model supports extended thinking mode."""
-        capabilities = self.get_capabilities(model_name)
-        return capabilities.supports_extended_thinking
-
     def get_thinking_budget(self, model_name: str, thinking_mode: str) -> int:
         """Get actual thinking token budget for a model and thinking mode."""
         resolved_name = self._resolve_model_name(model_name)

providers/openai_compatible.py

@@ -734,13 +734,6 @@ class OpenAICompatibleProvider(ModelProvider):
         """
         pass
 
-    def supports_thinking_mode(self, model_name: str) -> bool:
-        """Check if the model supports extended thinking mode.
-
-        Default is False for OpenAI-compatible providers.
-        """
-        return False
-
     def _is_error_retryable(self, error: Exception) -> bool:
         """Determine if an error should be retried based on structured error codes.
 

providers/openai_provider.py

@@ -7,12 +7,7 @@ if TYPE_CHECKING:
     from tools.models import ToolModelCategory
 
 from .openai_compatible import OpenAICompatibleProvider
-from .shared import (
+from .shared import ModelCapabilities, ModelResponse, ProviderType, TemperatureConstraint
-    ModelCapabilities,
-    ModelResponse,
-    ProviderType,
-    create_temperature_constraint,
-)
 
 logger = logging.getLogger(__name__)
 
@@ -41,7 +36,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
             supports_images=True,  # GPT-5 supports vision
             max_image_size_mb=20.0,  # 20MB per OpenAI docs
             supports_temperature=True,  # Regular models accept temperature parameter
-            temperature_constraint=create_temperature_constraint("fixed"),
+            temperature_constraint=TemperatureConstraint.create("fixed"),
             description="GPT-5 (400K context, 128K output) - Advanced model with reasoning support",
             aliases=["gpt5"],
         ),
@@ -59,7 +54,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
             supports_images=True,  # GPT-5-mini supports vision
             max_image_size_mb=20.0,  # 20MB per OpenAI docs
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("fixed"),
+            temperature_constraint=TemperatureConstraint.create("fixed"),
             description="GPT-5-mini (400K context, 128K output) - Efficient variant with reasoning support",
             aliases=["gpt5-mini", "gpt5mini", "mini"],
         ),
@@ -77,7 +72,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
             supports_images=True,
             max_image_size_mb=20.0,
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("fixed"),
+            temperature_constraint=TemperatureConstraint.create("fixed"),
             description="GPT-5 nano (400K context) - Fastest, cheapest version of GPT-5 for summarization and classification tasks",
             aliases=["gpt5nano", "gpt5-nano", "nano"],
         ),
@@ -95,7 +90,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
             supports_images=True,  # O3 models support vision
             max_image_size_mb=20.0,  # 20MB per OpenAI docs
             supports_temperature=False,  # O3 models don't accept temperature parameter
-            temperature_constraint=create_temperature_constraint("fixed"),
+            temperature_constraint=TemperatureConstraint.create("fixed"),
             description="Strong reasoning (200K context) - Logical problems, code generation, systematic analysis",
             aliases=[],
         ),
@@ -113,7 +108,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
             supports_images=True,  # O3 models support vision
             max_image_size_mb=20.0,  # 20MB per OpenAI docs
             supports_temperature=False,  # O3 models don't accept temperature parameter
-            temperature_constraint=create_temperature_constraint("fixed"),
+            temperature_constraint=TemperatureConstraint.create("fixed"),
             description="Fast O3 variant (200K context) - Balanced performance/speed, moderate complexity",
             aliases=["o3mini"],
         ),
@@ -131,7 +126,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
             supports_images=True,  # O3 models support vision
             max_image_size_mb=20.0,  # 20MB per OpenAI docs
             supports_temperature=False,  # O3 models don't accept temperature parameter
-            temperature_constraint=create_temperature_constraint("fixed"),
+            temperature_constraint=TemperatureConstraint.create("fixed"),
             description="Professional-grade reasoning (200K context) - EXTREMELY EXPENSIVE: Only for the most complex problems requiring universe-scale complexity analysis OR when the user explicitly asks for this model. Use sparingly for critical architectural decisions or exceptionally complex debugging that other models cannot handle.",
             aliases=["o3pro"],
         ),
@@ -149,7 +144,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
             supports_images=True,  # O4 models support vision
             max_image_size_mb=20.0,  # 20MB per OpenAI docs
             supports_temperature=False,  # O4 models don't accept temperature parameter
-            temperature_constraint=create_temperature_constraint("fixed"),
+            temperature_constraint=TemperatureConstraint.create("fixed"),
             description="Latest reasoning model (200K context) - Optimized for shorter contexts, rapid reasoning",
             aliases=["o4mini"],
         ),
@@ -167,7 +162,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
             supports_images=True,  # GPT-4.1 supports vision
             max_image_size_mb=20.0,  # 20MB per OpenAI docs
             supports_temperature=True,  # Regular models accept temperature parameter
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="GPT-4.1 (1M context) - Advanced reasoning model with large context window",
             aliases=["gpt4.1"],
         ),
@@ -303,13 +298,6 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
             **kwargs,
         )
 
-    def supports_thinking_mode(self, model_name: str) -> bool:
-        """Check if the model supports extended thinking mode."""
-        try:
-            return self.get_capabilities(model_name).supports_extended_thinking
-        except ValueError:
-            return False
-
     def get_preferred_model(self, category: "ToolModelCategory", allowed_models: list[str]) -> Optional[str]:
         """Get OpenAI's preferred model for a given category from allowed models.
 

providers/openrouter.py

@@ -204,20 +204,6 @@ class OpenRouterProvider(OpenAICompatibleProvider):
             **kwargs,
         )
 
-    def supports_thinking_mode(self, model_name: str) -> bool:
-        """Check if the model supports extended thinking mode.
-
-        Currently, no models via OpenRouter support extended thinking.
-        This may change as new models become available.
-
-        Args:
-            model_name: Model to check
-
-        Returns:
-            False (no OpenRouter models currently support thinking mode)
-        """
-        return False
-
     def list_models(
         self,
         *,

providers/openrouter_registry.py

@@ -12,7 +12,7 @@ from utils.file_utils import read_json_file
 from .shared import (
     ModelCapabilities,
     ProviderType,
-    create_temperature_constraint,
+    TemperatureConstraint,
 )
 
 
@@ -178,7 +178,7 @@ class OpenRouterModelRegistry:
                 # Create ModelCapabilities directly from JSON data
                 # Handle temperature_constraint conversion
                 temp_constraint_str = model_data.get("temperature_constraint")
-                temp_constraint = create_temperature_constraint(temp_constraint_str or "range")
+                temp_constraint = TemperatureConstraint.create(temp_constraint_str or "range")
 
                 # Set provider-specific defaults based on is_custom flag
                 is_custom = model_data.get("is_custom", False)

providers/shared/__init__.py

@@ -8,7 +8,6 @@ from .temperature import (
     FixedTemperatureConstraint,
     RangeTemperatureConstraint,
     TemperatureConstraint,
-    create_temperature_constraint,
 )
 
 __all__ = [
@@ -19,5 +18,4 @@ __all__ = [
     "FixedTemperatureConstraint",
     "RangeTemperatureConstraint",
     "DiscreteTemperatureConstraint",
-    "create_temperature_constraint",
 ]

providers/shared/temperature.py

@@ -8,7 +8,6 @@ __all__ = [
     "FixedTemperatureConstraint",
     "RangeTemperatureConstraint",
     "DiscreteTemperatureConstraint",
-    "create_temperature_constraint",
 ]
 
 # Common heuristics for determining temperature support when explicit
@@ -102,7 +101,7 @@ class TemperatureConstraint(ABC):
         """
 
         if constraint_hint:
-            constraint = create_temperature_constraint(constraint_hint)
+            constraint = TemperatureConstraint.create(constraint_hint)
             supports_temperature = constraint_hint != "fixed"
             reason = f"constraint hint '{constraint_hint}'"
             return supports_temperature, constraint, reason
@@ -115,6 +114,19 @@ class TemperatureConstraint(ABC):
 
         return supports_temperature, constraint, reason
 
+    @staticmethod
+    def create(constraint_type: str) -> "TemperatureConstraint":
+        """Factory that yields the appropriate constraint for a configuration hint."""
+
+        if constraint_type == "fixed":
+            # Fixed temperature models (O3/O4) only support temperature=1.0
+            return FixedTemperatureConstraint(1.0)
+        if constraint_type == "discrete":
+            # For models with specific allowed values - using common OpenAI values as default
+            return DiscreteTemperatureConstraint([0.0, 0.3, 0.7, 1.0, 1.5, 2.0], 0.3)
+        # Default range constraint (for "range" or None)
+        return RangeTemperatureConstraint(0.0, 2.0, 0.3)
+
 
 class FixedTemperatureConstraint(TemperatureConstraint):
     """Constraint for models that enforce an exact temperature (for example O3)."""
@@ -174,22 +186,3 @@ class DiscreteTemperatureConstraint(TemperatureConstraint):
 
     def get_default(self) -> float:
         return self.default_temp
-
-
-def create_temperature_constraint(constraint_type: str) -> TemperatureConstraint:
-    """Factory that yields the appropriate constraint for a model configuration.
-
-    The JSON configuration stored in ``conf/custom_models.json`` references this
-    helper via human-readable strings.  Providers feed those values into this
-    function so that runtime logic can rely on strongly typed constraint
-    objects.
-    """
-
-    if constraint_type == "fixed":
-        # Fixed temperature models (O3/O4) only support temperature=1.0
-        return FixedTemperatureConstraint(1.0)
-    if constraint_type == "discrete":
-        # For models with specific allowed values - using common OpenAI values as default
-        return DiscreteTemperatureConstraint([0.0, 0.3, 0.7, 1.0, 1.5, 2.0], 0.3)
-    # Default range constraint (for "range" or None)
-    return RangeTemperatureConstraint(0.0, 2.0, 0.3)

providers/xai.py

@@ -7,12 +7,7 @@ if TYPE_CHECKING:
     from tools.models import ToolModelCategory
 
 from .openai_compatible import OpenAICompatibleProvider
-from .shared import (
+from .shared import ModelCapabilities, ModelResponse, ProviderType, TemperatureConstraint
-    ModelCapabilities,
-    ModelResponse,
-    ProviderType,
-    create_temperature_constraint,
-)
 
 logger = logging.getLogger(__name__)
 
@@ -42,7 +37,7 @@ class XAIModelProvider(OpenAICompatibleProvider):
             supports_images=True,  # Multimodal capabilities
             max_image_size_mb=20.0,  # Standard image size limit
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="GROK-4 (256K context) - Frontier multimodal reasoning model with advanced capabilities",
             aliases=["grok", "grok4", "grok-4"],
         ),
@@ -60,7 +55,7 @@ class XAIModelProvider(OpenAICompatibleProvider):
             supports_images=False,  # Assuming GROK is text-only for now
             max_image_size_mb=0.0,
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="GROK-3 (131K context) - Advanced reasoning model from X.AI, excellent for complex analysis",
             aliases=["grok3"],
         ),
@@ -78,7 +73,7 @@ class XAIModelProvider(OpenAICompatibleProvider):
             supports_images=False,  # Assuming GROK is text-only for now
             max_image_size_mb=0.0,
             supports_temperature=True,
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
             description="GROK-3 Fast (131K context) - Higher performance variant, faster processing but more expensive",
             aliases=["grok3fast", "grokfast", "grok3-fast"],
         ),
@@ -153,14 +148,6 @@ class XAIModelProvider(OpenAICompatibleProvider):
             **kwargs,
         )
 
-    def supports_thinking_mode(self, model_name: str) -> bool:
-        """Check if the model supports extended thinking mode."""
-        resolved_name = self._resolve_model_name(model_name)
-        capabilities = self.MODEL_CAPABILITIES.get(resolved_name)
-        if capabilities:
-            return capabilities.supports_extended_thinking
-        return False
-
     def get_preferred_model(self, category: "ToolModelCategory", allowed_models: list[str]) -> Optional[str]:
         """Get XAI's preferred model for a given category from allowed models.
 

tests/mock_helpers.py

@@ -25,7 +25,6 @@ def create_mock_provider(model_name="gemini-2.5-flash", context_window=1_048_576
 
     mock_provider.get_capabilities.return_value = mock_capabilities
     mock_provider.get_provider_type.return_value = ProviderType.GOOGLE
-    mock_provider.supports_thinking_mode.return_value = False
     mock_provider.validate_model_name.return_value = True
 
     # Set up generate_content response

tests/test_collaboration.py

@@ -40,7 +40,6 @@ class TestDynamicContextRequests:
 
         mock_provider = create_mock_provider()
         mock_provider.get_provider_type.return_value = Mock(value="google")
-        mock_provider.supports_thinking_mode.return_value = False
         mock_provider.generate_content.return_value = Mock(
             content=clarification_json, usage={}, model_name="gemini-2.5-flash", metadata={}
         )
@@ -122,7 +121,6 @@ class TestDynamicContextRequests:
 
         mock_provider = create_mock_provider()
         mock_provider.get_provider_type.return_value = Mock(value="google")
-        mock_provider.supports_thinking_mode.return_value = False
         mock_provider.generate_content.return_value = Mock(
             content=malformed_json, usage={}, model_name="gemini-2.5-flash", metadata={}
         )
@@ -181,7 +179,6 @@ class TestDynamicContextRequests:
 
         mock_provider = create_mock_provider()
         mock_provider.get_provider_type.return_value = Mock(value="google")
-        mock_provider.supports_thinking_mode.return_value = False
         mock_provider.generate_content.return_value = Mock(
             content=clarification_json, usage={}, model_name="gemini-2.5-flash", metadata={}
         )
@@ -347,7 +344,6 @@ class TestCollaborationWorkflow:
 
         mock_provider = create_mock_provider()
         mock_provider.get_provider_type.return_value = Mock(value="google")
-        mock_provider.supports_thinking_mode.return_value = False
         mock_provider.generate_content.return_value = Mock(
             content=clarification_json, usage={}, model_name="gemini-2.5-flash", metadata={}
         )
@@ -414,7 +410,6 @@ class TestCollaborationWorkflow:
 
         mock_provider = create_mock_provider()
         mock_provider.get_provider_type.return_value = Mock(value="google")
-        mock_provider.supports_thinking_mode.return_value = False
         mock_provider.generate_content.return_value = Mock(
             content=clarification_json, usage={}, model_name="gemini-2.5-flash", metadata={}
         )

tests/test_custom_openai_temperature_fix.py

@@ -86,7 +86,7 @@ class TestCustomOpenAITemperatureParameterFix:
                 mock_registry_class.return_value = mock_registry
 
                 # Mock get_model_config to return our test model
-                from providers.shared import ModelCapabilities, ProviderType, create_temperature_constraint
+                from providers.shared import ModelCapabilities, ProviderType, TemperatureConstraint
 
                 test_capabilities = ModelCapabilities(
                     provider=ProviderType.OPENAI,
@@ -102,7 +102,7 @@ class TestCustomOpenAITemperatureParameterFix:
                     supports_images=True,
                     max_image_size_mb=20.0,
                     supports_temperature=False,  # This is the key setting
-                    temperature_constraint=create_temperature_constraint("fixed"),
+                    temperature_constraint=TemperatureConstraint.create("fixed"),
                     description="Custom OpenAI GPT-5 test model",
                 )
 
@@ -170,7 +170,7 @@ class TestCustomOpenAITemperatureParameterFix:
             mock_registry_class.return_value = mock_registry
 
             # Mock get_model_config to return a model that supports temperature
-            from providers.shared import ModelCapabilities, ProviderType, create_temperature_constraint
+            from providers.shared import ModelCapabilities, ProviderType, TemperatureConstraint
 
             test_capabilities = ModelCapabilities(
                 provider=ProviderType.OPENAI,
@@ -186,7 +186,7 @@ class TestCustomOpenAITemperatureParameterFix:
                 supports_images=True,
                 max_image_size_mb=20.0,
                 supports_temperature=True,  # This model DOES support temperature
-                temperature_constraint=create_temperature_constraint("range"),
+                temperature_constraint=TemperatureConstraint.create("range"),
                 description="Custom OpenAI GPT-4 test model",
             )
 
@@ -227,7 +227,7 @@ class TestCustomOpenAITemperatureParameterFix:
             mock_registry = Mock()
             mock_registry_class.return_value = mock_registry
 
-            from providers.shared import ModelCapabilities, ProviderType, create_temperature_constraint
+            from providers.shared import ModelCapabilities, ProviderType, TemperatureConstraint
 
             test_capabilities = ModelCapabilities(
                 provider=ProviderType.OPENAI,
@@ -243,7 +243,7 @@ class TestCustomOpenAITemperatureParameterFix:
                 supports_images=True,
                 max_image_size_mb=20.0,
                 supports_temperature=False,
-                temperature_constraint=create_temperature_constraint("fixed"),
+                temperature_constraint=TemperatureConstraint.create("fixed"),
                 description="Custom OpenAI O3 test model",
             )
 

tests/test_custom_provider.py

@@ -99,11 +99,11 @@ class TestCustomProvider:
         assert resolved_local == "llama3.2"
 
     def test_no_thinking_mode_support(self):
-        """Test CustomProvider doesn't support thinking mode."""
+        """Custom provider generic capabilities default to no thinking mode."""
         provider = CustomProvider(api_key="test-key", base_url="http://localhost:11434/v1")
 
-        assert not provider.supports_thinking_mode("llama3.2")
+        assert not provider.get_capabilities("llama3.2").supports_extended_thinking
-        assert not provider.supports_thinking_mode("any-model")
+        assert not provider.get_capabilities("any-model").supports_extended_thinking
 
     @patch("providers.custom.OpenAICompatibleProvider.generate_content")
     def test_generate_content_with_alias_resolution(self, mock_generate):

tests/test_image_validation.py

@@ -43,10 +43,6 @@ class MinimalTestProvider(ModelProvider):
         """Not needed for image validation tests."""
         raise NotImplementedError("Not needed for image validation tests")
 
-    def supports_thinking_mode(self, model_name: str) -> bool:
-        """Not needed for image validation tests."""
-        raise NotImplementedError("Not needed for image validation tests")
-
 
 class TestImageValidation:
     """Test suite for image validation functionality."""

tests/test_issue_245_simple.py

@@ -41,7 +41,7 @@ def test_issue_245_custom_openai_temperature_ignored():
                 mock_registry = Mock()
                 mock_registry_class.return_value = mock_registry
 
-                from providers.shared import ModelCapabilities, ProviderType, create_temperature_constraint
+                from providers.shared import ModelCapabilities, ProviderType, TemperatureConstraint
 
                 # This is what the user configured in their custom_models.json
                 custom_config = ModelCapabilities(
@@ -56,7 +56,7 @@ def test_issue_245_custom_openai_temperature_ignored():
                     supports_streaming=True,
                     supports_function_calling=True,
                     supports_temperature=False,  # User set this to false!
-                    temperature_constraint=create_temperature_constraint("fixed"),
+                    temperature_constraint=TemperatureConstraint.create("fixed"),
                     supports_images=True,
                     max_image_size_mb=20.0,
                     description="Custom OpenAI GPT-5",

tests/test_large_prompt_handling.py

@@ -244,7 +244,7 @@ class TestLargePromptHandling:
         with patch.object(tool, "get_model_provider") as mock_get_provider:
             mock_provider = MagicMock()
             mock_provider.get_provider_type.return_value = MagicMock(value="google")
-            mock_provider.supports_thinking_mode.return_value = False
+            mock_provider.get_capabilities.return_value = MagicMock(supports_extended_thinking=False)
             mock_provider.generate_content.return_value = MagicMock(
                 content="Success",
                 usage={"input_tokens": 10, "output_tokens": 20, "total_tokens": 30},
@@ -287,7 +287,7 @@ class TestLargePromptHandling:
         with patch.object(tool, "get_model_provider") as mock_get_provider:
             mock_provider = MagicMock()
             mock_provider.get_provider_type.return_value = MagicMock(value="google")
-            mock_provider.supports_thinking_mode.return_value = False
+            mock_provider.get_capabilities.return_value = MagicMock(supports_extended_thinking=False)
             mock_provider.generate_content.return_value = MagicMock(
                 content="Response to the large prompt",
                 usage={"input_tokens": 12000, "output_tokens": 10, "total_tokens": 12010},
@@ -319,7 +319,7 @@ class TestLargePromptHandling:
         with patch.object(tool, "get_model_provider") as mock_get_provider:
             mock_provider = MagicMock()
             mock_provider.get_provider_type.return_value = MagicMock(value="google")
-            mock_provider.supports_thinking_mode.return_value = False
+            mock_provider.get_capabilities.return_value = MagicMock(supports_extended_thinking=False)
             mock_provider.generate_content.return_value = MagicMock(
                 content="Success",
                 usage={"input_tokens": 10, "output_tokens": 20, "total_tokens": 30},

tests/test_openai_provider.py

@@ -252,33 +252,31 @@ class TestOpenAIProvider:
         call_kwargs = mock_client.chat.completions.create.call_args[1]
         assert call_kwargs["model"] == "o3-mini"  # Should be unchanged
 
-    def test_supports_thinking_mode(self):
+    def test_extended_thinking_capabilities(self):
-        """Test thinking mode support based on model capabilities."""
+        """Thinking-mode support should be reflected via ModelCapabilities."""
         provider = OpenAIModelProvider("test-key")
 
-        # GPT-5 models support thinking mode (reasoning tokens) - all variants
+        supported_aliases = [
-        assert provider.supports_thinking_mode("gpt-5") is True
+            "gpt-5",
-        assert provider.supports_thinking_mode("gpt-5-mini") is True
+            "gpt-5-mini",
-        assert provider.supports_thinking_mode("gpt-5-nano") is True  # Now included
+            "gpt-5-nano",
+            "gpt5",
+            "gpt5-mini",
+            "gpt5mini",
+            "gpt5-nano",
+            "gpt5nano",
+            "nano",
+            "mini",  # resolves to gpt-5-mini
+        ]
+        for alias in supported_aliases:
+            assert provider.get_capabilities(alias).supports_extended_thinking is True
 
-        # Test GPT-5 aliases
+        unsupported_aliases = ["o3", "o3-mini", "o4-mini"]
-        assert provider.supports_thinking_mode("gpt5") is True
+        for alias in unsupported_aliases:
-        assert provider.supports_thinking_mode("gpt5-mini") is True
+            assert provider.get_capabilities(alias).supports_extended_thinking is False
-        assert provider.supports_thinking_mode("gpt5mini") is True
-        assert provider.supports_thinking_mode("gpt5-nano") is True
-        assert provider.supports_thinking_mode("gpt5nano") is True
-        assert provider.supports_thinking_mode("nano") is True  # New alias for gpt-5-nano
 
-        # O3/O4 models don't support thinking mode
+        # Invalid models should not validate, treat as unsupported
-        assert provider.supports_thinking_mode("o3") is False
+        assert not provider.validate_model_name("invalid-model")
-        assert provider.supports_thinking_mode("o3-mini") is False
-        assert provider.supports_thinking_mode("o4-mini") is False
-        assert (
-            provider.supports_thinking_mode("mini") is True
-        )  # "mini" now resolves to gpt-5-mini which supports thinking
-
-        # Test invalid model name
-        assert provider.supports_thinking_mode("invalid-model") is False
 
     @patch("providers.openai_compatible.OpenAI")
     def test_o3_pro_routes_to_responses_endpoint(self, mock_openai_class):

tests/test_openrouter_registry.py

@@ -213,7 +213,7 @@ class TestOpenRouterModelRegistry:
 
     def test_model_with_all_capabilities(self):
         """Test model with all capability flags."""
-        from providers.shared import create_temperature_constraint
+        from providers.shared import TemperatureConstraint
 
         caps = ModelCapabilities(
             provider=ProviderType.OPENROUTER,
@@ -228,7 +228,7 @@ class TestOpenRouterModelRegistry:
             supports_function_calling=True,
             supports_json_mode=True,
             description="Fully featured test model",
-            temperature_constraint=create_temperature_constraint("range"),
+            temperature_constraint=TemperatureConstraint.create("range"),
         )
         assert caps.context_window == 128000
         assert caps.supports_extended_thinking

tests/test_providers.py

@@ -120,13 +120,6 @@ class TestGeminiProvider:
         capabilities = provider.get_capabilities("flash")
         assert capabilities.model_name == "gemini-2.5-flash"
 
-    def test_supports_thinking_mode(self):
-        """Test thinking mode support detection"""
-        provider = GeminiModelProvider(api_key="test-key")
-
-        assert provider.supports_thinking_mode("gemini-2.5-flash")
-        assert provider.supports_thinking_mode("gemini-2.5-pro")
-
     @patch("google.genai.Client")
     def test_generate_content(self, mock_client_class):
         """Test content generation"""
@@ -219,12 +212,10 @@ class TestOpenAIProvider:
         assert not provider.validate_model_name("gpt-4o")
         assert not provider.validate_model_name("invalid-model")
 
-    def test_no_thinking_mode_support(self):
+    def test_openai_models_do_not_support_extended_thinking(self):
-        """Test that no OpenAI models support thinking mode"""
+        """OpenAI catalogue exposes extended thinking capability via ModelCapabilities."""
         provider = OpenAIModelProvider(api_key="test-key")
 
-        assert not provider.supports_thinking_mode("o3")
+        aliases = ["o3", "o3mini", "o3-mini", "o4-mini", "o4mini"]
-        assert not provider.supports_thinking_mode("o3mini")
+        for alias in aliases:
-        assert not provider.supports_thinking_mode("o3-mini")
+            assert not provider.get_capabilities(alias).supports_extended_thinking
-        assert not provider.supports_thinking_mode("o4-mini")
-        assert not provider.supports_thinking_mode("o4-mini")

tests/test_workflow_utf8.py

@@ -75,7 +75,7 @@ class TestWorkflowToolsUTF8(unittest.IsolatedAsyncioTestCase):
         # Mock provider with more complete setup (same as codereview test)
         mock_provider = Mock()
         mock_provider.get_provider_type.return_value = Mock(value="test")
-        mock_provider.supports_thinking_mode.return_value = False
+        mock_provider.get_capabilities.return_value = Mock(supports_extended_thinking=False)
         mock_provider.generate_content = AsyncMock(
             return_value=Mock(
                 content=json.dumps(
@@ -93,6 +93,7 @@ class TestWorkflowToolsUTF8(unittest.IsolatedAsyncioTestCase):
         # Use the same provider for both contexts
         mock_get_provider.return_value = mock_provider
         mock_context_instance.provider = mock_provider
+        mock_context_instance.capabilities = Mock(supports_extended_thinking=False)
         mock_model_context.return_value = mock_context_instance
 
         # Test the tool
@@ -131,7 +132,7 @@ class TestWorkflowToolsUTF8(unittest.IsolatedAsyncioTestCase):
         # Mock with analysis in French
         mock_provider = Mock()
         mock_provider.get_provider_type.return_value = Mock(value="test")
-        mock_provider.supports_thinking_mode.return_value = False
+        mock_provider.get_capabilities.return_value = Mock(supports_extended_thinking=False)
         mock_provider.generate_content = AsyncMock(
             return_value=Mock(
                 content=json.dumps(
@@ -204,7 +205,7 @@ class TestWorkflowToolsUTF8(unittest.IsolatedAsyncioTestCase):
         # Mock provider
         mock_provider = Mock()
         mock_provider.get_provider_type.return_value = Mock(value="test")
-        mock_provider.supports_thinking_mode.return_value = False
+        mock_provider.get_capabilities.return_value = Mock(supports_extended_thinking=False)
         mock_provider.generate_content = AsyncMock(
             return_value=Mock(
                 content=json.dumps(

tests/test_xai_provider.py

@@ -144,21 +144,17 @@ class TestXAIProvider:
         with pytest.raises(ValueError, match="Unsupported X.AI model"):
             provider.get_capabilities("invalid-model")
 
-    def test_thinking_mode_support(self):
+    def test_extended_thinking_flags(self):
-        """Test thinking mode support for X.AI models."""
+        """X.AI capabilities should expose extended thinking support correctly."""
         provider = XAIModelProvider("test-key")
 
-        # Grok-4 supports thinking mode
+        thinking_aliases = ["grok-4", "grok", "grok4"]
-        assert provider.supports_thinking_mode("grok-4") is True
+        for alias in thinking_aliases:
-        assert provider.supports_thinking_mode("grok") is True  # Resolves to grok-4
+            assert provider.get_capabilities(alias).supports_extended_thinking is True
 
-        # Grok-3 models don't support thinking mode
+        non_thinking_aliases = ["grok-3", "grok-3-fast", "grokfast"]
-        assert not provider.supports_thinking_mode("grok-3")
+        for alias in non_thinking_aliases:
-        assert not provider.supports_thinking_mode("grok-3-fast")
+            assert provider.get_capabilities(alias).supports_extended_thinking is False
-        assert provider.supports_thinking_mode("grok-4")  # grok-4 supports thinking mode
-        assert provider.supports_thinking_mode("grok")  # resolves to grok-4
-        assert provider.supports_thinking_mode("grok4")  # resolves to grok-4
-        assert not provider.supports_thinking_mode("grokfast")
 
     def test_provider_type(self):
         """Test provider type identification."""

tools/simple/base.py

@@ -422,13 +422,17 @@ class SimpleTool(BaseTool):
             estimated_tokens = estimate_tokens(prompt)
             logger.debug(f"Prompt length: {len(prompt)} characters (~{estimated_tokens:,} tokens)")
 
+            # Resolve model capabilities for feature gating
+            capabilities = self._model_context.capabilities
+            supports_thinking = capabilities.supports_extended_thinking
+
             # Generate content with provider abstraction
             model_response = provider.generate_content(
                 prompt=prompt,
                 model_name=self._current_model_name,
                 system_prompt=system_prompt,
                 temperature=temperature,
-                thinking_mode=thinking_mode if provider.supports_thinking_mode(self._current_model_name) else None,
+                thinking_mode=thinking_mode if supports_thinking else None,
                 images=images if images else None,
             )
 
@@ -485,9 +489,7 @@ class SimpleTool(BaseTool):
                                 model_name=self._current_model_name,
                                 system_prompt=system_prompt,
                                 temperature=temperature,
-                                thinking_mode=(
+                                thinking_mode=thinking_mode if supports_thinking else None,
-                                    thinking_mode if provider.supports_thinking_mode(self._current_model_name) else None
-                                ),
                                 images=images if images else None,
                             )