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feat!: breaking change - OpenRouter models are now read from conf/openrouter_models.json while Custom / Self-hosted models are read from conf/custom_models.json

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feat: Azure OpenAI / Azure AI Foundry support. Models should be defined in conf/azure_models.json (or a custom path). See .env.example for environment variables or see readme. https://github.com/BeehiveInnovations/zen-mcp-server/issues/265

feat: OpenRouter / Custom Models / Azure can separately also use custom config paths now (see .env.example )

refactor: Model registry class made abstract, OpenRouter / Custom Provider / Azure OpenAI now subclass these

refactor: breaking change: `is_custom` property has been removed from model_capabilities.py (and thus custom_models.json) given each models are now read from separate configuration files
This commit is contained in:
Fahad
2025-10-04 21:10:56 +04:00
parent e91ed2a924
commit ff9a07a37a
40 changed files with 1651 additions and 852 deletions
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55
providers/openai_provider.py
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@@ -103,16 +103,16 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
model_name="o3-mini",
friendly_name="OpenAI (O3-mini)",
intelligence_score=12,
context_window=200_000, # 200K tokens
max_output_tokens=65536, # 64K max output tokens
context_window=200_000,
max_output_tokens=65536,
supports_extended_thinking=False,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
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
supports_images=True,
max_image_size_mb=20.0,
supports_temperature=False,
temperature_constraint=TemperatureConstraint.create("fixed"),
description="Fast O3 variant (200K context) - Balanced performance/speed, moderate complexity",
aliases=["o3mini"],
@@ -122,16 +122,16 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
model_name="o3-pro",
friendly_name="OpenAI (O3-Pro)",
intelligence_score=15,
context_window=200_000, # 200K tokens
max_output_tokens=65536, # 64K max output tokens
context_window=200_000,
max_output_tokens=65536,
supports_extended_thinking=False,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
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
supports_images=True,
max_image_size_mb=20.0,
supports_temperature=False,
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"],
@@ -141,16 +141,15 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
model_name="o4-mini",
friendly_name="OpenAI (O4-mini)",
intelligence_score=11,
context_window=200_000, # 200K tokens
max_output_tokens=65536, # 64K max output tokens
context_window=200_000,
supports_extended_thinking=False,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
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
supports_images=True,
max_image_size_mb=20.0,
supports_temperature=False,
temperature_constraint=TemperatureConstraint.create("fixed"),
description="Latest reasoning model (200K context) - Optimized for shorter contexts, rapid reasoning",
aliases=["o4mini"],
@@ -160,16 +159,16 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
model_name="gpt-4.1",
friendly_name="OpenAI (GPT 4.1)",
intelligence_score=13,
context_window=1_000_000, # 1M tokens
context_window=1_000_000,
max_output_tokens=32_768,
supports_extended_thinking=False,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
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
supports_images=True,
max_image_size_mb=20.0,
supports_temperature=True,
temperature_constraint=TemperatureConstraint.create("range"),
description="GPT-4.1 (1M context) - Advanced reasoning model with large context window",
aliases=["gpt4.1"],
@@ -178,19 +177,19 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
provider=ProviderType.OPENAI,
model_name="gpt-5-codex",
friendly_name="OpenAI (GPT-5 Codex)",
intelligence_score=17, # Higher than GPT-5 for coding tasks
context_window=400_000, # 400K tokens (same as GPT-5)
max_output_tokens=128_000, # 128K output tokens
supports_extended_thinking=True, # Responses API supports reasoning tokens
intelligence_score=17,
context_window=400_000,
max_output_tokens=128_000,
supports_extended_thinking=True,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True, # Enhanced for agentic software engineering
supports_function_calling=True,
supports_json_mode=True,
supports_images=True, # Screenshots, wireframes, diagrams
max_image_size_mb=20.0, # 20MB per OpenAI docs
supports_images=True,
max_image_size_mb=20.0,
supports_temperature=True,
temperature_constraint=TemperatureConstraint.create("range"),
description="GPT-5 Codex (400K context) - Uses Responses API for 40-80% cost savings. Specialized for coding, refactoring, and software architecture. 3% better performance on SWE-bench.",
description="GPT-5 Codex (400K context) Specialized for coding, refactoring, and software architecture.",
aliases=["gpt5-codex", "codex", "gpt-5-code", "gpt5-code"],
),
}
@@ -282,7 +281,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
if category == ToolModelCategory.EXTENDED_REASONING:
# Prefer models with extended thinking support
# GPT-5-Codex first for coding tasks (uses Responses API with 40-80% cost savings)
# GPT-5-Codex first for coding tasks
preferred = find_first(["gpt-5-codex", "o3", "o3-pro", "gpt-5"])
return preferred if preferred else allowed_models[0]