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4974012 574e4e7 4974012 574e4e7 4974012 574e4e7 4974012 0ba585f 43ea069 0ba585f 43ea069 0ba585f 6993cbc 0ba585f 6993cbc 43ea069 98fdd46 4974012 574e4e7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 | """Model capability registry for intelligent routing."""
from __future__ import annotations
from dataclasses import dataclass
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from collections.abc import Sequence
@dataclass(frozen=True, slots=True)
class ModelCapabilities:
"""Capabilities of a specific model for routing decisions."""
provider_id: str
model_id: str
model_ref: str # provider/model format
vision: bool = False # Can process images
supports_base64_images: bool = False # Accepts data: URLs with base64
supports_remote_images: bool = False # Accepts http/https URLs
supports_pdfs: bool = False # Can process PDF documents
max_images: int = 0 # Max images per request (0 = unlimited)
max_image_size_mb: float = 10.0 # Max size per image in MB
coding: bool = False # Good at code generation/analysis
reasoning: bool = False # Strong reasoning/thinking
general_text: bool = True # General text generation
multimodal_input: bool = False # Can handle multiple input types
multimodal_output: bool = False # Can produce multiple output types
max_tokens: int = 4096
speed: str = "medium" # "fast", "medium", "slow"
priority: int = 100 # Higher = preferred for its capabilities
# Registry of all available models and their capabilities
# This can be extended with actual model discovery later
MODEL_CAPABILITIES: dict[str, ModelCapabilities] = {
# Zen/minimax models
"zen/minimax-m2.5-free": ModelCapabilities(
provider_id="zen",
model_id="minimax-m2.5-free",
model_ref="zen/minimax-m2.5-free",
coding=True,
reasoning=True,
general_text=True,
max_tokens=32000,
speed="fast",
priority=80,
),
# NVIDIA NIM models
"nvidia_nim/stepfun-ai/step-3.5-flash": ModelCapabilities(
provider_id="nvidia_nim",
model_id="step-3.5-flash",
model_ref="nvidia_nim/stepfun-ai/step-3.5-flash",
coding=True,
reasoning=True,
general_text=True,
max_tokens=32000,
speed="fast",
priority=70,
),
"nvidia_nim/qwen/qwen3-coder-480b-a35b-instruct": ModelCapabilities(
provider_id="nvidia_nim",
model_id="qwen3-coder-480b-a35b-instruct",
model_ref="nvidia_nim/qwen/qwen3-coder-480b-a35b-instruct",
coding=True,
reasoning=True,
general_text=True,
max_tokens=32000,
speed="slow",
priority=90,
),
"nvidia_nim/mistralai/mistral-large-3-675b-instruct-2512": ModelCapabilities(
provider_id="nvidia_nim",
model_id="mistral-large-3-675b-instruct-2512",
model_ref="nvidia_nim/mistralai/mistral-large-3-675b-instruct-2512",
vision=True,
supports_base64_images=True,
supports_remote_images=False,
max_images=16,
max_image_size_mb=10.0,
multimodal_input=True,
coding=True,
reasoning=True,
general_text=True,
max_tokens=32000,
speed="slow",
priority=90,
),
"nvidia_nim/abacusai/dracarys-llama-3.1-70b-instruct": ModelCapabilities(
provider_id="nvidia_nim",
model_id="dracarys-llama-3.1-70b-instruct",
model_ref="nvidia_nim/abacusai/dracarys-llama-3.1-70b-instruct",
coding=True,
reasoning=True,
general_text=True,
max_tokens=32000,
speed="medium",
priority=75,
),
"nvidia_nim/z-ai/glm4.7": ModelCapabilities(
provider_id="nvidia_nim",
model_id="glm4.7",
model_ref="nvidia_nim/z-ai/glm4.7",
coding=True,
reasoning=True,
general_text=True,
max_tokens=32000,
speed="medium",
priority=70,
),
"nvidia_nim/bytedance/seed-oss-36b-instruct": ModelCapabilities(
provider_id="nvidia_nim",
model_id="seed-oss-36b-instruct",
model_ref="nvidia_nim/bytedance/seed-oss-36b-instruct",
coding=True,
reasoning=True,
general_text=True,
max_tokens=32000,
speed="medium",
priority=65,
),
"nvidia_nim/mistralai/mistral-nemotron": ModelCapabilities(
provider_id="nvidia_nim",
model_id="mistral-nemotron",
model_ref="nvidia_nim/mistralai/mistral-nemotron",
coding=True,
reasoning=True,
general_text=True,
max_tokens=32000,
speed="medium",
priority=60,
),
# Cerebras models (key only has access to llama3.1-8b currently)
# Note: qwen-3-235b-a22b-instruct-2507 exists but is rate-limited
# Note: zai-glm-4.7 and gpt-oss-120b are not accessible with current key
"cerebras/llama3.1-8b": ModelCapabilities(
provider_id="cerebras",
model_id="llama3.1-8b",
model_ref="cerebras/llama3.1-8b",
coding=True,
reasoning=False,
general_text=True,
max_tokens=32000,
speed="fast",
priority=60,
),
# Silicon Flow models
"silicon/Qwen/Qwen3.6-35B-A3B": ModelCapabilities(
provider_id="silicon",
model_id="Qwen/Qwen3.6-35B-A3B",
model_ref="silicon/Qwen/Qwen3.6-35B-A3B",
vision=True,
supports_base64_images=True,
max_images=1,
multimodal_input=True,
coding=True,
reasoning=True,
general_text=True,
max_tokens=262144,
speed="medium",
priority=85,
),
"silicon/Qwen/Qwen3.6-27B": ModelCapabilities(
provider_id="silicon",
model_id="Qwen/Qwen3.6-27B",
model_ref="silicon/Qwen/Qwen3.6-27B",
vision=True,
supports_base64_images=True,
max_images=1,
multimodal_input=True,
coding=True,
reasoning=True,
general_text=True,
max_tokens=262144,
speed="medium",
priority=82,
),
"silicon/Qwen/Qwen3.5-35B-A3B": ModelCapabilities(
provider_id="silicon",
model_id="Qwen/Qwen3.5-35B-A3B",
model_ref="silicon/Qwen/Qwen3.5-35B-A3B",
vision=True,
supports_base64_images=True,
max_images=1,
multimodal_input=True,
coding=True,
reasoning=True,
general_text=True,
max_tokens=262144,
speed="medium",
priority=80,
),
"silicon/Qwen/Qwen3.5-27B": ModelCapabilities(
provider_id="silicon",
model_id="Qwen/Qwen3.5-27B",
model_ref="silicon/Qwen/Qwen3.5-27B",
vision=True,
supports_base64_images=True,
max_images=1,
multimodal_input=True,
coding=True,
reasoning=True,
general_text=True,
max_tokens=262144,
speed="medium",
priority=78,
),
"silicon/google/gemma-4-26B-A4B-it": ModelCapabilities(
provider_id="silicon",
model_id="google/gemma-4-26B-A4B-it",
model_ref="silicon/google/gemma-4-26B-A4B-it",
coding=True,
reasoning=True,
general_text=True,
max_tokens=262144,
speed="fast",
priority=75,
),
"silicon/google/gemma-4-31B-it": ModelCapabilities(
provider_id="silicon",
model_id="google/gemma-4-31B-it",
model_ref="silicon/google/gemma-4-31B-it",
coding=True,
reasoning=True,
general_text=True,
max_tokens=262144,
speed="fast",
priority=76,
),
# Groq models
"groq/llama-3.3-70b-versatile": ModelCapabilities(
provider_id="groq",
model_id="llama-3.3-70b-versatile",
model_ref="groq/llama-3.3-70b-versatile",
coding=True,
reasoning=True,
general_text=True,
max_tokens=32768,
speed="fast",
priority=85,
),
"groq/llama-3.1-8b-instant": ModelCapabilities(
provider_id="groq",
model_id="llama-3.1-8b-instant",
model_ref="groq/llama-3.1-8b-instant",
coding=True,
general_text=True,
max_tokens=131072,
speed="fast",
priority=90,
),
"groq/qwen3-32b": ModelCapabilities(
provider_id="groq",
model_id="qwen3-32b",
model_ref="groq/qwen3-32b",
coding=True,
reasoning=True,
general_text=True,
max_tokens=40960,
speed="medium",
priority=88,
),
}
def get_model_capabilities(model_ref: str) -> ModelCapabilities | None:
"""Get capabilities for a specific model reference."""
return MODEL_CAPABILITIES.get(model_ref)
def find_models_with_capability(capability: str) -> list[ModelCapabilities]:
"""Find all models that have a specific capability."""
results = []
for caps in MODEL_CAPABILITIES.values():
if getattr(caps, capability, False):
results.append(caps)
# Sort by priority (higher = better)
results.sort(key=lambda x: x.priority, reverse=True)
return results
def find_best_model_for_task(
required_capabilities: set[str],
available_models: Sequence[str] | None = None,
) -> ModelCapabilities | None:
"""Find the best model matching required capabilities.
Args:
required_capabilities: Set of capability names needed (e.g., {"coding", "vision"})
available_models: Optional list of model refs to filter by
Returns:
Best matching ModelCapabilities or None
"""
candidates = []
models_to_check = (
[MODEL_CAPABILITIES[m] for m in available_models if m in MODEL_CAPABILITIES]
if available_models
else list(MODEL_CAPABILITIES.values())
)
for caps in models_to_check:
# Check if model has all required capabilities
if all(getattr(caps, cap, False) for cap in required_capabilities):
candidates.append(caps)
if not candidates:
return None
# Sort by priority and return best
candidates.sort(key=lambda x: x.priority, reverse=True)
return candidates[0]
def get_capability_match_score(
model_caps: ModelCapabilities,
required: set[str],
) -> tuple[int, int]:
"""Calculate match score for routing.
Returns (matched_count, priority) for sorting.
"""
matched = sum(1 for cap in required if getattr(model_caps, cap, False))
return (matched, model_caps.priority)
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