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gpt4free/g4f/models.py
hlohaus 082c152248 Fix unittests
2025-05-17 11:08:52 +02:00

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from __future__ import annotations
from dataclasses import dataclass
from .Provider import IterListProvider, ProviderType
from .Provider import (
### No Auth Required ###
ARTA,
Blackbox,
Chatai,
ChatGLM,
Cloudflare,
Copilot,
DDG,
DeepInfraChat,
Dynaspark,
Free2GPT,
FreeGpt,
HuggingSpace,
Grok,
DeepseekAI_JanusPro7b,
ImageLabs,
LambdaChat,
Liaobots,
PerplexityLabs,
Pi,
PollinationsAI,
PollinationsImage,
TypeGPT,
TeachAnything,
Websim,
Yqcloud,
### Needs Auth ###
BingCreateImages,
CopilotAccount,
Gemini,
GeminiPro,
HailuoAI,
HuggingChat,
HuggingFace,
HuggingFaceAPI,
MetaAI,
MicrosoftDesigner,
OpenaiAccount,
OpenaiChat,
Reka,
)
@dataclass(unsafe_hash=True)
class Model:
"""
Represents a machine learning model configuration.
Attributes:
name (str): Name of the model.
base_provider (str): Default provider for the model.
best_provider (ProviderType): The preferred provider for the model, typically with retry logic.
"""
name: str
base_provider: str
best_provider: ProviderType = None
@staticmethod
def __all__() -> list[str]:
"""Returns a list of all model names."""
return _all_models
class ImageModel(Model):
pass
class AudioModel(Model):
pass
class VideoModel(Model):
pass
class VisionModel(Model):
pass
### Default ###
default = Model(
name = "",
base_provider = "",
best_provider = IterListProvider([
Blackbox,
DDG,
Copilot,
DeepInfraChat,
PollinationsAI,
TypeGPT,
Free2GPT,
FreeGpt,
Dynaspark,
Chatai,
OpenaiChat,
Cloudflare,
])
)
default_vision = VisionModel(
name = "",
base_provider = "",
best_provider = IterListProvider([
Blackbox,
TypeGPT,
DeepInfraChat,
PollinationsAI,
Dynaspark,
HuggingSpace,
GeminiPro,
HuggingFaceAPI,
CopilotAccount,
OpenaiAccount,
Gemini,
], shuffle=False)
)
##########################
### Text//Audio/Vision ###
##########################
### OpenAI ###
# gpt-4
gpt_4 = Model(
name = 'gpt-4',
base_provider = 'OpenAI',
best_provider = IterListProvider([Blackbox, DDG, PollinationsAI, Copilot, Yqcloud, Liaobots, OpenaiChat])
)
gpt_4_turbo = Model(
name = 'gpt-4-turbo',
base_provider = 'OpenAI',
best_provider = Liaobots
)
# gpt-4.1
gpt_4_1 = Model(
name = 'gpt-4.1',
base_provider = 'OpenAI',
best_provider = IterListProvider([PollinationsAI, Liaobots])
)
gpt_4_1_mini = Model(
name = 'gpt-4.1-mini',
base_provider = 'OpenAI',
best_provider = IterListProvider([PollinationsAI, Liaobots])
)
gpt_4_1_nano = Model(
name = 'gpt-4.1-nano',
base_provider = 'OpenAI',
best_provider = IterListProvider([Blackbox, PollinationsAI])
)
# gpt-4o
gpt_4o = VisionModel(
name = 'gpt-4o',
base_provider = 'OpenAI',
best_provider = IterListProvider([Blackbox, PollinationsAI, Liaobots, OpenaiChat])
)
gpt_4o_mini = Model(
name = 'gpt-4o-mini',
base_provider = 'OpenAI',
best_provider = IterListProvider([Blackbox, DDG, PollinationsAI, TypeGPT, Chatai, Liaobots, OpenaiChat])
)
gpt_4o_audio = AudioModel(
name = 'gpt-4o-audio',
base_provider = 'OpenAI',
best_provider = PollinationsAI
)
# o1
o1 = Model(
name = 'o1',
base_provider = 'OpenAI',
best_provider = IterListProvider([Copilot, OpenaiAccount])
)
o1_mini = Model(
name = 'o1-mini',
base_provider = 'OpenAI',
best_provider = OpenaiAccount
)
# o3
o3_mini = Model(
name = 'o3-mini',
base_provider = 'OpenAI',
best_provider = IterListProvider([DDG, Blackbox, Liaobots])
)
# o4
o4_mini = Model(
name = 'o4-mini',
base_provider = 'OpenAI',
best_provider = PollinationsAI
)
### Meta ###
meta = Model(
name = "meta-ai",
base_provider = "Meta",
best_provider = MetaAI
)
# llama 2
llama_2_7b = Model(
name = "llama-2-7b",
base_provider = "Meta Llama",
best_provider = Cloudflare
)
# llama 3
llama_3_8b = Model(
name = "llama-3-8b",
base_provider = "Meta Llama",
best_provider = Cloudflare
)
# llama 3.1
llama_3_1_8b = Model(
name = "llama-3.1-8b",
base_provider = "Meta Llama",
best_provider = IterListProvider([Blackbox, DeepInfraChat, Cloudflare])
)
# llama 3.2
llama_3_2_1b = Model(
name = "llama-3.2-1b",
base_provider = "Meta Llama",
best_provider = IterListProvider([Blackbox, Cloudflare])
)
llama_3_2_3b = Model(
name = "llama-3.2-3b",
base_provider = "Meta Llama",
best_provider = IterListProvider([Blackbox])
)
llama_3_2_11b = VisionModel(
name = "llama-3.2-11b",
base_provider = "Meta Llama",
best_provider = IterListProvider([Blackbox, HuggingChat, HuggingFace])
)
llama_3_2_90b = Model(
name = "llama-3.2-90b",
base_provider = "Meta Llama",
best_provider = DeepInfraChat
)
# llama 3.3
llama_3_3_70b = Model(
name = "llama-3.3-70b",
base_provider = "Meta Llama",
best_provider = IterListProvider([Blackbox, DDG, DeepInfraChat, LambdaChat, PollinationsAI, HuggingChat, HuggingFace])
)
# llama 4
llama_4_scout = Model(
name = "llama-4-scout",
base_provider = "Meta Llama",
best_provider = IterListProvider([Blackbox, PollinationsAI, Cloudflare])
)
llama_4_scout_17b = Model(
name = "llama-4-scout-17b",
base_provider = "Meta Llama",
best_provider = IterListProvider([DeepInfraChat, PollinationsAI])
)
llama_4_maverick = Model(
name = "llama-4-maverick",
base_provider = "Meta Llama",
best_provider = IterListProvider([Blackbox, DeepInfraChat])
)
llama_4_maverick_17b = Model(
name = "llama-4-maverick-17b",
base_provider = "Meta Llama",
best_provider = DeepInfraChat
)
### Mistral ###
mistral_7b = Model(
name = "mistral-7b",
base_provider = "Mistral",
best_provider = Blackbox
)
mixtral_8x22b = Model(
name = "mixtral-8x22b",
base_provider = "Mistral",
best_provider = DeepInfraChat
)
mistral_nemo = Model(
name = "mistral-nemo",
base_provider = "Mistral",
best_provider = IterListProvider([Blackbox, HuggingChat, HuggingFace])
)
mistral_small_24b = Model(
name = "mistral-small-24b",
base_provider = "Mistral",
best_provider = IterListProvider([Blackbox, DDG, DeepInfraChat])
)
mistral_small_3_1_24b = Model(
name = "mistral-small-3.1-24b",
base_provider = "Mistral",
best_provider = IterListProvider([Blackbox, PollinationsAI])
)
### NousResearch ###
hermes_3 = Model(
name = "hermes-3",
base_provider = "NousResearch",
best_provider = LambdaChat
)
hermes_3_405b = Model(
name = "hermes-3-405b",
base_provider = "NousResearch",
best_provider = LambdaChat
)
deephermes_3_8b = Model(
name = "deephermes-3-8b",
base_provider = "NousResearch",
best_provider = Blackbox
)
### Microsoft ###
# phi
phi_3_5_mini = Model(
name = "phi-3.5-mini",
base_provider = "Microsoft",
best_provider = HuggingChat
)
phi_4 = Model(
name = "phi-4",
base_provider = "Microsoft",
best_provider = IterListProvider([DeepInfraChat, PollinationsAI, HuggingSpace])
)
phi_4_multimodal = VisionModel(
name = "phi-4-multimodal",
base_provider = "Microsoft",
best_provider = IterListProvider([DeepInfraChat, HuggingSpace])
)
phi_4_reasoning_plus = Model(
name = "phi-4-reasoning-plus",
base_provider = "Microsoft",
best_provider = DeepInfraChat
)
# wizardlm
wizardlm_2_7b = Model(
name = 'wizardlm-2-7b',
base_provider = 'Microsoft',
best_provider = DeepInfraChat
)
wizardlm_2_8x22b = Model(
name = 'wizardlm-2-8x22b',
base_provider = 'Microsoft',
best_provider = DeepInfraChat
)
### Google DeepMind ###
# gemini
gemini = Model(
name = 'gemini-2.0',
base_provider = 'Google',
best_provider = Gemini
)
# gemini-1.0
gemini_1_0_pro = Model(
name = 'gemini-1.0-pro',
base_provider = 'Google DeepMind',
best_provider = Liaobots
)
# gemini-1.5
gemini_1_5_flash = Model(
name = 'gemini-1.5-flash',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([Free2GPT, FreeGpt, TeachAnything, Websim, Dynaspark, GeminiPro])
)
gemini_1_5_pro = Model(
name = 'gemini-1.5-pro',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([Free2GPT, FreeGpt, TeachAnything, Websim, GeminiPro])
)
# gemini-2.0
gemini_2_0_flash = Model(
name = 'gemini-2.0-flash',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([Blackbox, Dynaspark, GeminiPro, Gemini, Liaobots])
)
gemini_2_0_flash_thinking = Model(
name = 'gemini-2.0-flash-thinking',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([PollinationsAI, Liaobots, Gemini])
)
gemini_2_0_flash_thinking_with_apps = Model(
name = 'gemini-2.0-flash-thinking-with-apps',
base_provider = 'Google DeepMind',
best_provider = Gemini
)
# gemini-2.5
gemini_2_5_flash = Model(
name = 'gemini-2.5-flash',
base_provider = 'Google DeepMind',
best_provider = PollinationsAI
)
gemini_2_5_pro = Model(
name = 'gemini-2.5-pro',
base_provider = 'Google DeepMind',
best_provider = Liaobots
)
# gemma
gemma_2_9b = Model(
name = 'gemma-2-9b',
base_provider = 'Google DeepMind',
best_provider = Blackbox
)
gemma_3_12b = Model(
name = 'gemma-3-12b',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([Blackbox, DeepInfraChat])
)
gemma_3_1b = Model(
name = 'gemma-3-1b',
base_provider = 'Google DeepMind',
best_provider = Blackbox
)
gemma_3_27b = Model(
name = 'gemma-3-27b',
base_provider = 'Google DeepMind',
best_provider = IterListProvider([Blackbox, DeepInfraChat])
)
gemma_3_4b = Model(
name = 'gemma-3-4b',
base_provider = 'Google DeepMind',
best_provider = Blackbox
)
### Anthropic ###
# claude 3
claude_3_haiku = Model(
name = 'claude-3-haiku',
base_provider = 'Anthropic',
best_provider = DDG
)
# claude 3.5
claude_3_5_sonnet = Model(
name = 'claude-3.5-sonnet',
base_provider = 'Anthropic',
best_provider = IterListProvider([Blackbox, Liaobots])
)
# claude 3.7
claude_3_7_sonnet = Model(
name = 'claude-3.7-sonnet',
base_provider = 'Anthropic',
best_provider = IterListProvider([Blackbox, Liaobots])
)
### Reka AI ###
reka_core = Model(
name = 'reka-core',
base_provider = 'Reka AI',
best_provider = Reka
)
reka_flash = Model(
name = 'reka-flash',
base_provider = 'Reka AI',
best_provider = Blackbox
)
### Blackbox AI ###
blackboxai = Model(
name = 'blackboxai',
base_provider = 'Blackbox AI',
best_provider = Blackbox
)
### CohereForAI ###
command_r = Model(
name = 'command-r',
base_provider = 'CohereForAI',
best_provider = HuggingSpace
)
command_r_plus_08_2024 = Model(
name = 'command-r-plus-08-2024',
base_provider = 'CohereForAI',
best_provider = IterListProvider([PollinationsAI, HuggingSpace])
)
command_r_08_2024 = Model(
name = 'command-r-08-2024',
base_provider = 'CohereForAI',
best_provider = HuggingSpace
)
command_r_plus = Model(
name = 'command-r-plus',
base_provider = 'CohereForAI',
best_provider = IterListProvider([HuggingSpace, HuggingChat])
)
command_r7b_12_2024 = Model(
name = 'command-r7b-12-2024',
base_provider = 'CohereForAI',
best_provider = HuggingSpace
)
command_r7b_arabic_02_2025 = Model(
name = 'command-r7b-arabic-02-2025',
base_provider = 'CohereForAI',
best_provider = HuggingSpace
)
command_a_03_2025 = Model(
name = 'command-a-03-2025',
base_provider = 'CohereForAI',
best_provider = HuggingSpace
)
### Qwen ###
# qwen-1.5
qwen_1_5_7b = Model(
name = 'qwen-1.5-7b',
base_provider = 'Qwen',
best_provider = Cloudflare
)
# qwen-2
qwen_2_72b = Model(
name = 'qwen-2-72b',
base_provider = 'Qwen',
best_provider = IterListProvider([DeepInfraChat, HuggingSpace])
)
qwen_2_vl_7b = VisionModel(
name = "qwen-2-vl-7b",
base_provider = 'Qwen',
best_provider = HuggingFaceAPI
)
# qwen-2.5
qwen_2_5 = Model(
name = 'qwen-2.5',
base_provider = 'Qwen',
best_provider = HuggingSpace
)
qwen_2_5_7b = Model(
name = 'qwen-2.5-7b',
base_provider = 'Qwen',
best_provider = Blackbox
)
qwen_2_5_72b = Model(
name = 'qwen-2.5-72b',
base_provider = 'Qwen',
best_provider = Blackbox
)
qwen_2_5_coder_32b = Model(
name = 'qwen-2.5-coder-32b',
base_provider = 'Qwen',
best_provider = IterListProvider([Blackbox, PollinationsAI, LambdaChat, HuggingChat])
)
qwen_2_5_1m = Model(
name = 'qwen-2.5-1m',
base_provider = 'Qwen',
best_provider = HuggingSpace
)
qwen_2_5_max = Model(
name = 'qwen-2-5-max',
base_provider = 'Qwen',
best_provider = HuggingSpace
)
qwen_2_5_vl_3b = Model(
name = 'qwen-2.5-vl-3b',
base_provider = 'Qwen',
best_provider = Blackbox
)
qwen_2_5_vl_7b = Model(
name = 'qwen-2.5-vl-7b',
base_provider = 'Qwen',
best_provider = Blackbox
)
qwen_2_5_vl_32b = Model(
name = 'qwen-2.5-vl-32b',
base_provider = 'Qwen',
best_provider = Blackbox
)
qwen_2_5_vl_72b = Model(
name = 'qwen-2.5-vl-72b',
base_provider = 'Qwen',
best_provider = Blackbox
)
# qwen3
qwen_3_235b = Model(
name = 'qwen3-235b',
base_provider = 'Qwen',
best_provider = IterListProvider([DeepInfraChat, HuggingSpace, Liaobots])
)
qwen_3_32b = Model(
name = 'qwen3-32b',
base_provider = 'Qwen',
best_provider = IterListProvider([DeepInfraChat, HuggingSpace])
)
qwen_3_30b = Model(
name = 'qwen3-30b',
base_provider = 'Qwen',
best_provider = IterListProvider([DeepInfraChat, HuggingSpace])
)
qwen_3_14b = Model(
name = 'qwen3-14b',
base_provider = 'Qwen',
best_provider = IterListProvider([DeepInfraChat, HuggingSpace])
)
qwen_3_4b = Model(
name = 'qwen3-4b',
base_provider = 'Qwen',
best_provider = HuggingSpace
)
qwen_3_1_7b = Model(
name = 'qwen3-1.7b',
base_provider = 'Qwen',
best_provider = HuggingSpace
)
qwen_3_0_6b = Model(
name = 'qwen3-0.6b',
base_provider = 'Qwen',
best_provider = HuggingSpace
)
### qwq/qvq ###
qwq_32b = Model(
name = 'qwq-32b',
base_provider = 'Qwen',
best_provider = IterListProvider([Blackbox, DeepInfraChat, PollinationsAI, HuggingChat])
)
qwq_32b_preview = Model(
name = 'qwq-32b-preview',
base_provider = 'Qwen',
best_provider = Blackbox
)
qwq_32b_arliai = Model(
name = 'qwq-32b-arliai',
base_provider = 'Qwen',
best_provider = Blackbox
)
qvq_72b = VisionModel(
name = 'qvq-72b',
base_provider = 'Qwen',
best_provider = HuggingSpace
)
### Inflection ###
pi = Model(
name = 'pi',
base_provider = 'Inflection',
best_provider = Pi
)
### DeepSeek ###
# deepseek-r3
deepseek_v3 = Model(
name = 'deepseek-v3',
base_provider = 'DeepSeek',
best_provider = IterListProvider([DeepInfraChat, PollinationsAI, TypeGPT, Liaobots])
)
# deepseek-r1
deepseek_r1 = Model(
name = 'deepseek-r1',
base_provider = 'DeepSeek',
best_provider = IterListProvider([Blackbox, DeepInfraChat, LambdaChat, PollinationsAI, TypeGPT, HuggingChat, HuggingFace])
)
deepseek_r1_zero = Model(
name = 'deepseek-r1-zero',
base_provider = 'DeepSeek',
best_provider = Blackbox
)
deepseek_r1_turbo = Model(
name = 'deepseek-r1-turbo',
base_provider = 'DeepSeek',
best_provider = DeepInfraChat
)
deepseek_r1_distill_llama_70b = Model(
name = 'deepseek-r1-distill-llama-70b',
base_provider = 'DeepSeek',
best_provider = IterListProvider([Blackbox, DeepInfraChat, PollinationsAI])
)
deepseek_r1_distill_qwen_14b = Model(
name = 'deepseek-r1-distill-qwen-14b',
base_provider = 'DeepSeek',
best_provider = Blackbox
)
deepseek_r1_distill_qwen_32b = Model(
name = 'deepseek-r1-distill-qwen-32b',
base_provider = 'DeepSeek',
best_provider = IterListProvider([Blackbox, DeepInfraChat, PollinationsAI])
)
# deepseek-v2
deepseek_prover_v2_671b = Model(
name = 'deepseek-prover-v2-671b',
base_provider = 'DeepSeek',
best_provider = DeepInfraChat
)
# deepseek-v3-0324
deepseek_v3_0324 = Model(
name = 'deepseek-v3-0324',
base_provider = 'DeepSeek',
best_provider = IterListProvider([DeepInfraChat, PollinationsAI])
)
# janus
janus_pro_7b = VisionModel(
name = DeepseekAI_JanusPro7b.default_model,
base_provider = 'DeepSeek',
best_provider = DeepseekAI_JanusPro7b
)
### x.ai ###
grok_3 = Model(
name = 'grok-3',
base_provider = 'x.ai',
best_provider = IterListProvider([Grok, Liaobots])
)
grok_3_r1 = Model(
name = 'grok-3-r1',
base_provider = 'x.ai',
best_provider = IterListProvider([Grok, Liaobots])
)
### Perplexity AI ###
sonar = Model(
name = 'sonar',
base_provider = 'Perplexity AI',
best_provider = PerplexityLabs
)
sonar_pro = Model(
name = 'sonar-pro',
base_provider = 'Perplexity AI',
best_provider = PerplexityLabs
)
sonar_reasoning = Model(
name = 'sonar-reasoning',
base_provider = 'Perplexity AI',
best_provider = PerplexityLabs
)
sonar_reasoning_pro = Model(
name = 'sonar-reasoning-pro',
base_provider = 'Perplexity AI',
best_provider = PerplexityLabs
)
r1_1776 = Model(
name = 'r1-1776',
base_provider = 'Perplexity AI',
best_provider = PerplexityLabs
)
### Nvidia ###
nemotron_49b = Model(
name = 'nemotron-49b',
base_provider = 'Nvidia',
best_provider = Blackbox
)
nemotron_70b = Model(
name = 'nemotron-70b',
base_provider = 'Nvidia',
best_provider = IterListProvider([LambdaChat, HuggingChat, HuggingFace])
)
nemotron_253b = Model(
name = 'nemotron-253b',
base_provider = 'Nvidia',
best_provider = Blackbox
)
### THUDM ###
glm_4 = Model(
name = 'glm-4',
base_provider = 'THUDM',
best_provider = ChatGLM
)
### MiniMax ###
mini_max = Model(
name = "MiniMax",
base_provider = "MiniMax",
best_provider = HailuoAI
)
### Cognitive Computations ###
dolphin_2_6 = Model(
name = "dolphin-2.6",
base_provider = "Cognitive Computations",
best_provider = DeepInfraChat
)
dolphin_2_9 = Model(
name = "dolphin-2.9",
base_provider = "Cognitive Computations",
best_provider = DeepInfraChat
)
dolphin_3_0_24b = Model(
name = "dolphin-3.0-24b",
base_provider = "Cognitive Computations",
best_provider = Blackbox
)
dolphin_3_0_r1_24b = Model(
name = "dolphin-3.0-r1-24b",
base_provider = "Cognitive Computations",
best_provider = Blackbox
)
### DeepInfra ###
airoboros_70b = Model(
name = "airoboros-70b",
base_provider = "DeepInfra",
best_provider = DeepInfraChat
)
### Lizpreciatior ###
lzlv_70b = Model(
name = "lzlv-70b",
base_provider = "Lizpreciatior",
best_provider = DeepInfraChat
)
### Ai2 ###
molmo_7b = Model(
name = "molmo-7b",
base_provider = "Ai2",
best_provider = Blackbox
)
### Liquid AI ###
lfm_40b = Model(
name = "lfm-40b",
base_provider = "Liquid AI",
best_provider = LambdaChat
)
### Agentica ###
deepcode_14b = Model(
name = "deepcoder-14b",
base_provider = "Agentica",
best_provider = Blackbox
)
### Moonshot AI ###
kimi_vl_a3b_thinking = Model(
name = "kimi-vl-a3b-thinking",
base_provider = "Moonshot AI",
best_provider = Blackbox
)
moonlight_16b = Model(
name = "moonlight-16b",
base_provider = "Moonshot AI",
best_provider = Blackbox
)
### Featherless Serverless LLM ###
qwerky_72b = Model(
name = 'qwerky-72b',
base_provider = 'Featherless Serverless LLM',
best_provider = Blackbox
)
### Uncensored AI ###
evil = Model(
name = 'evil',
base_provider = 'Evil Mode - Experimental',
best_provider = IterListProvider([PollinationsAI, TypeGPT])
)
#############
### Image ###
#############
### Stability AI ###
sdxl_turbo = ImageModel(
name = 'sdxl-turbo',
base_provider = 'Stability AI',
best_provider = IterListProvider([PollinationsImage, ImageLabs])
)
sd_3_5 = ImageModel(
name = 'stable-diffusion-3.5-large',
base_provider = 'Stability AI',
best_provider = HuggingSpace
)
### Black Forest Labs ###
flux = ImageModel(
name = 'flux',
base_provider = 'Black Forest Labs',
best_provider = IterListProvider([PollinationsImage, Websim, HuggingSpace, ARTA])
)
flux_pro = ImageModel(
name = 'flux-pro',
base_provider = 'Black Forest Labs',
best_provider = PollinationsImage
)
flux_dev = ImageModel(
name = 'flux-dev',
base_provider = 'Black Forest Labs',
best_provider = IterListProvider([PollinationsImage, HuggingSpace, HuggingChat, HuggingFace])
)
flux_schnell = ImageModel(
name = 'flux-schnell',
base_provider = 'Black Forest Labs',
best_provider = IterListProvider([PollinationsImage, HuggingSpace, HuggingChat, HuggingFace])
)
### OpenAI ###
dall_e_3 = ImageModel(
name = 'dall-e-3',
base_provider = 'OpenAI',
best_provider = IterListProvider([PollinationsImage, CopilotAccount, OpenaiAccount, MicrosoftDesigner, BingCreateImages])
)
### Midjourney ###
midjourney = ImageModel(
name = 'midjourney',
base_provider = 'Midjourney',
best_provider = PollinationsImage
)
class ModelUtils:
"""
Utility class for mapping string identifiers to Model instances.
Attributes:
convert (dict[str, Model]): Dictionary mapping model string identifiers to Model instances.
"""
convert: dict[str, Model] = {
############
### Text ###
############
### OpenAI ###
# gpt-4
gpt_4.name: gpt_4,
gpt_4_turbo.name: gpt_4_turbo,
# gpt-4.1
gpt_4_1.name: gpt_4_1,
gpt_4_1_nano.name: gpt_4_1_nano,
gpt_4_1_mini.name: gpt_4_1_mini,
# gpt-4o
gpt_4o.name: gpt_4o,
gpt_4o_mini.name: gpt_4o_mini,
gpt_4o_audio.name: gpt_4o_audio,
# o1
o1.name: o1,
o1_mini.name: o1_mini,
# o3
o3_mini.name: o3_mini,
# o4
o4_mini.name: o4_mini,
### Meta ###
meta.name: meta,
# llama-2
llama_2_7b.name: llama_2_7b,
# llama-3
llama_3_8b.name: llama_3_8b,
# llama-3.1
llama_3_1_8b.name: llama_3_1_8b,
# llama-3.2
llama_3_2_1b.name: llama_3_2_1b,
llama_3_2_3b.name: llama_3_2_3b,
llama_3_2_11b.name: llama_3_2_11b,
llama_3_2_90b.name: llama_3_2_90b,
# llama-3.3
llama_3_3_70b.name: llama_3_3_70b,
# llama-4
llama_4_scout.name: llama_4_scout,
llama_4_scout_17b.name: llama_4_scout_17b,
llama_4_maverick.name: llama_4_maverick,
llama_4_maverick_17b.name: llama_4_maverick_17b,
### Mistral ###
mistral_7b.name: mistral_7b,
mixtral_8x22b.name: mixtral_8x22b,
mistral_nemo.name: mistral_nemo,
mistral_small_24b.name: mistral_small_24b,
mistral_small_3_1_24b.name: mistral_small_3_1_24b,
### NousResearch ###
hermes_3.name: hermes_3,
hermes_3_405b.name: hermes_3_405b,
deephermes_3_8b.name: deephermes_3_8b,
### Microsoft ###
# phi
phi_3_5_mini.name: phi_3_5_mini,
phi_4.name: phi_4,
phi_4_multimodal.name: phi_4_multimodal,
phi_4_reasoning_plus.name: phi_4_reasoning_plus,
# wizardlm
wizardlm_2_7b.name: wizardlm_2_7b,
wizardlm_2_8x22b.name: wizardlm_2_8x22b,
### Google ###
### gemini
"gemini": gemini,
gemini.name: gemini,
# gemini-1.0
gemini_1_0_pro.name: gemini_1_0_pro,
# gemini-1.5
gemini_1_5_pro.name: gemini_1_5_pro,
gemini_1_5_flash.name: gemini_1_5_flash,
# gemini-2.0
gemini_2_0_flash.name: gemini_2_0_flash,
gemini_2_0_flash_thinking.name: gemini_2_0_flash_thinking,
gemini_2_0_flash_thinking_with_apps.name: gemini_2_0_flash_thinking_with_apps,
# gemini-2.5
gemini_2_5_flash.name: gemini_2_5_flash,
gemini_2_5_pro.name: gemini_2_5_pro,
# gemma
gemma_2_9b.name: gemma_2_9b,
gemma_3_12b.name: gemma_3_12b,
gemma_3_1b.name: gemma_3_1b,
gemma_3_27b.name: gemma_3_27b,
gemma_3_4b.name: gemma_3_4b,
### Anthropic ###
# claude 3
claude_3_haiku.name: claude_3_haiku,
# claude 3.5
claude_3_5_sonnet.name: claude_3_5_sonnet,
# claude 3.7
claude_3_7_sonnet.name: claude_3_7_sonnet,
### Reka AI ###
reka_core.name: reka_core,
reka_flash.name: reka_flash,
### Blackbox AI ###
blackboxai.name: blackboxai,
### CohereForAI ###
command_r.name: command_r,
command_r_plus_08_2024.name: command_r_plus_08_2024,
command_r_08_2024.name: command_r_08_2024,
command_r_plus.name: command_r_plus,
command_r7b_12_2024.name: command_r7b_12_2024,
command_r7b_arabic_02_2025.name: command_r7b_arabic_02_2025,
command_a_03_2025.name: command_a_03_2025,
### Qwen ###
# qwen-1.5
qwen_1_5_7b.name: qwen_1_5_7b,
# qwen-2
qwen_2_72b.name: qwen_2_72b,
qwen_2_vl_7b.name: qwen_2_vl_7b,
# qwen-2.5
qwen_2_5.name: qwen_2_5,
qwen_2_5_7b.name: qwen_2_5_7b,
qwen_2_5_72b.name: qwen_2_5_72b,
qwen_2_5_coder_32b.name: qwen_2_5_coder_32b,
qwen_2_5_1m.name: qwen_2_5_1m,
qwen_2_5_max.name: qwen_2_5_max,
qwen_2_5_vl_3b.name: qwen_2_5_vl_3b,
qwen_2_5_vl_7b.name: qwen_2_5_vl_7b,
qwen_2_5_vl_32b.name: qwen_2_5_vl_32b,
qwen_2_5_vl_72b.name: qwen_2_5_vl_72b,
# qwen-2.5
qwen_3_235b.name: qwen_3_235b,
qwen_3_32b.name: qwen_3_32b,
qwen_3_30b.name: qwen_3_30b,
qwen_3_14b.name: qwen_3_14b,
qwen_3_4b.name: qwen_3_4b,
qwen_3_1_7b.name: qwen_3_1_7b,
qwen_3_0_6b.name: qwen_3_0_6b,
# qwq/qvq
qwq_32b.name: qwq_32b,
qwq_32b_preview.name: qwq_32b_preview,
qwq_32b_arliai.name: qwq_32b_arliai,
qvq_72b.name: qvq_72b,
### Inflection ###
pi.name: pi,
### x.ai ###
grok_3.name: grok_3,
### Perplexity AI ###
sonar.name: sonar,
sonar_pro.name: sonar_pro,
sonar_reasoning.name: sonar_reasoning,
sonar_reasoning_pro.name: sonar_reasoning_pro,
r1_1776.name: r1_1776,
### DeepSeek ###
# deepseek-v3
deepseek_v3.name: deepseek_v3,
# deepseek-r1
deepseek_r1.name: deepseek_r1,
deepseek_r1_zero.name: deepseek_r1_zero,
deepseek_r1_turbo.name: deepseek_r1_turbo,
deepseek_r1_distill_llama_70b.name: deepseek_r1_distill_llama_70b,
deepseek_r1_distill_qwen_14b.name: deepseek_r1_distill_qwen_14b,
deepseek_r1_distill_qwen_32b.name: deepseek_r1_distill_qwen_32b,
# deepseek-v2
deepseek_prover_v2_671b.name: deepseek_prover_v2_671b,
# deepseek-v3-0324
deepseek_v3_0324.name: deepseek_v3_0324,
### Nvidia ###
nemotron_49b.name: nemotron_49b,
nemotron_70b.name: nemotron_70b,
nemotron_253b.name: nemotron_253b,
### THUDM ###
glm_4.name: glm_4,
## MiniMax ###
mini_max.name: mini_max,
### Cognitive Computations ###
dolphin_2_6.name: dolphin_2_6,
dolphin_2_9.name: dolphin_2_9,
dolphin_3_0_24b.name: dolphin_3_0_24b,
dolphin_3_0_r1_24b.name: dolphin_3_0_r1_24b,
### DeepInfra ###
airoboros_70b.name: airoboros_70b,
### Lizpreciatior ###
lzlv_70b.name: lzlv_70b,
### Ai2 ###
molmo_7b.name: molmo_7b,
### Liquid AI ###
lfm_40b.name: lfm_40b,
deepcode_14b.name: deepcode_14b,
### Moonshot AI ###
moonlight_16b.name: moonlight_16b,
### Featherless Serverless LLM ###
qwerky_72b.name: qwerky_72b,
### Uncensored AI ###
evil.name: evil,
#############
### Image ###
#############
### Stability AI ###
sdxl_turbo.name: sdxl_turbo,
sd_3_5.name: sd_3_5,
### Flux AI ###
flux.name: flux,
flux_pro.name: flux_pro,
flux_dev.name: flux_dev,
flux_schnell.name: flux_schnell,
### OpenAI ###
dall_e_3.name: dall_e_3,
### Midjourney ###
midjourney.name: midjourney,
}
demo_models = {
llama_3_2_11b.name: [llama_3_2_11b, [HuggingChat]],
qwen_2_vl_7b.name: [qwen_2_vl_7b, [HuggingFaceAPI]],
deepseek_r1.name: [deepseek_r1, [HuggingFace, PollinationsAI]],
janus_pro_7b.name: [janus_pro_7b, [HuggingSpace]],
command_r.name: [command_r, [HuggingSpace]],
command_r_plus.name: [command_r_plus, [HuggingSpace]],
command_r7b_12_2024.name: [command_r7b_12_2024, [HuggingSpace]],
qwen_2_5_coder_32b.name: [qwen_2_5_coder_32b, [HuggingFace]],
qwq_32b.name: [qwq_32b, [HuggingFace]],
llama_3_3_70b.name: [llama_3_3_70b, [HuggingFace]],
sd_3_5.name: [sd_3_5, [HuggingSpace, HuggingFace]],
flux_dev.name: [flux_dev, [PollinationsImage, HuggingFace, HuggingSpace]],
flux_schnell.name: [flux_schnell, [PollinationsImage, HuggingFace, HuggingSpace]],
}
# Create a list of all models and his providers
__models__ = {
model.name: (model, providers)
for model, providers in [
(model, [provider for provider in model.best_provider.providers if provider.working]
if isinstance(model.best_provider, IterListProvider)
else [model.best_provider]
if model.best_provider is not None and model.best_provider.working
else [])
for model in ModelUtils.convert.values()]
if model.name and [True for provider in providers if provider.working]
}
_all_models = list(__models__.keys())
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