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ecbe3e3a21
- GPT-5.6 (sol/terra/luna) is GA and OpenAI's recommended default; add it and make gpt-5.6 (deep) / gpt-5.6-luna (quick) the defaults - add GLM-5.3 and GLM-5.3-Flash, Zhipu's current flagship line
211 lines
9.2 KiB
Python
211 lines
9.2 KiB
Python
"""Shared model catalog for CLI selections and validation."""
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from __future__ import annotations
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ModelOption = tuple[str, str]
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ProviderModeOptions = dict[str, dict[str, list[ModelOption]]]
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# Providers that serve many / frequently-changing models: offer only "Custom
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# model ID" rather than a list that goes stale.
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_CUSTOM_ONLY: dict[str, list[ModelOption]] = {
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"quick": [("Custom model ID", "custom")],
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"deep": [("Custom model ID", "custom")],
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}
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# Shared model list for GLM via Z.AI (international) and BigModel (China).
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# Source: docs.z.ai (GLM Coding Plan supported models + LLM guides).
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# All GLM 4.7+ entries support thinking mode via thinking={"type":"enabled"}.
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_GLM_MODELS: dict[str, list[ModelOption]] = {
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"quick": [
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("GLM-5.3-Flash - Fast, cost-efficient, 1M ctx", "glm-5.3-flash"),
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("GLM-5-Turbo - Fast, switchable thinking modes", "glm-5-turbo"),
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("GLM-4.5-Air - Lightweight, cost-efficient", "glm-4.5-air"),
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("Custom model ID", "custom"),
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],
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"deep": [
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("GLM-5.3 - Latest flagship, 1M ctx", "glm-5.3"),
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("GLM-5.2 - 744B, 1M ctx", "glm-5.2"),
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("GLM-5.1 - 745B, 200K ctx", "glm-5.1"),
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("GLM-4.7 - Previous-gen flagship", "glm-4.7"),
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("Custom model ID", "custom"),
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],
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}
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# Shared model list for Qwen's global (dashscope-intl) and CN (dashscope) endpoints.
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# Source: modelstudio.console.alibabacloud.com (Featured Models — Flagship + Cost-optimized).
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#
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# Only versioned IDs are exposed in the dropdown. The version-less aliases
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# (qwen-plus, qwen-flash) are documented by Alibaba as auto-upgrading
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# pointers ("backbone, latest, and snapshot ... have been upgraded to the
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# Qwen3 series"), which means their behavior shifts when Alibaba rotates
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# the backing model. Users who want a specific generation pick it
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# explicitly; users who really want auto-latest can enter the alias via
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# "Custom model ID".
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_QWEN_MODELS: dict[str, list[ModelOption]] = {
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"quick": [
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("Qwen 3.7 Plus - Latest, balanced speed/cost", "qwen3.7-plus"),
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("Qwen 3.6 Plus - Previous-gen balanced", "qwen3.6-plus"),
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("Custom model ID", "custom"),
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],
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"deep": [
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("Qwen 3.7 Max - Latest flagship, most intelligent, 1M ctx", "qwen3.7-max"),
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("Qwen 3.6 Max - Previous-gen flagship", "qwen3.6-max"),
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("Qwen 3.7 Plus - Balanced alternative", "qwen3.7-plus"),
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("Custom model ID", "custom"),
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],
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}
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# Shared model list for MiniMax's global and CN endpoints (same IDs).
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# Full official lineup per platform.minimax.io/docs/api-reference/text-openai-api.
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# M3 carries a 1M-token context window; the M2.x line is 204,800 tokens.
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_MINIMAX_MODELS: dict[str, list[ModelOption]] = {
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"quick": [
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("MiniMax-M3 - Latest, 1M ctx, native multimodal", "MiniMax-M3"),
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("MiniMax-M2.7-highspeed - Fast M2.7, 204K ctx, ~100 TPS", "MiniMax-M2.7-highspeed"),
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("MiniMax-M2.5-highspeed - Previous-gen highspeed, 204K ctx", "MiniMax-M2.5-highspeed"),
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("Custom model ID", "custom"),
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],
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"deep": [
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("MiniMax-M3 - Latest flagship, 1M ctx, multimodal coding/agent", "MiniMax-M3"),
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("MiniMax-M2.7 - Previous flagship, 204K ctx", "MiniMax-M2.7"),
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("MiniMax-M2.7-highspeed - Same quality as M2.7, ~100 TPS", "MiniMax-M2.7-highspeed"),
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("MiniMax-M2.5 - Earlier flagship, 204K ctx", "MiniMax-M2.5"),
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("Custom model ID", "custom"),
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],
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}
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MODEL_OPTIONS: ProviderModeOptions = {
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"openai": {
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"quick": [
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("GPT-5.6 Luna - Fast, cost-efficient frontier", "gpt-5.6-luna"),
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("GPT-5.6 Terra - Balances intelligence and cost", "gpt-5.6-terra"),
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("GPT-5.4 Mini - Fast, strong coding and tool use", "gpt-5.4-mini"),
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],
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"deep": [
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("GPT-5.6 - Latest frontier reasoning (Sol)", "gpt-5.6"),
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("GPT-5.6 Terra - Balances intelligence and cost", "gpt-5.6-terra"),
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("GPT-5.5 - Previous-gen frontier, 1M context", "gpt-5.5"),
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("GPT-5.4 - Cost-effective, 1M context", "gpt-5.4"),
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],
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},
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"anthropic": {
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"quick": [
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("Claude Sonnet 5 - Best speed and intelligence balance", "claude-sonnet-5"),
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("Claude Haiku 4.5 - Fastest with near-frontier intelligence", "claude-haiku-4-5"),
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],
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"deep": [
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("Claude Fable 5 - Most capable, long-running agents", "claude-fable-5"),
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("Claude Opus 4.8 - Frontier agentic coding and reasoning", "claude-opus-4-8"),
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("Claude Sonnet 5 - Near-frontier intelligence at Sonnet cost", "claude-sonnet-5"),
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("Claude Opus 4.7 - Previous frontier, long-running agents", "claude-opus-4-7"),
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],
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},
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"google": {
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"quick": [
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("Gemini 3.5 Flash - Latest, frontier agentic + coding (GA)", "gemini-3.5-flash"),
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("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite"),
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],
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"deep": [
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("Gemini 3.1 Pro - Reasoning-first, complex workflows (preview)", "gemini-3.1-pro-preview"),
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("Gemini 3.5 Flash - Latest GA, strong agentic + coding", "gemini-3.5-flash"),
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],
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},
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"xai": {
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"quick": [
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("Grok 4.3 - Latest flagship, fast with built-in reasoning", "grok-4.3"),
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("Grok 4.20 (Non-Reasoning) - Speed-optimized", "grok-4.20-0309-non-reasoning"),
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("Grok Build 0.1 - Coding-specialized, 256K ctx", "grok-build-0.1"),
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],
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"deep": [
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("Grok 4.3 - Latest flagship, built-in reasoning, 1M ctx", "grok-4.3"),
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("Grok 4.20 (Reasoning) - Previous-gen reasoning", "grok-4.20-0309-reasoning"),
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("Grok 4.20 Multi-Agent - Multi-agent reasoning", "grok-4.20-multi-agent-0309"),
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],
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},
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# DeepSeek: the deepseek-chat / deepseek-reasoner aliases are deprecated
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# (2026-07-24) and now map to V4 Flash; expose the V4 IDs directly. V4 Flash
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# serves both non-thinking and thinking modes (the DeepSeekChatOpenAI client
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# handles the reasoning_content round-trip).
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"deepseek": {
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"quick": [
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("DeepSeek V4 Flash - Latest fast model, thinking + non-thinking", "deepseek-v4-flash"),
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("Custom model ID", "custom"),
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],
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"deep": [
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("DeepSeek V4 Pro - Latest flagship", "deepseek-v4-pro"),
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("DeepSeek V4 Flash - Fast, supports thinking", "deepseek-v4-flash"),
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("Custom model ID", "custom"),
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],
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},
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# Qwen: same model IDs across global (dashscope-intl) and China
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# (dashscope) endpoints, so the two provider keys share one model list.
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"qwen": _QWEN_MODELS,
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"qwen-cn": _QWEN_MODELS,
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# GLM: Z.AI (international) and BigModel (China) host the same model
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# IDs; the two provider keys share one model list.
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"glm": _GLM_MODELS,
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"glm-cn": _GLM_MODELS,
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# MiniMax: same model IDs across global (.io) and China (.com) regions,
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# so the two provider keys share one model list.
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"minimax": _MINIMAX_MODELS,
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"minimax-cn": _MINIMAX_MODELS,
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# OpenRouter: fetched dynamically. Azure: any deployed model name.
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# Ollama display labels intentionally omit a "local" marker — the
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# endpoint is now configurable via OLLAMA_BASE_URL, so the same labels
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# apply whether the user runs ollama-serve on localhost or against a
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# remote host. The actual resolved endpoint is surfaced separately by
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# cli.utils.confirm_ollama_endpoint() right after provider selection.
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# "Custom model ID" lets users pick any model they have pulled via
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# `ollama pull` beyond the three suggested defaults.
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"ollama": {
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"quick": [
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("Qwen3:latest (8B)", "qwen3:latest"),
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("GPT-OSS:latest (20B)", "gpt-oss:latest"),
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("GLM-4.7-Flash:latest (30B)", "glm-4.7-flash:latest"),
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("Custom model ID", "custom"),
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],
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"deep": [
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("GLM-4.7-Flash:latest (30B)", "glm-4.7-flash:latest"),
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("GPT-OSS:latest (20B)", "gpt-oss:latest"),
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("Qwen3:latest (8B)", "qwen3:latest"),
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("Custom model ID", "custom"),
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],
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},
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# Generic OpenAI-compatible endpoint: the model is whatever the user's
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# server serves, so only "Custom model ID" is offered.
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"openai_compatible": _CUSTOM_ONLY,
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# Hosted OpenAI-compatible providers that serve many (and frequently
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# changing) models — offer "Custom model ID" rather than a list that goes
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# stale. The endpoint + key are wired by the provider; the user picks the
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# model their account has access to.
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"mistral": _CUSTOM_ONLY,
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"kimi": _CUSTOM_ONLY,
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"groq": _CUSTOM_ONLY,
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"nvidia": _CUSTOM_ONLY,
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# Bedrock model IDs / cross-region inference profile IDs are user-specified.
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"bedrock": _CUSTOM_ONLY,
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}
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def get_model_options(provider: str, mode: str) -> list[ModelOption]:
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"""Return shared model options for a provider and selection mode."""
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return MODEL_OPTIONS[provider.lower()][mode]
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def get_known_models() -> dict[str, list[str]]:
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"""Build known model names from the shared CLI catalog."""
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return {
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provider: sorted(
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{
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value
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for options in mode_options.values()
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for _, value in options
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}
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)
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for provider, mode_options in MODEL_OPTIONS.items()
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}
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