OpenAI GPT-5.6 Sol/Terra/Luna 系列模型深度解析：三体战略、政府监管与AI商业新范式

"""
GPT-5.6 自适应专家路由模拟器
模拟 Sol/Terra/Luna 在不同复杂度查询下的专家激活策略
"""
import numpy as np
from dataclasses import dataclass
from typing import List, Tuple

@dataclass
class ModelConfig:
    name: str
    total_experts: int
    max_active_experts: int
    embed_dim: int
    ff_dim: int

MODELS = {
    "sol": ModelConfig("GPT-5.6 Sol", 256, 32, 16384, 65536),
    "terra": ModelConfig("GPT-5.6 Terra", 128, 16, 8192, 32768),
    "luna": ModelConfig("GPT-5.6 Luna", 64, 8, 4096, 16384),
}

class AdaptiveRouter:
    """自适应查询复杂度预测器"""
    
    def __init__(self, model: ModelConfig):
        self.model = model
        # 路由预测器权重
        self.complexity_proj = np.random.randn(model.embed_dim, model.total_experts) * 0.01
        self.top_k = model.max_active_experts
        
    def predict_complexity(self, query_embedding: np.ndarray) -> Tuple[int, float]:
        """
        预测查询复杂度，返回（预估专家数, 置信度）
        
        Args:
            query_embedding: 查询嵌入向量 (embed_dim,)
        
        Returns:
            (n_experts, confidence)
        """
        # 计算各专家的路由分数
        scores = query_embedding @ self.complexity_proj  # (total_experts,)
        
        # 前k个专家的平均分作为复杂度指标
        top_k_scores = np.sort(scores)[-self.top_k:]
        complexity = float(np.mean(top_k_scores))
        
        # 将复杂度映射到[1, max_active_experts]
        # 使用sigmoid风格的映射
        n_experts = max(1, int(self.top_k * (1 / (1 + np.exp(-complexity / 2.0)))))
        
        # 置信度：基于分数方差
        variance = float(np.var(top_k_scores))
        confidence = min(1.0, variance / 10.0)
        
        return min(n_experts, self.top_k), confidence

class MoERouter:
    """MoE 专家路由分配器"""
    
    def __init__(self, model: ModelConfig):
        self.model = model
        self.load_balancer = np.zeros(model.total_experts)
        self.load_balance_alpha = 0.1  # 负载均衡系数
        
    def route(self, query_embedding: np.ndarray, n_experts: int) -> List[int]:
        """
        分配专家，包含负载均衡
        
        Args:
            query_embedding: 查询嵌入
            n_experts: 需要激活的专家数
        
        Returns:
            选中的专家ID列表
        """
        # 计算与各专家的相关性
        relevance = query_embedding @ np.random.randn(
            self.model.embed_dim, self.model.total_experts
        )
        
        # 引入负载均衡惩罚：避免热点专家过载
        load_penalty = self.load_balance_alpha * self.load_balancer
        adjusted_scores = relevance - load_penalty
        
        # 选择top-n
        selected = np.argsort(adjusted_scores)[-n_experts:].tolist()
        
        # 更新负载计数器
        for eid in selected:
            self.load_balancer[eid] += 1.0
        # 衰减历史负载
        self.load_balancer *= 0.99
        
        return selected


# 模拟不同复杂度查询下的表现
def simulate_routing():
    """模拟三类典型查询的路由行为"""
    queries = [
        ("简单问答：'今天天气如何？'", 0.1),
        ("中等推理：'分析这段代码的时间复杂度'", 0.5),
        ("复杂推理：'证明黎曼猜想对zeta函数零点的分布影响'", 1.0),
    ]
    
    for model_name in ["luna", "terra", "sol"]:
        config = MODELS[model_name]
        router = MoERouter(config)
        adaptive = AdaptiveRouter(config)
        
        print(f"\n=== {config.name} ===")
        print(f"总专家数: {config.total_experts}, 最大激活: {config.max_active_experts}")
        
        for query_desc, complexity in queries:
            # 模拟查询嵌入（用随机向量代替）
            query_emb = np.random.randn(config.embed_dim) * complexity
            n_pred, conf = adaptive.predict_complexity(query_emb)
            selected = router.route(query_emb, n_pred)
            
            print(f"  [{query_desc[:20]}...]")
            print(f"    预估专家数: {n_pred}/{config.max_active_experts} (置信度: {conf:.2f})")
            print(f"    实际激活: {len(selected)} 个专家")
            print(f"    激活率: {len(selected)/config.total_experts*100:.1f}%")

if __name__ == "__main__":
    simulate_routing()

"""
GPT-5.6 三模型蒸馏链路实现模拟
展示了层级蒸馏的核心算法
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Dict, List, Optional

class ExpertModule(nn.Module):
    """单个专家模块"""
    def __init__(self, embed_dim: int, ff_dim: int):
        super().__init__()
        self.w1 = nn.Linear(embed_dim, ff_dim)
        self.w2 = nn.Linear(ff_dim, embed_dim)
        self.w3 = nn.Linear(embed_dim, ff_dim)  # 用于门控
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # SwiGLU 激活
        gate = F.silu(self.w3(x))
        hidden = self.w1(x)
        return self.w2(gate * hidden)


class DistillationMoE(nn.Module):
    """支持层级蒸馏的MoE层"""
    
    def __init__(self, embed_dim: int, total_experts: int, top_k: int):
        super().__init__()
        self.embed_dim = embed_dim
        self.total_experts = total_experts
        self.top_k = top_k
        
        # 共享路由预测器（三模型共享）
        self.router = nn.Linear(embed_dim, total_experts, bias=False)
        
        # 专家模块列表
        self.experts = nn.ModuleList([
            ExpertModule(embed_dim, embed_dim * 4) 
            for _ in range(total_experts)
        ])
        
    def forward(self, x: torch.Tensor, 
                active_experts: Optional[List[int]] = None,
                temperature: float = 1.0) -> torch.Tensor:
        """
        前向传播，支持稀疏激活
        
        Args:
            x: 输入 (batch, seq, dim)
            active_experts: 手动指定激活的专家（蒸馏时使用）
            temperature: 路由softmax温度（越高越均匀）
        """
        batch, seq, dim = x.shape
        
        # 路由分数
        router_logits = self.router(x)  # (batch, seq, n_experts)
        
        if active_experts is not None:
            # 蒸馏模式：强制使用指定专家子集
            mask = torch.zeros_like(router_logits)
            mask[:, :, active_experts] = 1.0
            router_logits = router_logits.masked_fill(mask == 0, float('-inf'))
        
        # Softmax路由权重
        routing_weights = F.softmax(router_logits / temperature, dim=-1)
        
        # 只保留top_k
        top_k_weights, top_k_indices = torch.topk(routing_weights, self.top_k, dim=-1)
        top_k_weights = top_k_weights / (top_k_weights.sum(dim=-1, keepdim=True) + 1e-6)
        
        # 稀疏激活计算
        output = torch.zeros_like(x)
        for i in range(self.top_k):
            expert_idx = top_k_indices[..., i]
            weight = top_k_weights[..., i].unsqueeze(-1)
            
            # 批量处理同一专家的请求
            for eid in range(self.total_experts):
                mask = (expert_idx == eid)
                if mask.any():
                    expert_input = x[mask]
                    expert_output = self.experts[eid](expert_input)
                    output[mask] += weight[mask] * expert_output
        
        return output


class DistillationTrainer:
    """层级蒸馏训练器"""
    
    def __init__(self, sol_model: DistillationMoE, 
                 terra_model: DistillationMoE,
                 luna_model: DistillationMoE):
        self.models = {
            "sol": sol_model,
            "terra": terra_model,
            "luna": luna_model,
        }
        
    def distillation_loss(self, student_output: torch.Tensor,
                          teacher_output: torch.Tensor,
                          temperature: float = 4.0) -> torch.Tensor:
        """
        蒸馏损失：学生输出的分布要接近老师
        
        Args:
            student_output: 学生模型输出分布
            teacher_output: 教师模型输出分布
            temperature: 蒸馏温度
        """
        # KL散度损失
        student_log_softmax = F.log_softmax(student_output / temperature, dim=-1)
        teacher_softmax = F.softmax(teacher_output / temperature, dim=-1)
        
        kl_loss = F.kl_div(
            student_log_softmax, teacher_softmax,
            reduction='batchmean'
        ) * (temperature ** 2)
        
        return kl_loss
    
    def train_step(self, batch: Dict[str, torch.Tensor],
                   alpha_sol_terra: float = 0.5,
                   alpha_terra_luna: float = 0.3):
        """
        一次训练步骤
        
        Sol → Terra 蒸馏 (权重0.5)
        Terra → Luna 蒸馏 (权重0.3)
        """
        x = batch["input"]
        y = batch["target"]
        
        # Sol 前向（全量专家）
        sol_out = self.models["sol"](x, temperature=0.8)
        
        # Terra 前向（带Sol蒸馏）
        # Terra使用Sol路由结果的前k个专家
        terra_out = self.models["terra"](
            x, 
            active_experts=list(range(0, 128)),  # Terra的128专家
            temperature=1.0
        )
        
        # Luna 前向（带Terra蒸馏）
        luna_out = self.models["luna"](
            x,
            active_experts=list(range(0, 64)),  # Luna的64专家
            temperature=1.2
        )
        
        # 计算各层级蒸馏损失
        terra_distill_loss = self.distillation_loss(terra_out, sol_out.detach())
        luna_distill_loss = self.distillation_loss(luna_out, terra_out.detach())
        
        # 总损失 = 任务损失 + 蒸馏损失
        total_loss = (
            F.cross_entropy(sol_out, y) +
            alpha_sol_terra * terra_distill_loss +
            alpha_terra_luna * luna_distill_loss
        )
        
        return total_loss, {
            "sol_loss": F.cross_entropy(sol_out, y).item(),
            "terra_distill": terra_distill_loss.item(),
            "luna_distill": luna_distill_loss.item(),
        }


# 模拟蒸馏训练
def simulate_distillation():
    embed_dim = 4096
    batch = {
        "input": torch.randn(4, 128, embed_dim),
        "target": torch.randint(0, 50000, (4, 128)),
    }
    
    sol = DistillationMoE(embed_dim, 256, 32)
    terra = DistillationMoE(embed_dim, 128, 16)
    luna = DistillationMoE(embed_dim, 64, 8)
    
    trainer = DistillationTrainer(sol, terra, luna)
    
    total, losses = trainer.train_step(batch)
    print(f"总损失: {total.item():.4f}")
    print(f"  Sol任务损失: {losses['sol_loss']:.4f}")
    print(f"  Terra蒸馏损失: {losses['terra_distill']:.4f}")
    print(f"  Luna蒸馏损失: {losses['luna_distill']:.4f}")

if __name__ == "__main__":
    simulate_distillation()

/*
GPT-5.6 系列定价策略分析
对比竞品，计算性价比指标
*/
package main

import (
	"fmt"
	"math"
)

type ModelPricing struct {
	Name          string
	InputPrice    float64 // $/百万token
	OutputPrice   float64 // $/百万token
	ContextWindow int     // 上下文窗口大小
	QualityScore  float64 // 综合质量评分(0-100)
}

func main() {
	models := []ModelPricing{
		{"GPT-5.6 Sol", 5.0, 30.0, 262144, 97},
		{"GPT-5.6 Terra", 2.0, 10.0, 131072, 90},
		{"GPT-5.6 Luna", 0.5, 2.0, 65536, 78},
		{"Anthropic Fable 5", 10.0, 60.0, 262144, 98},
		{"Anthropic Mythos 5", 3.0, 15.0, 131072, 92},
		{"Google Gemini 3.5 Ultra", 8.0, 40.0, 1048576, 95},
		{"Google Gemini 3.5 Pro", 2.5, 12.0, 262144, 88},
		{"DeepSeek V5", 0.8, 3.0, 131072, 82},
	}

	type CostScenario struct {
		Name           string
		InputTokens    int64
		OutputTokens   int64
		DailyCalls     int
	}

	scenarios := []CostScenario{
		{"个人开发助手", 5000, 1500, 100},
		{"企业客服系统", 2000, 500, 50000},
		{"代码自动生成", 8000, 3000, 1000},
		{"科研论文分析", 50000, 10000, 50},
	}

	fmt.Println("=== 单次调用成本对比 ($) ===")
	fmt.Printf("%-25s %-12s %-12s %-12s %-12s\n",
		"模型", "个人开发", "企业客服", "代码生成", "科研分析")
	fmt.Println("------------------------------------------------------------------")

	for _, m := range models {
		fmt.Printf("%-25s", m.Name)
		for _, s := range scenarios {
			inputCost := m.InputPrice * float64(s.InputTokens) / 1_000_000
			outputCost := m.OutputPrice * float64(s.OutputTokens) / 1_000_000
			totalCost := inputCost + outputCost
			fmt.Printf("$%-11.5f", totalCost)
		}
		fmt.Println()
	}

	fmt.Println("\n=== 性价比指数 (质量分/每百万token成本) ===")
	fmt.Println("性价比越高，说明单位成本获得的质量越好")
	fmt.Println("------------------------------------------------------------------")

	type ValueIndex struct {
		Name     string
		Index    float64
	}

	var indices []ValueIndex
	for _, m := range models {
		avgCost := (m.InputPrice + m.OutputPrice) / 2.0
		valueIndex := m.QualityScore / avgCost
		indices = append(indices, ValueIndex{m.Name, valueIndex})
	}

	// 冒泡排序
	for i := 0; i < len(indices); i++ {
		for j := i + 1; j < len(indices); j++ {
			if indices[j].Index > indices[i].Index {
				indices[i], indices[j] = indices[j], indices[i]
			}
		}
	}

	for _, vi := range indices {
		fmt.Printf("%-25s 性价比指数: %.1f\n", vi.Name, vi.Index)
	}

	// 成本优化模拟器
	fmt.Println("\n=== Luna/Terra/Sol 自动路由成本优化 ===")
	simulateCostOptimization()
}

func simulateCostOptimization() {
	// 假设每日有10万次API调用，复杂度分布如下
	type QueryComplexity struct {
		Level    string
		Ratio    float64 // 占比
		BestModel string
	}

	distribution := []QueryComplexity{
		{"简单(分类/提取)", 0.45, "Luna"},
		{"中等(代码/分析)", 0.35, "Terra"},
		{"复杂(推理/研究)", 0.15, "Terra"},
		{"极限(科研/数学)", 0.05, "Sol"},
	}

	pricing := map[string]struct{ input, output float64 }{
		"Luna":  {0.5, 2.0},
		"Terra": {2.0, 10.0},
		"Sol":   {5.0, 30.0},
	}

	// 优化前：全部用Sol
	beforeCost := 100000 * (5.0*5.0/1_000_000 + 30.0*1.5/1_000_000)
	beforeCost *= 100000

	// 优化后：按复杂度路由
	var afterCost float64
	for _, q := range distribution {
		p := pricing[q.BestModel]
		calls := int(100000 * q.Ratio)
		costPerCall := p.input*5.0/1_000_000 + p.output*1.5/1_000_000
		afterCost += float64(calls) * costPerCall
	}

	savings := (1 - afterCost/beforeCost) * 100
	fmt.Printf("全部使用Sol: 每日 $%.2f\n", beforeCost)
	fmt.Printf("智能路由(Luna/Terra/Sol): 每日 $%.2f\n", afterCost)
	fmt.Printf("成本节省: %.1f%%\n", savings)
}

"""
GPT-5.6 多智能体场景下的智能模型分配器
根据任务复杂度自动选择 Sol/Terra/Luna
"""
import asyncio
from enum import Enum
from dataclasses import dataclass
from typing import Optional

class ModelTier(Enum):
    LUNA = "gpt-5.6-luna"
    TERRA = "gpt-5.6-terra"
    SOL = "gpt-5.6-sol"

@dataclass
class TaskProfile:
    complexity: float  # 0.0-1.0
    latency_sla_ms: float
    context_tokens: int
    output_tokens: int

class SmartRouter:
    """基于任务画像的智能路由"""
    
    def __init__(self, cost_budget: float = 100.0):
        self.cost_budget = cost_budget
        self.daily_spent = 0.0
        
    def select_model(self, task: TaskProfile) -> ModelTier:
        """根据任务特征选择最经济的模型"""
        # 高复杂度 + 长上下文 → Sol
        if task.complexity > 0.8 and task.context_tokens > 50000:
            return ModelTier.SOL
        
        # 低延迟要求 + 低复杂度 → Luna
        if task.latency_sla_ms < 500 and task.complexity < 0.4:
            return ModelTier.LUNA
        
        # 中等复杂度 → Terra
        if task.complexity < 0.7:
            return ModelTier.TERRA
        
        # 默认：保守选择 Terra
        return ModelTier.TERRA
    
    async def route_and_execute(self, tasks: list[TaskProfile]):
        """批量路由并模拟执行"""
        for i, task in enumerate(tasks):
            model = self.select_model(task)
            cost_estimate = self._estimate_cost(task, model)
            
            if self.daily_spent + cost_estimate > self.cost_budget:
                model = ModelTier.LUNA  # 降级
                cost_estimate = self._estimate_cost(task, model)
            
            self.daily_spent += cost_estimate
            print(f"Task {i+1}: 复杂度={task.complexity:.2f} → {model.value}")
            print(f"  预估成本: ${cost_estimate:.4f}, 累计: ${self.daily_spent:.2f}")
            await asyncio.sleep(0.1)
    
    def _estimate_cost(self, task: TaskProfile, model: ModelTier) -> float:
        prices = {
            ModelTier.LUNA: (0.5, 2.0),
            ModelTier.TERRA: (2.0, 10.0),
            ModelTier.SOL: (5.0, 30.0),
        }
        inp, out = prices[model]
        return (inp * task.context_tokens + out * task.output_tokens) / 1_000_000


# 模拟企业级工作负载
async def main():
    tasks = [
        TaskProfile(complexity=0.2, latency_sla_ms=200, 
                     context_tokens=2000, output_tokens=500),
        TaskProfile(complexity=0.6, latency_sla_ms=2000,
                     context_tokens=8000, output_tokens=2000),
        TaskProfile(complexity=0.95, latency_sla_ms=10000,
                     context_tokens=80000, output_tokens=5000),
        TaskProfile(complexity=0.3, latency_sla_ms=1000,
                     context_tokens=3000, output_tokens=800),
        TaskProfile(complexity=0.85, latency_sla_ms=5000,
                     context_tokens=50000, output_tokens=3000),
        TaskProfile(complexity=0.15, latency_sla_ms=300,
                     context_tokens=1500, output_tokens=400),
        TaskProfile(complexity=0.7, latency_sla_ms=3000,
                     context_tokens=12000, output_tokens=2500),
        TaskProfile(complexity=0.4, latency_sla_ms=1500,
                     context_tokens=4000, output_tokens=1000),
    ]
    
    router = SmartRouter(cost_budget=2.0)
    await router.route_and_execute(tasks)

if __name__ == "__main__":
    asyncio.run(main())