OpenCode CLI 配置与多模型对比

OpenCode 是一个开源的 AI 编程助手 CLI，支持同时接入 Claude、Gemini、GPT-4——让你在一个工具里切换和对比多个模型。

OpenCode 在工具链中的位置

安装与多模型配置

# 安装 OpenCode（Go 二进制，无依赖）
# macOS
brew install opencode-ai/tap/opencode
# Linux
curl -fsSL https://opencode.ai/install.sh | sh
# 或从 npm 安装
npm install -g opencode-ai
# 验证
opencode --version

"""
OpenCode CLI 配置与多模型工作流演示
展示如何在项目中设置和切换 AI 模型
"""
import json
import os
from dataclasses import dataclass, field
from enum import Enum
class ModelProvider(Enum):
ANTHROPIC = "anthropic"
GOOGLE = "google"
OPENAI = "openai"
OLLAMA = "ollama"
GROQ = "groq"
@dataclass
class ModelConfig:
"""模型配置"""
provider: ModelProvider
model_id: str
display_name: str
context_window: int          # token 数
cost_per_1m_input: float     # 美元
cost_per_1m_output: float
strengths: list[str]
weaknesses: list[str]
env_var: str                 # API Key 环境变量
@dataclass
class OpenCodeConfig:
"""OpenCode 项目配置（opencode.json）"""
model: str = "claude-sonnet-4-5-20251001"
providers: dict = field(default_factory=dict)
instructions: str = ""      # 相当于 CLAUDE.md / GEMINI.md
autoshare: bool = False
keybindings: dict = field(default_factory=dict)
def to_json(self) -> str:
return json.dumps({
"model": self.model,
"provider": self.providers,
"instructions": self.instructions,
"autoshare": self.autoshare,
}, indent=2, ensure_ascii=False)
class ModelBenchmark:
"""多模型对比基准"""
MODELS = [
ModelConfig(
provider=ModelProvider.ANTHROPIC,
model_id="claude-sonnet-4-6",
display_name="Claude Sonnet 4.6",
context_window=200_000,
cost_per_1m_input=3.0,
cost_per_1m_output=15.0,
strengths=["代码生成质量最高", "指令遵循准确", "长文档理解", "安全拒绝边缘请求"],
weaknesses=["无实时网络搜索", "知识截止日期"],
env_var="ANTHROPIC_API_KEY",
),
ModelConfig(
provider=ModelProvider.GOOGLE,
model_id="gemini-2.0-flash",
display_name="Gemini 2.0 Flash",
context_window=1_000_000,
cost_per_1m_input=0.075,
cost_per_1m_output=0.30,
strengths=["超大上下文（100万token）", "速度最快", "Google Search 集成", "多模态"],
weaknesses=["代码复杂逻辑偶有偏差", "指令遵循不如 Claude"],
env_var="GEMINI_API_KEY",
),
ModelConfig(
provider=ModelProvider.OPENAI,
model_id="gpt-4o",
display_name="GPT-4o",
context_window=128_000,
cost_per_1m_input=2.50,
cost_per_1m_output=10.0,
strengths=["生态最成熟", "插件/工具支持广", "代码 Interpreter"],
weaknesses=["代码生成不如 Claude", "价格较高"],
env_var="OPENAI_API_KEY",
),
ModelConfig(
provider=ModelProvider.OLLAMA,
model_id="codellama:13b",
display_name="CodeLlama 13B (本地)",
context_window=16_000,
cost_per_1m_input=0.0,
cost_per_1m_output=0.0,
strengths=["完全离线", "数据不出本机", "无 API 费用"],
weaknesses=["能力远低于云端模型", "需要本地 GPU", "上下文窗口小"],
env_var="",
),
]
TASK_RECOMMENDATIONS = {
"日常代码生成": "Claude Sonnet 4.6",
"超大代码库分析（>50万字符）": "Gemini 2.0 Flash",
"快速原型/草稿": "Gemini 2.0 Flash（免费快速）",
"需要实时信息": "Gemini 2.0 Flash（Google Search）",
"高保密性代码": "CodeLlama 本地运行",
"OpenAI API 兼容生态": "GPT-4o",
"成本最优": "Gemini Flash（最低价）",
"质量最优": "Claude Opus 4.6",
}
@classmethod
def compare_models(cls) -> None:
print("=== 模型对比矩阵 ===\n")
print(f"{'模型':<25} {'上下文':<12} {'输入价/1M':<12} {'主要优势'}")
print("-" * 80)
for m in cls.MODELS:
ctx = f"{m.context_window // 1000}K" if m.context_window < 1_000_000 else "1M"
price = f"${m.cost_per_1m_input}" if m.cost_per_1m_input > 0 else "免费"
print(f"{m.display_name:<25} {ctx:<12} {price:<12} {m.strengths[0]}")
@classmethod
def recommend(cls, task: str) -> str:
for task_key, model in cls.TASK_RECOMMENDATIONS.items():
if any(kw in task for kw in task_key.split("/")):
return f"推荐: {model}（{task_key}）"
return "推荐: Claude Sonnet 4.6（通用最优）"
class OpenCodeWorkflows:
"""OpenCode CLI 典型工作流"""
CONFIG_EXAMPLE = OpenCodeConfig(
model="claude-sonnet-4-5-20251001",
providers={
"anthropic": {"apiKey": "${ANTHROPIC_API_KEY}"},
"google": {"apiKey": "${GEMINI_API_KEY}"},
"openai": {"apiKey": "${OPENAI_API_KEY}"},
},
instructions="""你是这个 FastAPI 项目的开发助手。
- 使用 Python 3.12+ 特性
- 遵循 PEP 8 代码规范
- 所有函数需要类型注解
- 数据库操作使用异步 SQLAlchemy""",
)
SHELL_COMMANDS = {
"进入交互模式": "opencode",
"快速提问": 'opencode -m "如何优化这个 SQL 查询？" < query.sql',
"切换模型": "/model gemini-2.0-flash",          # 交互模式内命令
"查看当前模型": "/model",
"分析文件": 'opencode -m "审查这段代码的安全问题" < api.py',
"对比两个模型": "opencode --compare claude,gemini -m '实现一个 LRU 缓存'",
"查看费用统计": "opencode stats",
"清除会话": "/clear",
}
@classmethod
def print_config(cls):
print("=== opencode.json 配置示例 ===\n")
print(cls.CONFIG_EXAMPLE.to_json())
@classmethod
def print_commands(cls):
print("\n=== 常用命令速查 ===\n")
for desc, cmd in cls.SHELL_COMMANDS.items():
print(f"  {desc}:")
print(f"    $ {cmd}\n")
# 演示
print("=== OpenCode CLI — 多模型开发工作流 ===\n")
benchmark = ModelBenchmark()
benchmark.compare_models()
print("\n=== 任务 → 模型推荐 ===\n")
tasks = ["超大代码库分析", "日常代码生成", "需要实时信息", "高保密性代码"]
for task in tasks:
print(f"  {task}: {benchmark.recommend(task)}")
workflows = OpenCodeWorkflows()
workflows.print_config()
workflows.print_commands()

模型切换决策树

行动清单

• [ ] 安装 OpenCode：brew install opencode-ai/tap/opencode 或 npm install -g opencode-ai
• [ ] 在项目根目录创建 opencode.json，配置 Anthropic + Google 双 API Key
• [ ] 运行 opencode 进入 TUI，用 /model 命令切换模型体验差异
• [ ] 对比测试：同一个重构任务分别问 Claude 和 Gemini，对比回答质量
• [ ] 设置 instructions 字段（相当于项目 System Prompt），记录代码规范
• [ ] 用 opencode stats 查看本月各模型用量和费用，优化模型选择策略

下一节：03-双引擎策略-分工协作 — Claude + Gemini 互补使用的系统化策略。