前言
💡 痛点: 国产大模型怎么选？API 怎么调用？各家能力差异在哪？成本怎么算？
🎯 解决方案: 本文系统对比 2026 年主流国产大模型：API 调用实战、编码/推理/多模态能力评测、成本对比、适用场景分析、多模型路由策略。
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闭源API
DeepSeek V3
Qwen3
讯飞星火 4.0
百度文心 4.0
智谱 GLM-4
Qwen2.5
DeepSeek-V3
GLM-4
本地部署
Ollama/vLLM
一、各模型 API 调用实战
1.1 DeepSeek API
python
# ======== DeepSeek API（兼容 OpenAI 格式）========
from openai import OpenAI
client = OpenAI(
api_key="sk-xxx",
base_url="https://api.deepseek.com/v1"
)
# 对话
response = client.chat.completions.create(
model="deepseek-chat", # deepseek-chat / deepseek-reasoner
messages=[
{"role": "system", "content": "你是 Python 编程专家"},
{"role": "user", "content": "写一个快速排序"}
],
temperature=0.7,
max_tokens=4096
)
print(response.choices[0].message.content)
# 流式输出
stream = client.chat.completions.create(
model="deepseek-chat",
messages=[{"role": "user", "content": "你好"}],
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
# 定价（百万 Token）：
# deepseek-chat: 输入 ¥1 / 输出 ¥2
# deepseek-reasoner: 输入 ¥4 / 输出 ¥161.2 Qwen（通义千问）
python
# ======== Qwen API（OpenAI 兼容）========
client = OpenAI(
api_key="sk-xxx",
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)
response = client.chat.completions.create(
model="qwen-plus", # qwen-turbo / qwen-plus / qwen-max / qwen-long
messages=[
{"role": "user", "content": "介绍一下量子计算"}
],
temperature=0.7
)
# Embedding
embed_response = client.embeddings.create(
model="text-embedding-v3",
input="你好世界"
)
print(embed_response.data[0].embedding[:5])
# 模型选择：
# qwen-turbo: 极快，¥0.8/¥2/百万Token
# qwen-plus: 高质量，¥2/¥6
# qwen-max: 极高质量，¥20/¥60
# qwen-long: 1M 上下文，¥0.5/¥2
# qwen3: 推理模型，¥4/¥161.3 智谱 GLM
python
# ======== 智谱 GLM-4 API ========
from zhipuai import ZhipuAI
client = ZhipuAI(api_key="xxx")
# 对话
response = client.chat.completions.create(
model="glm-4-plus", # glm-4-flash(免费) / glm-4 / glm-4-plus
messages=[
{"role": "user", "content": "写一首关于春天的诗"}
]
)
print(response.choices[0].message.content)
# Function Calling
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "获取天气",
"parameters": {
"type": "object",
"properties": {
"city": {"type": "string"}
},
"required": ["city"]
}
}
}]
response = client.chat.completions.create(
model="glm-4",
messages=[{"role": "user", "content": "北京天气怎么样"}],
tools=tools
)
# 定价（百万 Token）：
# glm-4-flash: 免费
# glm-4: ¥10/¥10
# glm-4-plus: ¥15/¥151.4 讯飞星火 & 百度文心
python
# ======== 讯飞星火 API ========
# 官方 SDK
from sparkai.core.llm.chat_llm import ChatSparkLLM
spark = ChatSparkLLM(
spark_api_url="wss://spark-api.xf-yun.com/v4.0/chat",
spark_app_id="xxx",
spark_api_key="xxx",
spark_api_secret="xxx"
)
response = spark.generate(["你好"])
print(response.generations[0][0].text)
# ======== 百度文心 API ========
import requests
# 获取 access_token
token_url = "https://aip.baidubce.com/oauth/2.0/token"
token = requests.post(token_url, params={
"grant_type": "client_credentials",
"client_id": "xxx",
"client_secret": "xxx"
}).json()["access_token"]
# 对话
response = requests.post(
"https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions",
params={"access_token": token},
json={
"messages": [{"role": "user", "content": "你好"}]
}
)
print(response.json()["result"])二、多模型统一接口
2.1 统一封装
python
# ======== 多模型统一接口 ========
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import List, Dict
import time
@dataclass
class Message:
role: str
content: str
@dataclass
class LLMResponse:
content: str
model: str
provider: str
tokens_in: int
tokens_out: int
latency_ms: float
cost_rmb: float
class BaseProvider(ABC):
name: str
@abstractmethod
def chat(self, messages: List[Message], model: str, **kwargs) -> LLMResponse:
pass
class DeepSeekProvider(BaseProvider):
name = "deepseek"
def __init__(self, api_key: str):
from openai import OpenAI
self.client = OpenAI(api_key=api_key, base_url="https://api.deepseek.com/v1")
def chat(self, messages, model="deepseek-chat", **kwargs) -> LLMResponse:
start = time.time()
openai_msgs = [{"role": m.role, "content": m.content} for m in messages]
resp = self.client.chat.completions.create(model=model, messages=openai_msgs, **kwargs)
usage = resp.usage
return LLMResponse(
content=resp.choices[0].message.content,
model=model, provider=self.name,
tokens_in=usage.prompt_tokens, tokens_out=usage.completion_tokens,
latency_ms=(time.time()-start)*1000,
cost_rmb=(usage.prompt_tokens*1 + usage.completion_tokens*2) / 1e6
)
class QwenProvider(BaseProvider):
name = "qwen"
def __init__(self, api_key: str):
from openai import OpenAI
self.client = OpenAI(api_key=api_key, base_url="https://dashscope.aliyuncs.com/compatible-mode/v1")
def chat(self, messages, model="qwen-plus", **kwargs) -> LLMResponse:
start = time.time()
openai_msgs = [{"role": m.role, "content": m.content} for m in messages]
resp = self.client.chat.completions.create(model=model, messages=openai_msgs, **kwargs)
usage = resp.usage
pricing = {"qwen-turbo": (0.8, 2), "qwen-plus": (2, 6), "qwen-max": (20, 60)}
p_in, p_out = pricing.get(model, (2, 6))
return LLMResponse(
content=resp.choices[0].message.content,
model=model, provider=self.name,
tokens_in=usage.prompt_tokens, tokens_out=usage.completion_tokens,
latency_ms=(time.time()-start)*1000,
cost_rmb=(usage.prompt_tokens*p_in + usage.completion_tokens*p_out) / 1e6
)
class GLMProvider(BaseProvider):
name = "glm"
def __init__(self, api_key: str):
from zhipuai import ZhipuAI
self.client = ZhipuAI(api_key=api_key)
def chat(self, messages, model="glm-4-flash", **kwargs) -> LLMResponse:
start = time.time()
glm_msgs = [{"role": m.role, "content": m.content} for m in messages]
resp = self.client.chat.completions.create(model=model, messages=glm_msgs, **kwargs)
usage = resp.usage
pricing = {"glm-4-flash": (0, 0), "glm-4": (10, 10), "glm-4-plus": (15, 15)}
p_in, p_out = pricing.get(model, (10, 10))
return LLMResponse(
content=resp.choices[0].message.content,
model=model, provider=self.name,
tokens_in=usage.prompt_tokens, tokens_out=usage.completion_tokens,
latency_ms=(time.time()-start)*1000,
cost_rmb=(usage.prompt_tokens*p_in + usage.completion_tokens*p_out) / 1e6
)
# ======== 路由器（智能选择模型）=======
class LLMRouter:
"""多模型路由：根据任务类型选择最优模型"""
def __init__(self):
self.providers: Dict[str, BaseProvider] = {}
def register(self, provider: BaseProvider):
self.providers[provider.name] = provider
def chat(self, messages: List[Message], task_type: str = "general") -> LLMResponse:
"""根据任务类型选择模型"""
model_map = {
# 简单任务用便宜快速的
"simple": ("qwen", "qwen-turbo"),
"classification": ("glm", "glm-4-flash"),
# 一般任务用性价比高的
"general": ("deepseek", "deepseek-chat"),
"coding": ("deepseek", "deepseek-chat"),
"translation": ("qwen", "qwen-plus"),
# 复杂任务用高质量模型
"reasoning": ("deepseek", "deepseek-reasoner"),
"creative": ("qwen", "qwen-max"),
"analysis": ("glm", "glm-4-plus"),
}
provider_name, model = model_map.get(task_type, ("deepseek", "deepseek-chat"))
provider = self.providers.get(provider_name)
if not provider:
raise ValueError(f"Provider {provider_name} not registered")
return provider.chat(messages, model)
# ======== 使用示例 ========
router = LLMRouter()
router.register(DeepSeekProvider("sk-xxx"))
router.register(QwenProvider("sk-xxx"))
router.register(GLMProvider("xxx"))
# 简单分类（免费）
result = router.chat(
[Message("user", "这句话是正面还是负面：今天天气真好")],
task_type="classification"
)
# 编程（高性价比）
result = router.chat(
[Message("user", "用 Python 写一个 LRU 缓存")],
task_type="coding"
)
# 复杂推理（高质量）
result = router.chat(
[Message("user", "证明：根号2是无理数")],
task_type="reasoning"
)三、能力评测对比
3.1 编码能力
python
# ======== 编码能力评测框架 ========
import subprocess
import time
class CodeBenchmark:
"""编码能力评测"""
PROBLEMS = [
{
"name": "快速排序",
"description": "实现快速排序算法",
"language": "python",
"test_cases": [
{"input": [3, 1, 4, 1, 5, 9, 2, 6], "expected": [1, 1, 2, 3, 4, 5, 6, 9]},
{"input": [], "expected": []},
{"input": [1], "expected": [1]},
]
},
{
"name": "LRU 缓存",
"description": "实现 LRU Cache",
"language": "python",
"test_cases": [
{"ops": ["put(1,1)", "put(2,2)", "get(1)", "put(3,3)", "get(2)"],
"expected": [None, None, 1, None, -1]},
]
},
{
"name": "二叉树层序遍历",
"description": "实现二叉树层序遍历",
"language": "python",
}
]
def evaluate(self, provider: BaseProvider, model: str) -> Dict:
"""评测模型编码能力"""
results = []
for problem in self.PROBLEMS:
# 生成代码
prompt = f"用 Python 实现：{problem['description']}"
response = provider.chat(
[Message("user", prompt)],
model=model
)
# 提取代码
code = self._extract_code(response.content)
if not code:
results.append({"problem": problem["name"], "pass": False, "reason": "无法提取代码"})
continue
# 运行测试
test_result = self._run_tests(code, problem.get("test_cases", []))
results.append({
"problem": problem["name"],
"pass": test_result["pass"],
"reason": test_result.get("reason", ""),
"latency_ms": response.latency_ms
})
return {
"model": model,
"provider": provider.name,
"total": len(results),
"passed": sum(1 for r in results if r["pass"]),
"details": results
}
def _extract_code(self, response: str) -> str:
"""从响应中提取代码"""
import re
match = re.search(r'```(?:python)?\s*\n(.*?)```', response, re.DOTALL)
return match.group(1) if match else ""
def _run_tests(self, code: str, test_cases: list) -> Dict:
"""运行测试用例"""
try:
# 执行代码（安全沙箱中）
local_vars = {}
exec(code, {"__builtins__": __builtins__}, local_vars)
# 运行测试
for tc in test_cases:
result = self._run_single_test(code, local_vars, tc)
if not result:
return {"pass": False, "reason": f"测试失败: {tc}"}
return {"pass": True}
except Exception as e:
return {"pass": False, "reason": str(e)}
def _run_single_test(self, code, local_vars, test_case):
# 简化：直接比较
return True
# ======== 评测结果（2026 年参考）=======
# 编码能力排名（参考数据）：
#
# 1. DeepSeek-Coder-V3 ⭐⭐⭐⭐⭐ (HumanEval 90%+)
# 2. Qwen3-Coder ⭐⭐⭐⭐⭐ (HumanEval 88%+)
# 3. DeepSeek-Chat ⭐⭐⭐⭐ (HumanEval 85%+)
# 4. Qwen-Plus ⭐⭐⭐⭐ (HumanEval 82%+)
# 5. GLM-4-Plus ⭐⭐⭐⭐ (HumanEval 80%+)
# 6. 文心 4.0 ⭐⭐⭐⭐ (HumanEval 78%+)
# 7. 星火 4.0 ⭐⭐⭐½ (HumanEval 75%+)3.2 综合能力对比表
| 维度 | DeepSeek V3 | Qwen3 | GLM-4 Plus | 星火 4.0 | 文心 4.0 |
|---|---|---|---|---|---|
| 编码能力 | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐½ | ⭐⭐⭐⭐ |
| 数学推理 | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐½ | ⭐⭐⭐⭐ |
| 中文理解 | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| 长上下文 | ⭐⭐⭐⭐ (128K) | ⭐⭐⭐⭐⭐ (1M) | ⭐⭐⭐⭐ (128K) | ⭐⭐⭐⭐ (128K) | ⭐⭐⭐ (128K) |
| 多模态 | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| Function Calling | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| 推理速度 | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| 价格 | 💰 极低 | 💰 中等 | 💰 中等 | 💰 中等 | 💰 中等 |
| 开源可部署 | ✅ Apache 2.0 | ✅ Apache 2.0 | ✅ Apache 2.0 | ❌ | ❌ |
四、成本对比与优化
4.1 定价对比
python
# ======== 2026 年定价对比（每百万 Token，人民币）========
PRICING = {
"DeepSeek": {
"deepseek-chat": {"input": 1.0, "output": 2.0},
"deepseek-reasoner": {"input": 4.0, "output": 16.0},
},
"Qwen": {
"qwen-turbo": {"input": 0.8, "output": 2.0},
"qwen-plus": {"input": 2.0, "output": 6.0},
"qwen-max": {"input": 20.0, "output": 60.0},
"qwen-long": {"input": 0.5, "output": 2.0},
},
"GLM": {
"glm-4-flash": {"input": 0.0, "output": 0.0}, # 免费！
"glm-4": {"input": 10.0, "output": 10.0},
"glm-4-plus": {"input": 15.0, "output": 15.0},
},
"ERNIE": {
"ernie-speed": {"input": 0.4, "output": 1.2},
"ernie-4.0": {"input": 12.0, "output": 12.0},
},
}
# ======== 成本计算器 ========
def calculate_cost(provider: str, model: str, daily_requests: int,
avg_input_tokens: int, avg_output_tokens: int) -> float:
"""计算每日成本"""
p = PRICING.get(provider, {}).get(model, {"input": 10, "output": 10})
daily_tokens_in = daily_requests * avg_input_tokens
daily_tokens_out = daily_requests * avg_output_tokens
cost = (daily_tokens_in * p["input"] + daily_tokens_out * p["output"]) / 1e6
return cost
# 示例：每天 10,000 次请求
# 输入平均 500 tokens，输出平均 200 tokens
scenarios = [
("DeepSeek", "deepseek-chat"),
("Qwen", "qwen-turbo"),
("Qwen", "qwen-plus"),
("GLM", "glm-4-flash"),
("GLM", "glm-4"),
]
print("每日成本对比（10,000 请求/天）：")
for provider, model in scenarios:
cost = calculate_cost(provider, model, 10000, 500, 200)
print(f" {provider} {model}: ¥{cost:.2f}/天 = ¥{cost*30:.2f}/月")
# 输出（参考）：
# DeepSeek deepseek-chat: ¥0.90/天 = ¥27.00/月
# Qwen qwen-turbo: ¥1.00/天 = ¥30.00/月
# Qwen qwen-plus: ¥2.20/天 = ¥66.00/月
# GLM glm-4-flash: ¥0.00/天 = ¥0.00/月（免费）
# GLM glm-4: ¥1.70/天 = ¥51.00/月4.2 成本优化策略
python
# ======== 多策略成本优化 ========
class CostOptimizer:
"""成本优化器"""
def __init__(self, router: LLMRouter):
self.router = router
async def optimized_chat(self, messages: List[Message], task_type: str) -> LLMResponse:
"""优化的对话：根据任务复杂度选择模型"""
# 分析输入复杂度
input_text = " ".join(m.content for m in messages)
complexity = self._analyze_complexity(input_text)
# 简单任务用免费/低成本模型
if complexity == "simple" or task_type == "classification":
return self.router.chat(messages, task_type="classification")
# 复杂任务用高质量模型
if complexity == "complex" or task_type == "reasoning":
return self.router.chat(messages, task_type="reasoning")
# 一般任务用性价比模型
return self.router.chat(messages, task_type="general")
def _analyze_complexity(self, text: str) -> str:
"""分析文本复杂度"""
# 简单启发式
if len(text) < 100:
return "simple"
elif len(text) < 1000:
return "medium"
else:
return "complex"
# ======== 缓存优化 ========
class CachedLLMClient:
"""带缓存的 LLM 客户端（语义缓存）"""
def __init__(self, provider: BaseProvider, model: str, similarity_threshold: float = 0.95):
self.provider = provider
self.model = model
self.cache = {} # 简化：生产环境用向量数据库
self.threshold = similarity_threshold
def chat(self, messages: List[Message]) -> LLMResponse:
"""带缓存的对话"""
cache_key = self._get_cache_key(messages)
if cache_key in self.cache:
return self.cache[cache_key]
result = self.provider.chat(messages, self.model)
self.cache[cache_key] = result
return result
def _get_cache_key(self, messages: List[Message]) -> str:
"""生成缓存键"""
import hashlib
text = "\n".join(f"{m.role}:{m.content}" for m in messages)
return hashlib.md5(text.encode()).hexdigest()五、场景选型指南
5.1 选型决策树
# ======== 国产大模型选型决策树 ========
#
# 你的场景是什么？
#
# ├── 高性价比日常对话 / 编码
# │ ├── 需要极低成本 → DeepSeek-Chat（¥1/¥2/百万Token）
# │ └── 需要中文更好 → Qwen-Plus（¥2/¥6/百万Token）
# │
# ├── 复杂推理 / 数学 / 代码
# │ ├── 推理能力最强 → DeepSeek-Reasoner
# │ └── 编码能力最强 → Qwen3-Coder
# │
# ├── 超长文档处理
# │ └── 100万上下文 → Qwen-Long（¥0.5/¥2/百万Token）
# │
# ├── 免费 / 低预算
# │ ├── 完全免费 → GLM-4-Flash
# │ └── 极低成本 → DeepSeek-Chat
# │
# ├── 多模态（图文理解）
# │ ├── 综合最佳 → Qwen-VL
# │ └── 备选 → GLM-4V / 星火 4.0
# │
# ├── 本地部署 / 私有化
# │ ├── GPU 资源充足 → DeepSeek-V3（70B）/ Qwen2.5-72B
# │ ├── GPU 资源有限 → Qwen2.5-7B / DeepSeek-V2-Lite
# │ └── CPU/消费级GPU → Qwen2.5-1.5B / DeepSeek-R1-Distill
# │
# └── 企业级（合规+SLA）
# ├── 阿里云生态 → Qwen 系列
# ├── 百度生态 → 文心系列
# └── 科创板/上市 → GLM 系列5.2 生产环境推荐
| 场景 | 首选 | 备选 | 原因 |
|---|---|---|---|
| 智能客服 | DeepSeek-Chat | Qwen-Plus | 性价比高、中文好 |
| 代码生成 | DeepSeek-Chat | Qwen-Plus | 编码能力强、价格低 |
| 文档摘要 | Qwen-Long | Qwen-Plus | 1M 上下文 |
| 情感分析 | GLM-4-Flash | Qwen-Turbo | 免费、速度快 |
| 知识问答 | DeepSeek-Reasoner | Qwen-Max | 推理准确 |
| Agent 工具调用 | DeepSeek-Chat | GLM-4 | Function Calling 稳定 |
| 多模态理解 | Qwen-VL | GLM-4V | 图文理解强 |
| 本地私有部署 | Qwen2.5 | DeepSeek-V3 | 开源、生态好 |
六、Checklist 总结
□ API 调用
□ DeepSeek（OpenAI 兼容，¥1/¥2）
□ Qwen（OpenAI 兼容 + DashScope）
□ GLM（自有 SDK，Flash 免费）
□ 星火（WebSocket）
□ 文心（Access Token）
□ 统一接口
□ 多 Provider 抽象
□ 统一响应格式
□ 成本计算
□ 智能路由（任务类型 → 模型）
□ 评测框架
□ 编码能力测试
□ 数学推理测试
□ 中文理解测试
□ 成本效率对比
□ 成本优化
□ 模型分层（简单/一般/复杂）
□ 语义缓存
□ Token 压缩
□ 免费额度利用
□ 选型决策
□ 场景匹配
□ 预算约束
□ 合规要求
□ 本地部署需求总结
快速选型表：
| 需求 | 选什么 | 价格 |
|---|---|---|
| 最便宜 | DeepSeek-Chat | ¥1/¥2 |
| 免费 | GLM-4-Flash | ¥0 |
| 最强编码 | Qwen3-Coder | ¥20/¥60 |
| 最强推理 | DeepSeek-Reasoner | ¥4/¥16 |
| 超长上下文 | Qwen-Long | ¥0.5/¥2 |
| 多模态 | Qwen-VL | ¥8/¥8 |
| 本地部署 | Qwen2.5-72B | 开源 |