DeepSeek V4 Pro (0813) vs GLM-5
何时用哪一个
两者都是文本进、文本出的模型，支持聊天、代码、推理和工具，所以分野是上下文、输出长度和价格。deepseek-v4-pro-0813 有 1,000,000 token 上下文对 200000，允许 393216 输出 token 对 131072（约 3 倍），缓存读取更便宜，$0.132 对 $0.2。glm-5 每 token 更便宜：输入 $1 对 $1.32，低约 1.3 倍；输出 $3.2 对 $3.96，低约 1.2 倍，而且可关闭思考。长提示或超长单次回复选 deepseek-v4-pro-0813；能装进 200000 token 的大批量工作选 glm-5。
Benchmark 成绩
厂商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定价
| DeepSeek V4 Pro (0813) | GLM-5 | Δ | |
|---|---|---|---|
| 输入 / 1M tokens | $1.32 | $1 | 1.3× |
| 输出 / 1M tokens | $3.96 | $3.2 | 1.2× |
| 缓存读取 / 1M tokens | $0.132 | $0.2 | 0.66× |
| 缓存写入 | 不单独收费 | - | - |
费率取自构建时的实时目录；每个模型页面均附有当前的费率卡。
它们的位置 — 以该计费单位计费的所有 64 个 聊天 模型的 每 1M token 的输入价格（对数刻度）
能力
| DeepSeek V4 Pro (0813) | GLM-5 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 始终开启 | 可配置 |
| 结构化输出 | 是 | 是 |
| 提示词缓存 | 隐式（自动） | 隐式（自动） |
| 缓存生存时间 | no fixed TTL (evicted when unused) | 未公开 |
| 最小缓存前缀 | 未公开 | 未公开 |
规格
| DeepSeek V4 Pro (0813) | GLM-5 | |
|---|---|---|
| 输入模态 | 文本 | 文本 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-08-13 | 2026-02-12 |
| 上下文窗口 | 1M | 200K |
| 最大输出 | 393K | 131K |
| 思考参数 | reasoning_effort | thinking.type |
| 允许的值 | reasoning_effort
| thinking.type
|
| 默认值 | - | enabled, and on GLM-5 the model automatically determines whether to think |
规格转录自各供应商的文档；若供应商未发布某项数据，则直接省略该行，而非进行推断。 完整来源： DeepSeek V4 Pro (0813) · GLM-5
单个 Prompt，两个模型 —— 通过网关实测
DeepSeek V4 Pro (0813) 通过 · 3 sentences
Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.
输出 308 tok (+226 思考) 延迟 5.1 s
GLM-5 通过 · 3 sentences
Enabling reasoning forces the model to perform multiple hidden steps to "think" through a problem, drastically increasing the volume of data processed per query compared to a standard response. Because our billing is directly tied to this processing volume, the additional computational work required for higher accuracy has proportionally doubled our consumption. Essentially, the spike reflects a trade-off where we are paying a higher variable cost to secure significantly better analytical outcomes.
输出 1393 tok (+1308 思考) 延迟 36.0 s
指令遵循（恰好三句，可数）、受众适配（面向 CFO 的语气），以及下方 token 计量所暴露的隐藏思考计费缺口。
DeepSeek V4 Pro (0813) 通过 · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.
输出 1315 tok (+1130 思考) 延迟 16.9 s
GLM-5 通过 · 8/8 cases
Here is the fixed code: ```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 return best ``` **Explanation:** The bug stems from initializing `best` and `cur` to zero, which causes the function to count the number of *transitions* between adjacent elements rather than the total number of items in the run, resulting in a return value that is always one less than the actual length (reporting 0 for a single element and 1 for a pair). By initializing the counters to 1 and resetting `cur` to 1 when a streak breaks, the logic correctly counts the items themselves; adding an explicit check for an empty list ensures the function handles the zero-element edge case correctly.
输出 2907 tok (+2694 思考) 延迟 73.7 s
修复是否真的正确（可运行）、解释的信息密度，以及在一个边界明确的任务上的 token 效率。
DeepSeek V4 Pro (0813) 通过 · 5/5 fields, guidance null
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}
输出 2155 tok (+2121 思考) 延迟 26.0 s
GLM-5 通过 · 5/5 fields, guidance "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld pending the Q4 close" } ```
输出 3620 tok (+3561 思考) 延迟 91.8 s
schema 服从度（不臆造字段）、幻觉压力（guidance 明确被暂缓给出），以及结构化输出路径的差异。
DeepSeek V4 Pro (0813) 通过 · 121 words, 0 banned, 1 question
Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.
输出 2845 tok (+2694 思考) 延迟 25.5 s
GLM-5 通过 · 119 words, 0 banned, 1 question
We are thrilled to introduce Global Prompt Caching, a powerful new capability within our API gateway designed to optimize your AI operations. By intelligently storing prompt responses across every supported provider, this feature drastically reduces latency and cuts operational costs. Instead of processing identical requests repeatedly, our system serves cached results instantly, ensuring consistent performance even during high-traffic periods. Why pay full price for repeated inference on the same inputs? This update gives developers fine-grained control over cache lifetimes and hit rates, allowing for predictable budgeting and faster application response times. You can activate this functionality directly in your dashboard settings today. Start maximizing your efficiency now and deliver a snappier experience to your end-users without unnecessary API expenditure.
输出 715 tok (+571 思考) 延迟 18.9 s
约束服从度（字数预算、禁用词表、唯一的那句问句）、文风指纹，以及长度控制。
只需一行代码即可在它们之间切换
两个 ID 都包含在下方的每个选项卡中 — 高亮显示的两行是唯一的修改。相同的端点，相同的密钥，相同的请求结构。
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="deepseek-v4-pro-0813",
# model="glm-5", # 取消注释此行，注释上一行
messages=[{"role": "user", "content": "Summarize this diff"}],
reasoning_effort="medium",
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "deepseek-v4-pro-0813",
// model: "glm-5", // 取消注释此行，注释上一行
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);curl https://synthorai.io/v1/chat/completions \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-pro-0813",
# "model": "glm-5", # 取消注释此行，注释上一行
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
Model: "deepseek-v4-pro-0813",
// Model: "glm-5", // 取消注释此行，注释上一行
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("deepseek-v4-pro-0813")
// .model("glm-5") // 取消注释此行，注释上一行
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常见问题
DeepSeek V4 Pro (0813) 和 GLM-5 哪个更便宜？
在 输入 / 1m tokens 方面，GLM-5 更便宜（$1 对比 $1.32，相差 1.3×）。其他行可能得出相反的结论——上方表格提供了完整信息，实际成本取决于你的组合使用情况。
我可以在不进行两次集成的情况下，对 DeepSeek V4 Pro (0813) 和 GLM-5 进行 A/B 测试吗？
可以。两者均通过同一个兼容 OpenAI 的端点提供服务，并使用同一把 API 密钥——切换只需更改一行模型字符串，因此你可以将一部分流量路由到各个模型并直接比较账单。
DeepSeek V4 Pro (0813) 和 GLM-5 支持提示词缓存吗？
是的 —— 两者对缓存读取的计费均低于其输入费率，因此热前缀工作负载的成本低于标价。准确的缓存读取行位于上方的定价表中。