Claude Sonnet 5 vs GLM-5.1
何時該用哪一個 — 綜合評斷，而非基準測試數據表
當任務需要在文字之外接受影像輸入、1000000 token 脈絡，或它明確的思考模式時選 claude-sonnet-5；它的快取讀取也更便宜，每百萬 $0.2 對 $0.26。適合放進 200000 token 的純文字工作選 glm-5.1，它 $1.4 的輸入和 $4.4 的輸出低於 Sonnet 的 $2 和 $10——輸出 token 上便宜約 2.3 倍——最大輸出略大，為 131072 token。兩者按相同的每百萬 token 單位計費，都做程式碼、工具和推理，也都可以關閉思考，所以差別其實是脈絡與模態對輸出成本的取捨。
定價
| Claude Sonnet 5 | GLM-5.1 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $2 | $1.4 | 1.4× |
| 輸出 / 1M tokens | $10 | $4.4 | 2.3× |
| 快取讀取 / 1M tokens | $0.2 | $0.26 | 0.77× |
| 快取寫入 | 1.25x (5m) / 2x (1h) | — | — |
費率取自建置時的即時目錄；各模型頁面皆附有目前的費率卡。
它們的相對位置 — 在此計費單位下，所有 63 個 聊天 模型的 每 1M tokens 的輸入價格（對數尺度）
能力
| Claude Sonnet 5 | GLM-5.1 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可配置 | 可配置 |
| 結構化輸出 | 是 | 是 |
| 提示快取 | 顯式（由您標記前綴） | 隱式（自動） |
| 快取生命週期 | 5m default, 1h option | 未公開 |
| 最小快取前綴 | 1024 個 token | 未公開 |
規格
| Claude Sonnet 5 | GLM-5.1 | |
|---|---|---|
| 輸入模態 | 文字 影像 | 文字 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-06-30 | 2026-04-07 |
| 知識截止日期 | 2026-01 | — |
| 上下文視窗 | 1M | 200K |
| 最大輸出 | 128K | 131K |
| 思考參數 |
| thinking.type |
| 可接受的值 | thinking.type
effort
"enabled" returns 400 | thinking.type
|
| 預設值 | thinking on (adaptive) effort
| enabled, and the model automatically determines whether to think |
規格摘錄自各供應商的文件；供應商未發布的資料列會直接省略，而非自行推測。 完整來源： Claude Sonnet 5 · GLM-5.1
單一提示詞，兩款模型 — 經由閘道測量
Claude Sonnet 5 通過 · 3 sentences
Reasoning mode works by having the model generate extended internal "thinking" steps before producing its final answer, and since we're billed on total tokens, that hidden reasoning text adds significantly to what we pay for—even though you never see it in the output. This is different from simply getting longer visible responses; the model is essentially "showing its work" behind the scenes, and complex prompts can trigger thousands of extra reasoning tokens per request. The upside is improved accuracy on complex tasks, but if we want to control costs, we should selectively enable reasoning only for queries that truly need it, rather than applying it universally.
輸出 194 tok 延遲 4.3 s
GLM-5.1 通過 · 3 sentences
Enabling reasoning requires the LLM to generate thousands of invisible "thinking" tokens to work through complex logic before producing the final answer. Because we are billed on total token volume, these intermediate steps are charged at the premium output rate, drastically increasing the variable cost per query. The invoice doubled simply because the model is now doing roughly twice the computational work per request, trading higher variable compute costs for significantly improved analytical accuracy.
輸出 913 tok (+827 思考) 延遲 10.9 s
指令遵循（恰好三句，可數）、受眾適配（面向 CFO 的語氣），以及下方 token 計量所暴露的隱藏思考計費缺口。
Claude Sonnet 5 通過 · 8/8 cases
## The Bug The function has two related off-by-one issues rooted in the same cause: `cur` and `best` are initialized to `0` instead of `1`, treating run lengths as the number of *matches* between adjacent elements rather than the number of *elements* in the run. This means a single-element input never enters the loop (since `range(1, 1)` is empty) and returns `0` instead of the correct answer of `1`. It also means that for any input, the run length is undercounted by exactly one — a run of 3 equal elements only produces 2 "match" increments, so `best` ends up as 2 instead of 3. The claim that it "misses runs that end at the last index" is actually a symptom of this same undercount: the logic does update `best` correctly on every iteration (including the last), but because it's counting matches instead of elements, the final reported value is always one less than the true longest run. […]
輸出 536 tok 延遲 7.1 s
GLM-5.1 通過 · 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 best = max(best, cur) else: cur = 1 return best ``` The original function tracks the number of adjacent equal pairs rather than the actual run length. By initializing `cur` and `best` to 0 and incrementing `cur` only when a match occurs, a run of length L produces a count of L−1. This causes single‑element inputs to return 0 (since the loop never runs) and generally returns a value one less than the true run length, effectively omitting the final element of any run—including those that end at the last index. […]
輸出 2642 tok (+2414 思考) 延遲 26.1 s
修復是否真的正確（可執行）、解釋的資訊密度，以及在一個邊界明確的任務上的 token 效率。
Claude Sonnet 5 通過 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11.5, "guidance": null } ```
輸出 348 tok 延遲 5.3 s
GLM-5.1 通過 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null } ```
輸出 1465 tok (+1410 思考) 延遲 16.1 s
schema 服從度（不臆造欄位）、幻覺壓力（guidance 明確被暫緩給出），以及結構化輸出路徑的差異。
Claude Sonnet 5 通過 · 123 words, 0 banned, 1 question
## Introducing Cross-Provider Prompt Caching We're rolling out a new caching layer for our API gateway that works across every major LLM provider you connect through it. Instead of resending identical prompts and paying full price each time, the gateway recognizes repeated requests and serves cached responses in milliseconds—regardless of whether you're routing to OpenAI, Anthropic, or Google. What does this mean for your bill and your latency? Both drop, often significantly, especially for teams running high-volume, repetitive workloads like customer support bots or batch content generation. The cache is configurable per route, with adjustable TTLs and invalidation rules, so you stay in control of freshness versus cost. Available now for all Pro and Enterprise plans. Check your dashboard to enable it today.
輸出 259 tok 延遲 4.8 s
GLM-5.1 通過 · 120 words, 0 banned, 1 question
We are thrilled to introduce prompt caching across providers in the API gateway. Why pay twice for the same prompt context? Now, when your application sends identical prompt prefixes to different LLM providers, our gateway automatically caches the input, reducing latency and cutting costs. This feature intelligently recognizes repeated prompt structures across OpenAI, Anthropic, and others, storing them efficiently at the network edge. Developers no longer need to manage separate caching logic for each individual provider. Instead, our unified system handles it directly, ensuring faster response times on all subsequent requests. Stop wasting valuable tokens on redundant processing workloads. Upgrade your integration today and experience immediate performance gains while keeping your infrastructure simple and your overall monthly billing incredibly low.
輸出 7589 tok (+7447 思考) 延遲 188.9 s
約束服從度（字數預算、禁用詞表、唯一的那句問句）、文風指紋，以及長度控制。
只需一行程式碼即可在兩者間切換
以下每個頁籤中都有這兩個 ID — 醒目提示的這兩行是唯一的修改處。相同的端點，相同的金鑰，相同的請求結構。
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="claude-sonnet-5",
# model="glm-5.1", # 取消註解此行，並註解上一行
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: "claude-sonnet-5",
// model: "glm-5.1", // 取消註解此行，並註解上一行
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": "claude-sonnet-5",
# "model": "glm-5.1", # 取消註解此行，並註解上一行
"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: "claude-sonnet-5",
// Model: "glm-5.1", // 取消註解此行，並註解上一行
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("claude-sonnet-5")
// .model("glm-5.1") // 取消註解此行，並註解上一行
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常見問題
Claude Sonnet 5 和 GLM-5.1 哪個比較便宜？
GLM-5.1 在 輸入 / 1m tokens 上較便宜（$1.4 對比 $2，相差 1.4×）。其他項目可能呈現相反結果 — 上表提供完整資訊，實際成本取決於您的使用組合。
我可以在不進行兩次整合的情況下，對 Claude Sonnet 5 和 GLM-5.1 進行 A/B 測試嗎？
可以。兩者皆透過同一個相容 OpenAI 的端點提供服務，並使用同一把 API 金鑰 — 切換只需更改一行的模型字串，因此您可以將部分流量分別導向兩者並直接比較帳單。
Claude Sonnet 5 與 GLM-5.1 支援提示快取嗎？
是的 — 兩者的快取讀取費率皆低於其輸入費率，因此具有暖前綴的工作負載成本會低於牌價所示。確切的快取讀取列請見上方的定價表。