Claude Sonnet 5 vs GLM-5.2
什麼情況選哪一個
兩個模型都有 1,000,000 token 上下文視窗，都可關閉思考，所以真正的分野是模態和價目表：claude-sonnet-5 接受圖像和文字輸入，每百萬 token 收 $2 輸入 / $10 輸出；glm-5.2 只接受文字，$1.4 輸入 / $4.4 輸出，輸入便宜約 1.4 倍、輸出便宜 2.3 倍。需要圖像輸入或它明確的思考能力時選 claude-sonnet-5，並注意它的快取讀取更便宜，$0.2 對 $0.26；大批量文字和長上下文工作選 glm-5.2，更低的 token 單價和 131072 最大輸出 token 更重要。
Benchmark 成績
兩邊都有成績的有 5 項。
供應商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| Claude Sonnet 5 | GLM-5.2 | 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) | - | - |
費率取自網站建置時的即時目錄；各模型頁面都列有最新的價目。
兩者的相對位置：每 1M tokens 的輸入價格，涵蓋同一計費單位下全部 76 個聊天模型（對數尺度）
功能
| Claude Sonnet 5 | GLM-5.2 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可設定 | 可設定 |
| 結構化輸出 | 是 | 是 |
| 提示詞快取 | 顯式（由你標記前綴） | 隱式（自動） |
| 快取存活時間 | 5m default, 1h option | 未公布 |
| 最小快取前綴 | 1024 個 token | 未公布 |
規格
| Claude Sonnet 5 | GLM-5.2 | |
|---|---|---|
| 輸入模態 | 文字 圖像 | 文字 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-06-30 | 2026-06-16 |
| 知識截止日期 | 2026-01 | - |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 128K | 131K |
| 思考參數 |
|
|
| 可接受的值 | thinking.type
effort
"enabled" returns 400 | thinking.type
reasoning_effort
|
| 預設值 | thinking on (adaptive) effort
| enabled, with reasoning_effort at max: the only GLM with an effort dial, and it defaults to the top of it |
規格摘錄自各供應商的文件；供應商沒有公布的項目就直接略過，不自行推測。 完整來源： Claude Sonnet 5 · GLM-5.2
同一段提示詞，兩個模型，經閘道實測
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-5.2 通過 · 3 sentences
Enabling reasoning means the LLM now generates thousands of invisible "thinking" tokens to systematically work through complex problems before producing a final answer. Because our cloud providers bill for these internal processing steps at the same rate as standard output, our billable token volume per query has doubled. While this increases our direct API costs, it drastically reduces error rates and manual review labor, ultimately lowering our total cost per resolved transaction.
輸出 1223 tok (+1138 思考) 延遲 17.1 s
是否照指令做（剛好三句，數得出來）、語氣是否貼合對象（寫給 CFO 看），以及下方 token 計量顯示出的隱藏思考計費落差。
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-5.2 未通過 · 1/8 cases (fails [1])
```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 […]
輸出 4097 tok (+4036 思考) 延遲 58.4 s
修正是否真的正確（跑得起來）、說明的資訊密度，以及在範圍明確的任務上的 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-5.2 通過 · 5/5 fields, guidance "withheld"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld" } ```
輸出 1947 tok (+1893 思考) 延遲 30.9 s
是否遵守 schema（不自創欄位）、面對幻覺誘因的表現（題目明確不給 guidance），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-5.2 通過 · 120 words, 0 banned, 1 question
We are introducing Caching for our API Gateway, the smartest way to optimize your workflows. Why pay for the exact same response twice? Now, you can automatically store and reuse prompt results across multiple AI providers, drastically reducing latency and overall operational costs. If a user submits a duplicate query, the gateway serves the cached answer instantly, regardless of whether you route to OpenAI, Anthropic, or others. This directly translates to faster applications and significantly lower monthly API bills. You can easily configure your specific caching rules within the developer dashboard and watch your efficiency soar. Stop wasting your valuable tokens on completely redundant computations. Upgrade to the latest gateway version today and experience the future of intelligent prompt management.
輸出 11125 tok (+10984 思考) 延遲 114.8 s
是否遵守限制（字數上限、禁用詞清單、只能有一句問句）、文字風格的特徵，以及長度控制。
改一行程式碼就能在兩者之間切換
下面每個頁籤都列了這兩個模型 ID，要改的只有醒目標示的那兩行。端點、金鑰和請求格式都不變。
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-5.2", # 取消這一行的註解，並把上一行註解掉
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-5.2", // 取消這一行的註解，並把上一行註解掉
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-5.2", # 取消這一行的註解，並把上一行註解掉
"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-5.2", // 取消這一行的註解，並把上一行註解掉
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-5.2") // 取消這一行的註解，並把上一行註解掉
.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-5.2 哪個比較便宜？
以「輸入 / 1M tokens」來看，GLM-5.2 比較便宜（$1.4 對 $2，相差 1.4×）。其他項目的結果可能相反，完整價目請看上表；實際成本要看你的用量組合。
可以只串接一次，就對 Claude Sonnet 5 和 GLM-5.2 做 A/B 測試嗎？
可以。兩個模型都走同一個 OpenAI 相容端點，用的也是同一把 API 金鑰，切換時只要改一行裡的模型名稱字串。你可以把一部分流量分別導到兩邊，再直接比較帳單。
Claude Sonnet 5 與 GLM-5.2 支援提示詞快取嗎？
支援。兩者的快取讀取費率都低於輸入費率，所以前綴已經進快取的工作負載，實際成本會比官網價算出來的低。確切的快取讀取價格請見上方定價表。