GPT-5.4 vs GPT-5.6 Luna
什麼情況選哪一個
從這些資訊來看，兩者只在價格與日期上有差別：都支援文字與圖片輸入，上下文視窗都是 1050000 token，最大輸出都是 128000 token，也都可以關閉思考模式。gpt-5.6-luna 的每一項費率都更低：每百萬 token 輸入 $0.2、輸出 $1.2、快取輸入 $0.02，而 gpt-5.4 為 $2.5、$15 與 $1.25，輸入與輸出價格是 gpt-5.6-luna 的 12.5 倍，快取讀取價格則是 62.5 倍。優先選 gpt-5.6-luna，它的知識截止日期也較晚，為 2026-02（gpt-5.4 為 2025-08）；只有想繼續使用較早的版本時才選 gpt-5.4。
Benchmark 成績
兩邊都有成績的有 14 項。
供應商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| GPT-5.4 | GPT-5.6 Luna | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $2.5 | $0.2 | 13× |
| 輸出 / 1M tokens | $15 | $1.2 | 13× |
| 快取讀取 / 1M tokens | $1.25 | $0.02 | 63× |
| 快取寫入 | 不額外計費 | 不額外計費 | - |
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 78 個聊天模型（對數尺度）
功能
| GPT-5.4 | GPT-5.6 Luna | |
|---|---|---|
| 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 | 隱式（自動） | 隱式（自動） |
| 快取存活時間 | 5-10m, up to 1h | 5-10m, up to 1h |
| 最小快取前綴 | 1024 個 token | 1024 個 token |
規格
| GPT-5.4 | GPT-5.6 Luna | |
|---|---|---|
| 輸入模態 | 文字 圖像 | 文字 圖像 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-03-05 | 2026-07-09 |
| 知識截止日期 | 2025-08 | 2026-02 |
| 上下文視窗 | 1.1M | 1.1M |
| 最大輸出 | 128K | 128K |
| 思考參數 | reasoning.effort | reasoning.effort |
| 可接受的值 | reasoning.effort
| reasoning.effort
|
| 預設值 | none | medium |
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： GPT-5.4 · GPT-5.6 Luna
同一段提示詞，兩個模型，經閘道實測
GPT-5.4 通過 · 3 sentences
Enabling reasoning makes the model generate and evaluate additional internal steps before producing an answer, which increases the total tokens processed and therefore the cost per request. It also tends to increase latency and can trigger more tool calls, retries, or longer context retention, all of which compound usage-based charges. So even if user traffic stayed flat, the average cost per interaction rose materially once reasoning was turned on.
輸出 82 tok 延遲 1.9 s
GPT-5.6 Luna 通過 · 3 sentences
Enabling reasoning causes the model to use substantially more computational steps and generated tokens per request, even when the visible answer is similar. Because our provider charges based largely on token consumption and compute, that increased processing translated into roughly twice the cost. We can control the expense by reserving reasoning for complex tasks, setting token and effort limits, and monitoring cost per successful outcome.
輸出 146 tok (+60 思考) 延遲 3.1 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。
GPT-5.4 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 else: best = max(best, cur) cur = 1 best = max(best, cur) return best ``` The bug is that the original code was counting *matching transitions* between adjacent elements instead of the *length of the run itself*, so a single element started at `0` instead of `1`, making all run lengths off by one. It also only updated `best` inside the equality case, which means the final run could be lost if the loop ended before a change triggered any correction logic. […]
輸出 243 tok 延遲 3.0 s
GPT-5.6 Luna 通過 · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: best = max(best, cur) cur = 1 return max(best, cur) ``` The bug is that `cur` counts equal *transitions* rather than the number of items in the current run, so a single-element run returns `0` and a run of length `n` returns `n - 1`; additionally, `best` is only updated when a run is interrupted, so a run continuing through the final element is not finalized before returning. Initializing the current run to one item, resetting it to one after a change, and taking a final `max` fixes both issues.
輸出 356 tok (+161 思考) 延遲 4.7 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。
GPT-5.4 通過 · 5/5 fields, guidance "withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11,"guidance":"withheld pending the Q4 close"}
輸出 39 tok 延遲 1.3 s
GPT-5.6 Luna 通過 · 5/5 fields, guidance "Withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":null,"guidance":"Withheld pending the Q4 close"}
輸出 227 tok (+181 思考) 延遲 3.6 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。
GPT-5.4 通過 · 126 words, 0 banned, 1 question
Today we’re introducing Prompt Cache, a new API gateway feature that stores prompt results and reuses them across supported model providers. Teams can reduce latency, lower repeated compute costs, and keep application behavior more consistent when identical or near-identical prompts appear in production. Prompt Cache works at the gateway layer, so developers can adopt it without rewriting provider integrations or changing routing logic. Built-in controls let you set cache keys, expiration policies, and privacy rules for sensitive workloads. Need faster responses without duplicating optimization work for each vendor? With shared observability, you can track hit rates, savings, and cache performance in one place. […]
輸出 154 tok 延遲 2.9 s
GPT-5.6 Luna 通過 · 120 words, 0 banned, 1 question
Introducing PromptCache, an API gateway feature that caches prompts across providers, helping teams reduce latency, control spend, and deliver consistent results. How much faster could your applications respond when repeated prompts are served from a shared cache instead of being sent upstream? PromptCache supports provider-aware routing, configurable time-to-live policies, encrypted storage, cache invalidation, and usage analytics through one operational layer. It works with language-model providers while preserving your authentication, observability, and fallback workflows. Developers can enable caching by endpoint, model, tenant, or prompt pattern, then monitor hit rates and savings in real time. Built for production workloads, PromptCache gives platform teams controls for performance and cost without requiring application rewrites. […]
輸出 948 tok (+778 思考) 延遲 8.0 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="gpt-5.4",
# model="gpt-5.6-luna", # 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: "gpt-5.4",
// model: "gpt-5.6-luna", // 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": "gpt-5.4",
# "model": "gpt-5.6-luna", # 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: "gpt-5.4",
// Model: "gpt-5.6-luna", // 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("gpt-5.4")
// .model("gpt-5.6-luna") // 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
GPT-5.4 和 GPT-5.6 Luna 哪個比較便宜？
以「輸入 / 1M tokens」來看，GPT-5.6 Luna 比較便宜（$0.2 對 $2.5，相差 13×）。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 GPT-5.4 和 GPT-5.6 Luna 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。
GPT-5.4 與 GPT-5.6 Luna 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。