GPT-5.6 vs GPT-5.6 Terra

Q: GPT-5.6 和 GPT-5.6 Terra 哪個比較便宜？

GPT-5.6 Terra 在 輸入 / 1m tokens 上較便宜（$2.5 對比 $5，相差 2.0×）。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。

更換任一側 vs

何時該用哪一個 — 綜合評斷，而非基準測試數據表

Terra 是半價的 GPT-5.6（$2.5/$15 對比 $5/$30），並具備相同的 1.05M 上下文。如果您的流量不需要完整模型的上限，Terra 屬於同系列、相同視窗但花費減半的選擇——這正是適當調整規模的定義。

定價

	GPT-5.6	GPT-5.6 Terra	Δ
輸入 / 1M tokens	$5	$2.5	2×
輸出 / 1M tokens	$30	$15	2×
快取讀取 / 1M tokens	$0.5	$0.25	2×
快取寫入	不額外計費	不額外計費	—

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）

GPT-5.6 · $5 GPT-5.6 Terra · $2.5

$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

	GPT-5.6	GPT-5.6 Terra
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	隱式（自動）	隱式（自動）
快取生命週期	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-5.6	GPT-5.6 Terra
輸入模態	文字影像	文字影像
輸出模態	文字	文字
發布日期	2026-07-09	2026-07-09
知識截止日期	2026-02	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 none low medium high xhigh max	reasoning.effort none low medium high xhigh max
預設值	medium	medium

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： GPT-5.6 · GPT-5.6 Terra

單一提示詞，兩款模型 — 經由閘道測量

針對 CFO 的推論修復包含錯誤的函式擷取至 schema 依照限制條件撰寫

提示詞 Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. 檢查恰好 3 句

GPT-5.6 通過 · 3 sentences

Enabling reasoning makes the model generate many additional internal tokens before producing an answer, so each request consumes substantially more compute. Those reasoning tokens are billed even though they are not visible to users, and longer processing can also increase tool calls, retries, and output length. The bill doubled because our cost per request rose, not necessarily because usage volume increased; we can control it by limiting reasoning effort and routing simpler tasks to cheaper models.

輸出 126 tok (+30 思考) 延遲 3.0 s

GPT-5.6 Terra 通過 · 3 sentences

Enabling reasoning makes the model generate additional internal analysis tokens and perform more computation before producing the final answer, increasing usage per request. Those extra tokens are billed like other model output or are reflected in higher-priced reasoning-model rates, so even unchanged user traffic can cost substantially more. The bill doubled because average cost per request rose, likely from longer reasoning traces, more complex model routing, or both.

輸出 120 tok (+31 思考) 延遲 2.4 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。

提示詞 This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. 檢查修復通過測試

GPT-5.6 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 original code counted equal *transitions* rather than items, so a run of one had length `0`, a run of three had length `2`, and tracking from zero caused the off-by-one error. The corrected version starts each run at length `1`, resets to `1` when the value changes, and performs a final `max` after the loop so a run ending at the last element is included; an empty input still returns `0`.

輸出 277 tok (+93 思考) 延遲 4.2 s

GPT-5.6 Terra 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: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` and `best` were counting adjacent *matches* rather than the number of items in a run: a run of one item has zero matching adjacent pairs, and a run of length `n` has only `n - 1` such pairs. Initializing both counters to `1` for a nonempty list and resetting `cur` to `1` when the value changes makes them represent actual run lengths. Updating `best` on every iteration also ensures a run that continues through the final element is included.

輸出 196 tok 延遲 2.6 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。

提示詞 Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. 檢查合法 JSON，schema 精確

GPT-5.6 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 179 tok (+118 思考) 延遲 3.7 s

GPT-5.6 Terra 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 149 tok (+103 思考) 延遲 2.3 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。

提示詞 Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. 檢查 120 詞，0 個禁用詞

GPT-5.6 通過 · 120 words, 0 banned, 1 question

Today we are introducing Prompt Cache, a new API gateway feature that stores reusable prompt prefixes and serves them across supported model providers. By recognizing identical system instructions, tools, examples, and context blocks, Prompt Cache reduces repeated input processing, lowers latency, and helps control token costs without changing application code. Teams can configure retention windows, cache keys, provider routing, encryption, and regional storage policies from one dashboard or API. What happens when a preferred provider is unavailable? The gateway can route requests to another provider while reusing eligible cached content, preserving performance and consistency. Built-in metrics report hit rates, savings, latency, and provider usage, while audit logs support governance. Prompt Cache is available today in public preview for all customers.

輸出 628 tok (+473 思考) 延遲 7.3 s

GPT-5.6 Terra 通過 · 120 words, 0 banned, 1 question

Today, our API gateway introduces cross-provider prompt caching, reducing repeated token processing while preserving your existing model routing. Teams can store reusable system prompts, policy blocks, and long context prefixes once, then serve them to supported providers with consistent cache keys and observability. The feature tracks hit rates, latency savings, and estimated cost reductions in the dashboard you already use for requests. Why pay to recompute identical context on every call? Configure cache policies by route, tenant, model, or TTL, and fall back automatically when a provider lacks compatible caching. Built-in controls help protect sensitive data through encryption, regional settings, and explicit expiration. Start with a single endpoint, compare results across providers, and scale prompt reuse without rewriting application logic.

輸出 631 tok (+475 思考) 延遲 6.0 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="gpt-5.6",
    # model="gpt-5.6-terra",  # 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: "gpt-5.6",
  // model: "gpt-5.6-terra",  // 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": "gpt-5.6",
    # "model": "gpt-5.6-terra",  # 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: "gpt-5.6",
		// Model: "gpt-5.6-terra",  // 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("gpt-5.6")
        // .model("gpt-5.6-terra")  // 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(""));

取得 API 金鑰 →

常見問題

GPT-5.6 和 GPT-5.6 Terra 哪個比較便宜？

GPT-5.6 Terra 在輸入 / 1m tokens 上較便宜（$2.5 對比 $5，相差 2.0×）。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 GPT-5.6 和 GPT-5.6 Terra 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。

GPT-5.6 與 GPT-5.6 Terra 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。

定價

能力

規格

單一提示詞，兩款模型 — 經由閘道測量

只需一行程式碼即可在兩者間切換

常見問題

相關比較