Claude Fable 5 vs GPT-5.6
何時該用哪一個 — 綜合評斷,而非基準測試數據表
兩者皆接受文字與圖片輸入,回傳文字,並將輸出限制在 128000 token,因此真正的差異在於價格與控制:gpt-5.6 的收費為輸入 $5 / 輸出 $30 / 快取讀取 $0.5,對比 claude-fable-5 的 $10 / $50 / $1,使得 Anthropic 的模型在輸入與快取成本上為 2x,輸出成本約為 1.67x,且擁有略小一些的 1000000-token 視窗對比 1050000。針對想要關閉思考功能的高用量或對延遲敏感的工作請選擇 gpt-5.6;當你希望將其常駐推理模式應用於每個請求並願意支付溢價時請選擇 claude-fable-5。
定價
| Claude Fable 5 | GPT-5.6 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $10 | $5 | 2× |
| 輸出 / 1M tokens | $50 | $30 | 1.7× |
| 快取讀取 / 1M tokens | $1 | $0.5 | 2× |
| 快取寫入 | 1.25x (5m) / 2x (1h) | 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)
能力
| Claude Fable 5 | GPT-5.6 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 常駐開啟 | 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 | 5–10m, up to 1h |
| 最小快取前綴 | 1024 個 token | 1024 個 token |
規格
| Claude Fable 5 | GPT-5.6 | |
|---|---|---|
| 輸入模態 | 文字 影像 | 文字 影像 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-06-09 | 2026-07-09 |
| 知識截止日期 | 2026-01 | 2026-02 |
| 上下文視窗 | 1M | 1.1M |
| 最大輸出 | 128K | 128K |
| 思考參數 | output_config.effort (thinking.type is adaptive-only and needs no configuration) | reasoning.effort |
| 可接受的值 | effort
both "enabled" and "disabled" return 400 | reasoning.effort
|
| 預設值 | thinking always on (adaptive) effort
| 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: Claude Fable 5 · GPT-5.6
單一提示詞,兩款模型 — 經由閘道測量
Claude Fable 5 通過 · 3 sentences
When we enabled reasoning, the model began generating extensive internal "thinking" tokens before each answer, and those tokens are billed as output even though users never see them. Output tokens typically cost several times more than input tokens, so adding thousands of hidden reasoning tokens per request effectively multiplied our per-query cost. In short, we're paying for a much longer response behind the scenes in exchange for higher answer quality, and we can manage this by tuning reasoning effort levels or reserving reasoning mode for complex tasks only.
輸出 228 tok 延遲 6.7 s
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
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 Fable 5 通過 · 8/8 cases
## Fixed code ```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: cur = 1 best = max(best, cur) return best ``` ## Explanation The original function counts **adjacent equal pairs** rather than **items in a run**: a run of length *n* contains only *n − 1* equal neighbor pairs, so every result is off by one — a single-element list yields 0 instead of 1, and `[1, 2, 2]` yields 1 instead of 2 (which callers perceive as "missing" the run at the end). The fix is to initialize both `cur` and `best` to 1 (any nonempty list has a run of at least length 1), reset `cur` to 1 rather than 0 when
輸出 531 tok 延遲 12.5 s
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
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 Fable 5 通過 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "Q3 (September quarter)", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on interpretation: "up eleven and change" is rendered as 11 (the precise figure isn't stated), and guidance is null because it was explicitly withheld.
輸出 192 tok 延遲 6.5 s
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
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 Fable 5 通過 · 120 words, 0 banned, 1 question
**Introducing Cross-Provider Prompt Caching for our API Gateway** Starting today, your repeated prompts are cached once and reused across OpenAI, Anthropic, Google, and any other provider you route through the gateway. Instead of paying full token costs every time your application sends the same system prompt or context block, the gateway detects matching prefixes and serves cached results in milliseconds. Why keep spending money and latency on identical requests? Early adopters report cost reductions of up to 60 percent on high-volume workloads, along with faster median response times. Configuration is simple: enable caching in your dashboard, set a TTL, and choose which routes participate. Cache entries are encrypted at rest and never shared between accounts. Available now on all plans.
輸出 1173 tok 延遲 18.2 s
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
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-fable-5",
# model="gpt-5.6", # 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-fable-5",
// model: "gpt-5.6", // 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-fable-5",
# "model": "gpt-5.6", # 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-fable-5",
// Model: "gpt-5.6", // 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-fable-5")
// .model("gpt-5.6") // 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 Fable 5 和 GPT-5.6 哪個比較便宜?
GPT-5.6 在 輸入 / 1m tokens 上較便宜($5 對比 $10,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 Claude Fable 5 和 GPT-5.6 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 Fable 5 與 GPT-5.6 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。