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GPT-6 Luna vs Qwen3.8 Flash

vs

何時用哪一個

這兩者在費率表上非常接近:gpt-6-luna 每百萬 token 收費為輸入 $0.1、輸出 $0.5,而 qwen3.8-flash 輸入收費為 $0.15(為 Luna 的 1.5 倍),輸出則略低,為 $0.47,快取讀取為 $0.01 對上 $0.016。對於像是長文件填塞與快取提示等偏重輸入的工作,請選擇 gpt-6-luna,這得益於其較低的輸入與快取讀取費率以及 1050000 token 的脈絡長度;當你需要文字和圖片以外的影片輸入,或是以生成為主,且其 131072 token 的最大輸出與更便宜的輸出費率很重要時,請選擇 qwen3.8-flash。

Benchmark 成績

高於同儕均值無人分數更高GPT-6 Luna僅 1 項可比Qwen3.8 Flash12 / 163 / 16
GPT-6 Luna Qwen3.8 Flash 其他被測模型 同儕均值 無人分數更高
DeepSWE 1.1
66.6%
58.7%
OSWorld 2.0 partial
N/A
52.3%
JobBench
N/A
55.7%
GPQA Diamond
N/A
91.7%
ERQA
N/A
無人分數更高 72.3%
Agents' Last Exam Pass
N/A
24.3%
LVBench
N/A
76.6%

廠商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

定價

GPT-6 Luna Qwen3.8 Flash Δ
輸入 / 1M tokens $0.1 $0.15 0.67×
輸出 / 1M tokens $0.5 $0.47 1.1×
快取讀取 / 1M tokens $0.01 $0.016 0.63×
快取寫入 不額外計費 1.25x -

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 74 個 聊天 模型的 每 1M tokens 的輸入價格(對數尺度)

GPT-6 Luna · $0.1 Qwen3.8 Flash · $0.15
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

GPT-6 Luna Qwen3.8 Flash
工具使用
思考控制 可配置 可配置
結構化輸出
提示快取 隱式(自動) 隱式 + 顯式
快取生命週期 5-10m, up to 1h explicit: 5m, reset on hit
最小快取前綴 1024 個 token 1024 個 token

規格

GPT-6 Luna Qwen3.8 Flash
輸入模態 文字 影像 文字 影像 影片
輸出模態 文字 文字
發布日期 2026-09-22 2026-08-27
知識截止日期 2026-05 -
上下文視窗 1.1M 1M
最大輸出 128K 131K
思考參數 reasoning.effort
  • enable_thinking
  • thinking_budget
  • preserve_thinking
可接受的值
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
enable_thinking
  • true
  • false
thinking_budget
  • in tokens
preserve_thinking
  • true
  • false
預設值 medium

on

qwen3.8-flash is hybrid thinking with thinking enabled by default, thinking_budget defaults to the 262,144-token chain maximum, and preserve_thinking is off

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-6 Luna · Qwen3.8 Flash

單一提示詞,兩款模型 — 經由閘道測量

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-6 Luna 通過 · 3 sentences

Enabling reasoning typically makes the model process more information and generate more internal tokens before producing its answer. Because usage is billed by tokens—and reasoning may also route requests to a more expensive model—the cost per request can rise sharply. If request volume stayed similar, that higher cost per request could explain why the bill doubled.

輸出 117 tok (+48 思考) 延遲 3.4 s

Qwen3.8 Flash 通過 · 3 sentences

Enabling reasoning makes the model spend additional output tokens to plan and validate answers before responding, and those tokens are billed. It also increases compute and latency per request, so each task consumes more capacity than a non-reasoning call. As a result, billable tokens and compute roughly doubled, directly driving the higher invoice.

輸出 330 tok (+262 思考) 延遲 8.9 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。

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-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: cur = 1 best = max(best, cur) return best ``` The original code counted matching *adjacent pairs* rather than items, so a run of length `n` was reported as `n - 1`, and a single-item input returned `0`. This version counts the current item as part of each run and updates `best` at every index, including the last; it returns `0` for an empty input.

輸出 514 tok (+356 思考) 延遲 6.9 s

Qwen3.8 Flash 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 the original version counts adjacent equal pairs rather than items in a run, starting both `cur` and `best` at `0`, and it resets `cur` to `0` after a mismatch, thereby discarding the first element of the new run. That makes single-element inputs return `0`, and because every run is effectively measured in “steps” between equal elements, the last element is not counted—particularly noticeable when the longest run ends at the final index.

輸出 2123 tok (+1933 思考) 延遲 43.8 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-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 161 tok (+119 思考) 延遲 21.7 s

Qwen3.8 Flash 通過 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }

輸出 519 tok (+463 思考) 延遲 10.5 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-6 Luna 通過 · 120 words, 0 banned, 1 question

Introducing Prompt Cache, a new API gateway feature that recognizes repeat prompt prefixes and reuses provider-side cached context across supported models. Teams can route requests to different AI providers while preserving eligible cache hits, reducing redundant input processing and helping lower latency and token costs. Configure cache policies in one place, monitor hit rates by provider, and keep existing client integrations unchanged. The gateway applies provider-specific rules automatically, so developers do not need to build separate caching logic for each endpoint. Which workflows could benefit from faster responses and predictable spend? Prompt Cache is available today in preview for eligible accounts, with usage details, supported providers, and setup guidance in the dashboard. Start with one route, compare results, then expand.

輸出 959 tok (+813 思考) 延遲 14.6 s

Qwen3.8 Flash 通過 · 120 words, 0 banned, 1 question

We are announcing prompt caching across providers in our API gateway. Store approved prompt outputs once and reuse them across supported model providers, cutting latency, cost, and duplicate token spend. The feature matches identical requests, checks validity, and returns cached results while preserving routing controls. Developers keep existing endpoints; the gateway manages storage, invalidation, and provider differences. This reduces noisy repeat calls, improves steady responses, and frees teams to focus on better agent workflows. OpenAI, Anthropic, Google, Mistral, and custom routes are supported. Cache hits appear in analytics with latency, token, and cost reductions visible. Check retention and privacy rules before enabling it. Ready to add cache controls to your gateway? Enable it in settings and watch spend drop now.

輸出 5958 tok (+5805 思考) 延遲 88.8 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-6-luna",
    # model="qwen3.8-flash",  # 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)

取得 API 金鑰 →

常見問題

GPT-6 Luna 和 Qwen3.8 Flash 哪個比較便宜?

GPT-6 Luna 在 輸入 / 1m tokens 上較便宜($0.1 對比 $0.15,相差 1.5×)。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-6 Luna 和 Qwen3.8 Flash 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-6 Luna 與 Qwen3.8 Flash 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。

相關比較