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GPT-6 Luna vs GPT-6 Sol

vs

何時用哪一個

gpt-6-luna 和 gpt-6-sol 在規格上完全相同:兩者均具備 1050000-token 的上下文、128000-token 的最大輸出、文字和影像輸入與文字輸出、相同的聊天/視覺/程式碼/工具/推理標籤、可選的思考功能,以及 2026-09-22 的發佈日期。兩者的差異在於價格:gpt-6-sol 各個項目的費用都貴 20x(輸入 $2 對 $0.1,輸出 $10 對 $0.5,快取讀取 $0.2 對 $0.01),因此請將 gpt-6-luna 作為高流量及長上下文任務的預設選項。只有當使用您自己的提示詞進行測試後證明值得這 20x 的費用時,才去選擇 gpt-6-sol;gpt-6-luna 甚至具備較晚的知識截止日期,為 2026-05 對上 2026-04。

Benchmark 成績

高於同儕均值GPT-6 Luna僅 1 項可比GPT-6 Sol僅 4 項可比
GPT-6 Luna GPT-6 Sol 其他被測模型 同儕均值 無人分數更高
DeepSWE 1.1
66.6%
68.8%
OSWorld 2.0 offline set, partial
N/A
60.5%
Agents' Last Exam
N/A
56.4%

廠商公布: Alibaba (Qwen) Anthropic DeepSeek Google OpenAI Z.ai

定價

GPT-6 Luna GPT-6 Sol Δ
輸入 / 1M tokens $0.1 $2 0.05×
輸出 / 1M tokens $0.5 $10 0.05×
快取讀取 / 1M tokens $0.01 $0.2 0.05×
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 74 個 聊天 模型的 每 1M tokens 的輸入價格(對數尺度)

GPT-6 Luna · $0.1 GPT-6 Sol · $2
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

GPT-6 Luna GPT-6 Sol
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(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-6 Luna GPT-6 Sol
輸入模態 文字 影像 文字 影像
輸出模態 文字 文字
發布日期 2026-09-22 2026-09-22
知識截止日期 2026-05 2026-04
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-6 Luna · GPT-6 Sol

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

GPT-6 Sol 通過 · 3 sentences

Enabling reasoning made the model generate extra internal tokens to work through each request, and those tokens are billed even though users don’t see them. If request volume stayed roughly the same, more billed tokens per request could explain why the bill doubled. We should check the usage breakdown to confirm, then lower reasoning effort or limit it to tasks that need it.

輸出 188 tok (+111 思考) 延遲 5.0 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。

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

GPT-6 Sol 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 `cur` counted matching *adjacent pairs*, not items, so every run was one too short—including a single-element input, which returned 0. Starting each run at 1 counts its first item, and updating `best` on every iteration ensures a run that reaches the last index is included.

輸出 206 tok (+59 思考) 延遲 5.9 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

GPT-6 Sol 通過 · 5/5 fields, guidance "Withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":null,"growth_pct":null,"guidance":"Withheld pending the Q4 close"}

輸出 227 tok (+185 思考) 延遲 5.2 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

GPT-6 Sol 通過 · 120 words, 0 banned, 1 question

Today, we’re introducing cross-provider prompt caching for our API gateway, helping teams reuse repeated prompt content when routing requests among supported AI providers. The gateway identifies eligible prompt prefixes, stores cache references, and applies them to subsequent requests where provider capabilities allow. Why send the same context again if it can be reused? With fewer duplicate input tokens, applications can reduce costs and improve latency without changing how developers call the gateway. Configure caching by route, monitor hit rates and savings in your dashboard, and keep existing provider fallback rules in place. Availability and cache behavior vary by provider, so the gateway reports eligibility and usage for each request. Get started today with our documentation and test your existing workloads.

輸出 586 tok (+443 思考) 延遲 7.7 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="gpt-6-sol",  # 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 GPT-6 Sol 哪個比較便宜?

GPT-6 Luna 在 輸入 / 1m tokens 上較便宜($0.1 對比 $2,相差 20×)。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 GPT-6 Sol 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 GPT-6 Sol 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