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GPT-6 Luna vs Qwen3.8 Max

vs

何时用哪一个

这两者在纸面参数上涵盖了相同的领域:文本和图像输入,文本输出,都支持对话、视觉、代码、工具和推理,拥有大约一百万 token 的上下文(gpt-6-luna 为 1050000,qwen3.8-max 为 983616),以及相当的 128000 和 131072 输出 token 上限。定价是它们的分水岭:qwen3.8-max 的输入成本高出 20x,输出成本高出 12x,缓存读取成本高出 25x,因此 gpt-6-luna 是大批量任务的默认选择,且其关闭思考功能的能力也为你提供了一个便宜的非推理模式。仅当你明确需要 Alibaba 主打长上下文的模型时,才去选择 qwen3.8-max。

Benchmark 成绩

高于同侪均值无人分数更高GPT-6 Luna仅 1 项可比Qwen3.8 Max31 / 408 / 40
GPT-6 Luna Qwen3.8 Max 其他被测模型 同侪均值 无人分数更高
DeepSWE 1.1
66.6%
56.6%
AndroidBench
N/A
75.1%
Cybergym
N/A
78.5%
HealthBench
N/A
无人分数更高 60.2%
JobBench
N/A
53.4%
PLawBench
N/A
无人分数更高 73.2%
GPQA Diamond
N/A
92.6%
Agents' Last Exam
N/A
27%

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

定价

GPT-6 Luna Qwen3.8 Max Δ
输入 / 1M tokens $0.1 $2 0.05×
输出 / 1M tokens $0.5 $6 0.083×
缓存读取 / 1M tokens $0.01 $0.25 0.04×
缓存写入 不单独收费 1.25x -

费率取自构建时的实时目录;每个模型页面均附有当前的费率卡。

它们的位置 — 以该计费单位计费的所有 74 个 聊天 模型的 每 1M token 的输入价格(对数刻度)

GPT-6 Luna · $0.1 Qwen3.8 Max · $2
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

GPT-6 Luna Qwen3.8 Max
工具使用
思考控制 可配置 是 —— 厂商未公布调节参数
结构化输出
提示词缓存 隐式(自动) 隐式 + 显式
缓存生存时间 5-10m, up to 1h explicit: 5m, reset on hit
最小缓存前缀 1024 个 token 1024 个 token

规格

GPT-6 Luna Qwen3.8 Max
输入模态 文本 图像 文本 图像
输出模态 文本 文本
发布日期 2026-09-22 2026-08-03
知识截止日期 2026-05 -
上下文窗口 1.1M 984K
最大输出 128K 131K
思考参数 reasoning.effort -
允许的值
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
-
默认值 medium -

规格转录自各供应商的文档;若供应商未发布某项数据,则直接省略该行,而非进行推断。 完整来源: GPT-6 Luna · Qwen3.8 Max

单个 Prompt,两个模型 —— 通过网关实测

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

GPT-6 Luna 通过 · 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.

输出 117 tok (+48 思考) 延迟 3.4 s

Qwen3.8 Max 通过 · 3 sentences

Enabling reasoning makes the model produce additional hidden steps before responding, and those tokens are billable. It also tends to lengthen each interaction because the model works through more possibilities before settling on an answer. Therefore, the bill doubled mainly due to higher compute and token usage per request, not necessarily because the number of requests doubled.

输出 378 tok (+305 思考) 延迟 8.6 s

指令遵循(恰好三句,可数)、受众适配(面向 CFO 的语气),以及下方 token 计量所暴露的隐藏思考计费缺口。

提示词 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. 检查 修复通过测试

GPT-6 Luna 通过 · 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.

输出 514 tok (+356 思考) 延迟 6.9 s

Qwen3.8 Max 通过 · 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 bug is that the original code starts `best` and `cur` at `0`, so it counts adjacent equal *transitions* rather than the number of items in the run. A run of length `n` has only `n - 1` equal-neighbor transitions, so single-element inputs return `0`, and runs that reach the end are undercounted by one. Initializing the current run to `1` for the first element, resetting it to `1` on a break, and updating `best` from that count fixes the off-by-one.

输出 1616 tok (+1411 思考) 延迟 34.7 s

修复是否真的正确(可运行)、解释的信息密度,以及在一个边界明确的任务上的 token 效率。

提示词 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. 检查 合法 JSON,schema 精确

GPT-6 Luna 通过 · 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"}

输出 161 tok (+119 思考) 延迟 21.7 s

Qwen3.8 Max 通过 · 5/5 fields, guidance null

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

输出 1199 tok (+1141 思考) 延迟 24.4 s

schema 服从度(不臆造字段)、幻觉压力(guidance 明确被暂缓给出),以及结构化输出路径的差异。

提示词 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. 检查 120 词,0 个禁用词

GPT-6 Luna 通过 · 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.

输出 959 tok (+813 思考) 延迟 14.6 s

Qwen3.8 Max 通过 · 120 words, 0 banned, 1 question

Today, our API gateway adds prompt caching across major model providers. It stores prompts and responses in one fast cache layer. Teams can lower token spend, reduce latency, and repeat reliable answers. The feature supports OpenAI, Anthropic, Google, and Mistral through one configuration. You can set retention rules, scope access, and invalidate entries quickly. How does your team maintain consistent results during provider outages? Approved cached responses keep applications stable while fallback routes recover. The dashboard shows hit rates, savings, latency, and provider usage. Engineers receive audit trails for every cached prompt, enabling safer testing. Product managers can compare cost trends before and after cache adoption. Start with a small route, then safely expand caching to production traffic right now.

输出 2744 tok (+2591 思考) 延迟 46.3 s

约束服从度(字数预算、禁用词表、唯一的那句问句)、文风指纹,以及长度控制。

只需一行代码即可在它们之间切换

两个 ID 都包含在下方的每个选项卡中 — 高亮显示的两行是唯一的修改。相同的端点,相同的密钥,相同的请求结构。

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-max",  # 取消注释此行,注释上一行
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

获取 API 密钥 →

常见问题

GPT-6 Luna 和 Qwen3.8 Max 哪个更便宜?

在 输入 / 1m tokens 方面,GPT-6 Luna 更便宜($0.1 对比 $2,相差 20×)。其他行可能得出相反的结论——上方表格提供了完整信息,实际成本取决于你的组合使用情况。

我可以在不进行两次集成的情况下,对 GPT-6 Luna 和 Qwen3.8 Max 进行 A/B 测试吗?

可以。两者均通过同一个兼容 OpenAI 的端点提供服务,并使用同一把 API 密钥——切换只需更改一行模型字符串,因此你可以将一部分流量路由到各个模型并直接比较账单。

GPT-6 Luna 和 Qwen3.8 Max 支持提示词缓存吗?

是的 —— 两者对缓存读取的计费均低于其输入费率,因此热前缀工作负载的成本低于标价。准确的缓存读取行位于上方的定价表中。

相关对比

来自我们的实测研究