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GPT-5.6 Sol vs Qwen3.8 Max

vs

何时使用哪一个 — 经过整理的结论,而非基准测试表格

这两者形态接近——都接受文本和图像输入、返回文本,都带对话、视觉、代码、工具和推理——所以差别主要在价格和旋钮:qwen3.8-max 输入 $2、输出 $6,对比 gpt-5.6-sol 的 $5 和 $30,输入便宜 2.5 倍、输出便宜 5 倍,缓存读取 $0.25 对 $0.5。想要略大的 1050000 token 上下文,或为延迟敏感调用关闭思考的能力时选 gpt-5.6-sol;983616 token 窗口和 131072 最大输出够用的大批量工作选 qwen3.8-max。

定价

GPT-5.6 Sol Qwen3.8 Max Δ
输入 / 1M tokens $5 $2 2.5×
输出 / 1M tokens $30 $6
缓存读取 / 1M tokens $0.5 $0.25
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 63 个 聊天 模型的 每 1M token 的输入价格(对数刻度)

GPT-5.6 Sol · $5 Qwen3.8 Max · $2
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

GPT-5.6 Sol 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-5.6 Sol 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 2026-07-09 2026-08-03
知识截止日期 2026-02
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-5.6 Sol · 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-5.6 Sol 通过 · 3 sentences

Enabling reasoning makes the model generate additional internal tokens before producing an answer, so each request consumes more billable compute. Reasoning may also increase response length, latency, tool calls, and retries, further raising per-request cost. In short, usage volume may be unchanged, but the cost per task increased enough to double the bill.

输出 111 tok (+34 思考) 延迟 2.3 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-5.6 Sol 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 function counted equal *adjacencies* rather than items, so a run of length one produced `0` and every longer run was undercounted by one. Initializing `cur` and `best` to `1` for nonempty input makes them represent item counts, resetting `cur` to `1` starts a new run at the current item, and updating `best` on every iteration ensures a run reaching the final index is included.

输出 316 tok (+135 思考) 延迟 5.2 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-5.6 Sol 通过 · 5/5 fields, guidance "withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":"11+","guidance":"withheld pending the Q4 close"}

输出 194 tok (+147 思考) 延迟 3.2 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-5.6 Sol 通过 · 120 words, 0 banned, 1 question

Today, we’re introducing Provider Prompt Cache, a new API gateway feature that reuses prompt prefixes across supported AI providers, reducing latency, token costs, and duplicated processing. Teams can define cache policies once, route requests dynamically, and preserve provider flexibility without rewriting application logic. Switching models during testing or failover? The gateway identifies eligible prompt segments, applies provider-specific caching controls, and reports hits, misses, savings, and expiration details through unified logs and metrics. Configurable TTLs, tenant isolation, encryption, and cache-bypass options help teams balance performance, privacy, and freshness for every workload. Provider Prompt Cache is available today in beta through the dashboard and API, with SDK examples and migration guidance included. […]

输出 733 tok (+564 思考) 延迟 7.7 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-5.6-sol",
    # 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-5.6 Sol 和 Qwen3.8 Max 哪个更便宜?

在 输入 / 1m tokens 方面,Qwen3.8 Max 更便宜($2 对比 $5,相差 2.5×)。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-5.6 Sol 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-5.6 Sol 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