GPT-6.1 Sol vs Qwen3.8 Max
何时用哪一个
两者都接收文本和图像输入，返回文本，且均对每百万输入 tokens 收费相同的 $2，因此区别在于输出和缓存：gpt-6.1-sol 每百万输出收费 $10（约为 qwen3.8-max $6 的 1.67x），但缓存读取为 $0.1 对比 $0.25，便宜 2.5x。对于输出密集的生成任务及其略大的 131072 最大输出，加上基于 983616-token 窗口的 long-context 标志，请选择 qwen3.8-max。对于缓存密集、频繁复用 prompt 的工作负载和更宽的 1050000-token 上下文，请选择 gpt-6.1-sol，但请记住其推理无法被关闭。
Benchmark 成绩
GPT-6.1 Sol:厂商没有公布过 benchmark 成绩。
厂商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定价
| GPT-6.1 Sol | Qwen3.8 Max | Δ | |
|---|---|---|---|
| 输入 / 1M tokens | $2 | $2 | = |
| 输出 / 1M tokens | $10 | $6 | 1.7× |
| 缓存读取 / 1M tokens | $0.1 | $0.25 | 0.4× |
| 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 76 个 聊天 模型的 每 1M token 的输入价格（对数刻度）
能力
| GPT-6.1 Sol | Qwen3.8 Max | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 始终开启 | 是 —— 厂商未公布调节参数 |
| 结构化输出 | 是 | 是 |
| 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-6.1 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 | 2026-09-29 | 2026-08-03 |
| 知识截止日期 | 2026-04 | - |
| 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
| - |
| 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-6.1 Sol · Qwen3.8 Max
单个 Prompt，两个模型 —— 通过网关实测
GPT-6.1 Sol 通过 · 3 sentences
Enabling reasoning makes the model do more computational work before answering, and many providers bill the resulting reasoning tokens even though users never see them. That means the same number of requests can cost twice as much because each request consumes more billable tokens, even if the visible answers stay the same length. We should confirm the increase in our usage breakdown and reserve higher reasoning levels for tasks where the improvement justifies the added cost.
输出 144 tok (+54 思考) 延迟 6.6 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。
GPT-6.1 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 counts equal adjacent pairs rather than items in a run: a run of length `n` has only `n - 1` equal pairs, so a single-element input returns `0`, and a run ending at the last index is also undercounted. It does process the last index, so the issue is not a missing final check. Initializing each nonempty run to `1` and updating `best` after every iteration correctly counts items, including singleton and trailing runs; empty inputs still return `0`.
输出 189 tok 延迟 6.3 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。
GPT-6.1 Sol 通过 · 5/5 fields, guidance "Withheld pending the Q4 close"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": "eleven and change", "guidance": "Withheld pending the Q4 close" }
输出 345 tok (+285 思考) 延迟 10.2 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。
GPT-6.1 Sol 通过 · 120 words, 0 banned, 1 question
Introducing Cross-Provider Prompt Cache, a new API gateway feature that stores reusable prompts and manages caching across your supported AI providers. Why rebuild the same context every time your application switches models? With one configuration, teams can reuse shared instructions, standardize cache policies, and reduce repeated prompt processing wherever provider caching is available. The gateway handles provider-specific requirements while giving you clear visibility into cache hits, usage, and estimated savings. Set expiration windows, isolate cached content by project, and invalidate entries when prompts change. Your existing routing logic stays intact, so you can compare models without rebuilding your caching workflow. […]
输出 588 tok (+435 思考) 延迟 13.9 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-6.1-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)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "gpt-6.1-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",
});
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": "gpt-6.1-sol",
# "model": "qwen3.8-max", # 取消注释此行，注释上一行
"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: "gpt-6.1-sol",
// Model: "qwen3.8-max", // 取消注释此行，注释上一行
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("gpt-6.1-sol")
// .model("qwen3.8-max") // 取消注释此行，注释上一行
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常见问题
GPT-6.1 Sol 和 Qwen3.8 Max 哪个更便宜？
它们列出的 输入 / 1m tokens 相同（$2），因此价格不是这一项的决定因素——请参考下文的规格与能力。
我可以在不进行两次集成的情况下，对 GPT-6.1 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-6.1 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。