GPT-6.1 Sol vs Qwen3.8 Flash
何时用哪一个
两者都以几乎相等的规模覆盖了文本和图像输入，并支持对话、代码、工具、推理和视觉（gpt-6.1-sol 为 1050000 tokens 上下文和 128000 最大输出，qwen3.8-flash 为 1000000 和 131072），因此真正的区别在于价格和控制能力。qwen3.8-flash 在输入上的成本低约 13x（$0.15 对比 $2），在输出上的成本低约 21x（$0.47 对比 $10），在缓存读取上的成本低约 6x（$0.016 对比 $0.1），同时接受视频输入，并允许你关闭 thinking。当你想要 OpenAI 较新的 2026-09-29 版本且成本可以接受时，请选择 gpt-6.1-sol。
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 Flash | Δ | |
|---|---|---|---|
| 输入 / 1M tokens | $2 | $0.15 | 13× |
| 输出 / 1M tokens | $10 | $0.47 | 21× |
| 缓存读取 / 1M tokens | $0.1 | $0.016 | 6.3× |
| 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 Flash | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 始终开启 | 可配置 |
| 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 Flash | |
|---|---|---|
| 输入模态 | 文本 图像 | 文本 图像 视频 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-09-29 | 2026-08-27 |
| 知识截止日期 | 2026-04 | - |
| 上下文窗口 | 1.1M | 1M |
| 最大输出 | 128K | 131K |
| 思考参数 | reasoning.effort |
|
| 允许的值 | reasoning.effort
| enable_thinking
thinking_budget
preserve_thinking
|
| 默认值 | 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 |
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 Flash
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 Flash 通过 · 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.
输出 330 tok (+262 思考) 延迟 8.9 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 Flash 通过 · 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.
输出 2123 tok (+1933 思考) 延迟 43.8 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 Flash 通过 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
输出 519 tok (+463 思考) 延迟 10.5 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 Flash 通过 · 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.
输出 5958 tok (+5805 思考) 延迟 88.8 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-flash", # 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-flash", // 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-flash", # 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-flash", // 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-flash") // 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 Flash 哪个更便宜？
在 输入 / 1m tokens 方面，Qwen3.8 Flash 更便宜（$0.15 对比 $2，相差 13×）。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-6.1 Sol 和 Qwen3.8 Flash 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 Flash 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。