GPT-5.6 vs GPT-5.6 Sol
什么时候选哪个
就我们掌握的事实而言，gpt-5.6 和 gpt-5.6-sol 无法区分：每百万输入 $5、输出 $30、缓存读取 $0.5 完全相同，同为 1050000 token 上下文和 128000 token 最大输出，同样是文本加图像进、文本出，同样的对话、视觉、代码、工具和推理标志，思考同样可选，同为 2026-07-09 发布、2026-02 知识截止。同类负载选哪个都行，把它们当作可互换；按目录之外的因素来定，比如可用性，或者你的技术栈已经指向哪个 id。
Benchmark 成绩
GPT-5.6：供应商没有公布 benchmark 成绩。
供应商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
价格
| GPT-5.6 | GPT-5.6 Sol | Δ | |
|---|---|---|---|
| 输入 / 1M token | $5 | $5 | = |
| 输出 / 1M token | $30 | $30 | = |
| 缓存读取 / 1M token | $0.5 | $0.5 | = |
| 缓存写入 | 不单独收费 | 不单独收费 | - |
价格取自构建时的实时目录，最新价格见各模型页面。
两个模型所处的位置：全部 76 个按同一单位计费的聊天模型的每 1M token 输入价分布（对数刻度）
能力
| GPT-5.6 | GPT-5.6 Sol | |
|---|---|---|
| 工具调用 | 是 | 是 |
| 思考控制 | 可配置 | 可配置 |
| 结构化输出 | 是 | 是 |
| 提示词缓存 | 隐式（自动） | 隐式（自动） |
| 缓存有效期 | 5-10m, up to 1h | 5-10m, up to 1h |
| 最小缓存前缀 | 1024 个 token | 1024 个 token |
规格
| GPT-5.6 | GPT-5.6 Sol | |
|---|---|---|
| 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 | 2026-07-09 |
| 知识截止日期 | 2026-02 | 2026-02 |
| 上下文窗口 | 1.1M | 1.1M |
| 最大输出 | 128K | 128K |
| 思考参数 | reasoning.effort | reasoning.effort |
| 可选值 | reasoning.effort
| reasoning.effort
|
| 默认值 | medium | medium |
规格照录自各供应商的文档；供应商没有公布的项目，对应的行直接省略，不做推断。 完整来源： GPT-5.6 · GPT-5.6 Sol
同一条提示词，两个模型，经网关实测
GPT-5.6 通过 · 3 sentences
Enabling reasoning makes the model generate many additional internal tokens before producing an answer, so each request consumes substantially more compute. Those reasoning tokens are billed even though they are not visible to users, and longer processing can also increase tool calls, retries, and output length. The bill doubled because our cost per request rose, not necessarily because usage volume increased; we can control it by limiting reasoning effort and routing simpler tasks to cheaper models.
输出 126 tok (+30 思考) 延迟 3.0 s
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
指令遵循（恰好三句，数得出来）、受众适配（对 CFO 说话的口吻），以及下方 token 计数暴露出的隐藏思考计费差额。
GPT-5.6 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: best = max(best, cur) cur = 1 return max(best, cur) ``` The original code counted equal *transitions* rather than items, so a run of one had length `0`, a run of three had length `2`, and tracking from zero caused the off-by-one error. The corrected version starts each run at length `1`, resets to `1` when the value changes, and performs a final `max` after the loop so a run ending at the last element is included; an empty input still returns `0`.
输出 277 tok (+93 思考) 延迟 4.2 s
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
修复是否真的正确（能运行）、解释的信息密度，以及在一个范围明确的任务上的 token 效率。
GPT-5.6 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 179 tok (+118 思考) 延迟 3.7 s
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
是否严守 schema（不臆造字段）、能否顶住幻觉压力（原文明说暂不给出 guidance），以及结构化输出路径的差异。
GPT-5.6 通过 · 120 words, 0 banned, 1 question
Today we are introducing Prompt Cache, a new API gateway feature that stores reusable prompt prefixes and serves them across supported model providers. By recognizing identical system instructions, tools, examples, and context blocks, Prompt Cache reduces repeated input processing, lowers latency, and helps control token costs without changing application code. Teams can configure retention windows, cache keys, provider routing, encryption, and regional storage policies from one dashboard or API. What happens when a preferred provider is unavailable? The gateway can route requests to another provider while reusing eligible cached content, preserving performance and consistency. Built-in metrics report hit rates, savings, latency, and provider usage, while audit logs support governance. Prompt Cache is available today in public preview for all customers.
输出 628 tok (+473 思考) 延迟 7.3 s
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
是否守住约束（字数预算、禁用词表、只能有一个问句）、文风特征，以及长度控制。
改一行代码就能在两个模型之间切换
下方每个标签页里都有两个模型 ID，高亮的那两行是唯一要改的地方。端点不变，API key 不变，请求结构也不变。
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",
# model="gpt-5.6-sol", # 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-5.6",
// model: "gpt-5.6-sol", // 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-5.6",
# "model": "gpt-5.6-sol", # 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-5.6",
// Model: "gpt-5.6-sol", // 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-5.6")
// .model("gpt-5.6-sol") // 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-5.6 和 GPT-5.6 Sol 哪个更便宜？
两个模型的「输入 / 1M token」价格相同（$5），所以这一项上价格分不出高下，请看下方的规格与能力。
不用分别集成两次，就能对 GPT-5.6 和 GPT-5.6 Sol 做 A/B 测试吗？
可以。两个模型走同一个 OpenAI 兼容端点，用同一个 API key，切换时只要改一行里的模型名，所以可以给两个模型各分一部分流量，直接对比账单。
GPT-5.6 和 GPT-5.6 Sol 支持提示词缓存吗？
支持。两个模型的缓存读取价都低于各自的输入价，所以前缀能反复命中缓存的负载，实际成本会比按官网价估算的低。具体的缓存读取价见上方价格表。