Gemini 3.7 Flash vs GPT-5.6 Sol
什么时候选哪个
两款模型的上下文几乎相同（gemini-3.7-flash 为 1048576，gpt-5.6-sol 为 1050000），对话、视觉、代码、工具和推理标志也一致，所以真正的差别是价格、输入输出和输出长度。大批量工作或音频、视频输入选 gemini-3.7-flash：输入 $0.75、输出 $3.75，比 gpt-5.6-sol 的 $5 和 $30 输入便宜约 6.7 倍、输出便宜约 8 倍。需要单次回复最多 128000 输出 token，或需要关闭思考的能力，且文本加图像输入就够时选 gpt-5.6-sol。
Benchmark 成绩
20 项两边都有成绩，其中 1 项打平。
供应商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
价格
| Gemini 3.7 Flash | GPT-5.6 Sol | Δ | |
|---|---|---|---|
| 输入 / 1M token | $0.75 | $5 | 0.15× |
| 输出 / 1M token | $3.75 | $30 | 0.13× |
| 缓存读取 / 1M token | $0.075 | $0.5 | 0.15× |
| 缓存写入 | - | 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
| Gemini 3.7 Flash | GPT-5.6 Sol | |
|---|---|---|
| 工具调用 | 是 | 是 |
| 思考控制 | 是，但供应商未公布调节参数 | 可配置 |
| 结构化输出 | 是 | 是 |
| 提示词缓存 | 隐式 + 显式 | 隐式（自动） |
| 缓存有效期 | 未公布 | 5-10m, up to 1h |
| 最小缓存前缀 | 4096 个 token | 1024 个 token |
规格
| Gemini 3.7 Flash | GPT-5.6 Sol | |
|---|---|---|
| 输入模态 | 文本 图像 音频 视频 | 文本 图像 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-08-13 | 2026-07-09 |
| 知识截止日期 | 2026-03 | 2026-02 |
| 上下文窗口 | 1M | 1.1M |
| 最大输出 | 66K | 128K |
| 思考参数 | - | reasoning.effort |
| 可选值 | - | reasoning.effort
|
| 默认值 | - | 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： Gemini 3.7 Flash · GPT-5.6 Sol
同一条提示词，两个模型，经网关实测
Gemini 3.7 Flash 通过 · 3 sentences
Enabling reasoning causes the model to generate a high volume of invisible "thinking tokens" behind the scenes to deliberate and solve complex logic before delivering a final response. Because our vendor bills strictly on token volume, this additional internal computation effectively doubled our billable output per query. In short, our costs increased because we are now paying for the extra compute capacity required to produce significantly higher-accuracy answers.
输出 878 tok (+799 思考) 延迟 6.9 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。
Gemini 3.7 Flash 通过 · 8/8 cases
Here is the corrected function: ```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 ``` ### Explanation of the Bug The original function tracked equal adjacent transitions instead of the actual number of elements in a run, causing all non-empty run lengths to be undercounted by one (e.g., returning `0` instead of `1` for single-element lists because the loop never executes, and `1` instead of `2` for pairs). Initializing both `best` and `cur` to `1` (after handling empty inputs) and resetting `cur` to `1` on a mismatch ensures every individual element is counted as a valid run of at least length one.
输出 1050 tok (+827 思考) 延迟 6.4 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。
Gemini 3.7 Flash 通过 · 5/5 fields, guidance "withheld"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld" } ```
输出 918 tok (+858 思考) 延迟 6.2 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。
Gemini 3.7 Flash 通过 · 120 words, 0 banned, 1 question
Why pay twice for identical AI queries simply because you routed them to different model vendors? Today, we introduce Universal Prompt Caching directly within our unified API gateway architecture. This capability stores repeated prompt contexts across OpenAI, Anthropic, and local models, instantly returning stored results to eliminate redundant computation fees. When your application sends an LLM request, the gateway inspects the payload, identifies semantic matches, and returns accurate cached responses in under ten milliseconds. Engineering teams can now slash inference latency by eighty percent while dramatically reducing monthly token expenditures across diverse production deployments. You retain complete privacy control, flexible cache eviction policies, and granular metrics through a single dashboard. Update your routing settings today to accelerate overall system performance.
输出 2858 tok (+2718 思考) 延迟 14.1 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="gemini-3.7-flash",
# 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: "gemini-3.7-flash",
// 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": "gemini-3.7-flash",
# "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: "gemini-3.7-flash",
// 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("gemini-3.7-flash")
// .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
Gemini 3.7 Flash 和 GPT-5.6 Sol 哪个更便宜？
按「输入 / 1M token」算，Gemini 3.7 Flash 更便宜（$0.75 对 $5，相差 6.7×）。其他计费项的结论可能相反，完整价格见上方表格，实际成本取决于你的用量构成。
不用分别集成两次，就能对 Gemini 3.7 Flash 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。
Gemini 3.7 Flash 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。