Dola Seed 2.0 Lite vs Gemini 3.8 Flash
什么时候选哪个
两者都接受文本、图像、音频和视频并返回文本，因此真正的差异在于价格与上下文：Dola-Seed-2.0-lite 的计费为输入 $0.25 和输出 $2，而 gemini-3.8-flash 为 $0.75 和 $3.75，输入和输出大约分别便宜 3x 和 1.9x，并且它允许更长的 131072 token 回复。当你需要 1048576 token 窗口（是 Dola-Seed-2.0-lite 256000 的 4x）或其声明的 reasoning 和 vision 标志时，请选择 gemini-3.8-flash；对于大批量多模态批处理请求，请选择 Dola-Seed-2.0-lite，在这种情况下它的思考功能也可以被关闭。
Benchmark 成绩
供应商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
价格
| Dola Seed 2.0 Lite | Gemini 3.8 Flash | Δ | |
|---|---|---|---|
| 输入 / 1M token | $0.25 | $0.75 | 0.33× |
| 输出 / 1M token | $2 | $3.75 | 0.53× |
| 缓存读取 / 1M token | $0.05 | $0.075 | 0.67× |
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 77 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
| Dola Seed 2.0 Lite | Gemini 3.8 Flash | |
|---|---|---|
| 工具调用 | 是 | 是 |
| 思考控制 | 可配置 | 是，但供应商未公布调节参数 |
| 结构化输出 | 是 | 是 |
| 提示词缓存 | 隐式 + 显式 | 隐式 + 显式 |
| 缓存有效期 | 未公布 | 未公布 |
| 最小缓存前缀 | 1024 个 token | 4096 个 token |
规格
| Dola Seed 2.0 Lite | Gemini 3.8 Flash | |
|---|---|---|
| 输入模态 | 文本 图像 音频 视频 | 文本 图像 音频 视频 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-02-14 | 2026-09-02 |
| 知识截止日期 | - | 2026-03 |
| 上下文窗口 | 256K | 1M |
| 最大输出 | 131K | 66K |
| 思考参数 |
| - |
| 可选值 | thinking.type
reasoning_effort
| - |
| 默认值 | enabled, with reasoning_effort 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： Dola Seed 2.0 Lite · Gemini 3.8 Flash
同一条提示词，两个模型，经网关实测
Dola Seed 2.0 Lite 通过 · 3 sentences
Enabling reasoning on our production LLM requires the model to run resource-intensive multi-step chain-of-thought inference for every customer-facing query, rather than the single-pass inference that powered our original non-reasoning deployment, and GPU compute—which makes up 90% of our total LLM bill—scales directly with the number of compute cycles each query consumes. Our cloud provider’s LLM billing model charges per million processed tokens, and reasoning workflows generate an average of 2.1x more hidden processing tokens (the intermediate problem-solving steps the model never surfaces to end users) that are still billable, pushing our total monthly token consumption just over double what it was prior to enabling the feature. […]
输出 1306 tok (+1096 思考) 延迟 18.2 s
Gemini 3.8 Flash 通过 · 3 sentences
When we enabled reasoning, the model began generating hidden "thinking tokens" to work through logic step-by-step before producing a final answer. Because AI vendors bill for every single token processed—visible or not—this internal deliberation dramatically inflated our billable volume per query. In short, while our total number of user requests remained flat, the cost per transaction doubled to buy higher accuracy on complex tasks.
输出 705 tok (+624 思考) 延迟 6.4 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。
Dola Seed 2.0 Lite 通过 · 8/8 cases
### Corrected Function ```python def longest_run(xs): if not xs: # Handle empty input edge case return 0 best = cur = 1 # Base run length of 1 for any non-empty single element for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 # Reset run length for the new, different element return best ``` ### Bug Explanation (single paragraph) The original function’s core flaw was that it counted the number of consecutive equal adjacent element pairs, rather than the number of elements in the run itself, leading to all results being off by one, which caused both reported issues: a run of k equal elements only has k-1 consecutive matching pairs, so the original function’s maximum `best` value was always one less than the true element count of the longest run. […]
输出 2620 tok (+2217 思考) 延迟 33.2 s
Gemini 3.8 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 best = max(best, cur) else: cur = 1 return best ``` ### Explanation of the Bug The original function counted equal-neighbor transitions rather than the actual items making up each run. Because `cur` started at `0` (and was reset to `0` on mismatches), every run was undercounted by one: single-element lists returned `0` because the loop never executed, and any subsequent run—including those ending at the last index—recorded a count of $N - 1$ instead of $N$. The fix handles empty input upfront, initializes both `cur` and `best` to `1` (since any single element forms a run of length 1), and resets `cur` to `1` whenever a new run begins.
输出 1393 tok (+1149 思考) 延迟 10.1 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。
Dola Seed 2.0 Lite 通过 · 5/5 fields, guidance "Formal guidance withheld pending Q4 clos"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Formal guidance withheld pending Q4 close" }
输出 1502 tok (+1439 思考) 延迟 20.2 s
Gemini 3.8 Flash 通过 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": null } ```
输出 3989 tok (+3932 思考) 延迟 30.4 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。
Dola Seed 2.0 Lite 未通过 · 141 words, 0 banned, 1 question
Today we launch Cross-Provider Prompt Cache, a new feature for our Core API Gateway built to cut redundant AI costs and latency for teams routing requests across multiple LLM providers. Many development teams rotate between OpenAI, Anthropic, and Google Gemini to balance performance, cost, and capability, but identical prompts get reprocessed from scratch with every provider switch, wasting tokens and slowing response times. This feature stores validated prompt responses at the gateway layer, so repeat requests pull from cache regardless of which provider they route to, with configurable TTLs and built-in compliance with all major provider data policies. […]
输出 1870 tok (+1695 思考) 延迟 23.1 s
Gemini 3.8 Flash 通过 · 120 words, 0 banned, 1 question
Why pay multiple model providers for the exact same output? Introducing Universal Prompt Cache, our latest API gateway capability engineered to cut compute expenses and drop inference latency. When your application makes a call, the gateway inspects a central memory layer before routing traffic to external LLMs. If an identical query was previously processed by OpenAI, Anthropic, or Mistral, our gateway returns that response immediately. This shared cache eliminates duplicate token fees and insulates your production apps from vendor rate limits or unexpected downtime. Developers can easily customize expiration settings, enforce strict data privacy controls, and configure invalidation logic across every endpoint. Stop wasting your budget on repeated queries. Enable prompt caching in your dashboard to accelerate your pipeline today.
输出 3833 tok (+3688 思考) 延迟 21.8 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="Dola-Seed-2.0-lite",
# model="gemini-3.8-flash", # 取消注释此行，注释上一行
messages=[{"role": "user", "content": "Summarize this diff"}],
)
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: "Dola-Seed-2.0-lite",
// model: "gemini-3.8-flash", // 取消注释此行，注释上一行
messages: [{ role: "user", content: "Summarize this diff" }],
});
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": "Dola-Seed-2.0-lite",
# "model": "gemini-3.8-flash", # 取消注释此行，注释上一行
"messages": [{"role": "user", "content": "Hello"}]
}'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: "Dola-Seed-2.0-lite",
// Model: "gemini-3.8-flash", // 取消注释此行，注释上一行
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("Dola-Seed-2.0-lite")
// .model("gemini-3.8-flash") // 取消注释此行，注释上一行
.addUserMessage("Summarize this diff")
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常见问题
Dola Seed 2.0 Lite 和 Gemini 3.8 Flash 哪个更便宜？
按「输入 / 1M token」算，Dola Seed 2.0 Lite 更便宜（$0.25 对 $0.75，相差 3.0×）。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 Dola Seed 2.0 Lite 和 Gemini 3.8 Flash 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。
Dola Seed 2.0 Lite 和 Gemini 3.8 Flash 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。