Dola Seed 2.0 Lite vs Gemini 3.7 Flash
什么时候选哪个
两者都接受文本、图像、音频和视频并输出文本，所以差别其实在规模和价格：Dola-Seed-2.0-lite 每百万输入 $0.25、输出 $2，上下文 256000、最多输出 131072 token；gemini-3.7-flash 输入贵 3 倍、输出贵 1.875 倍（$0.75 和 $3.75），换来 4 倍大的 1048576 token 上下文。低成本大批量对话、代码和工具调用、超长生成，或想关闭思考时选 Dola-Seed-2.0-lite；单个提示词必须装下超大语料，或需要它的推理与视觉标志时选 gemini-3.7-flash。
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 3.7 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 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
| Dola Seed 2.0 Lite | Gemini 3.7 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 3.7 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 | 2026-08-13 |
| 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 3.7 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 3.7 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
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 3.7 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
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 3.7 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
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 3.7 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
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-3.7-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-3.7-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-3.7-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-3.7-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-3.7-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 3.7 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 3.7 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 3.7 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。