Dola Seed 2.0 Pro vs DeepSeek V4 Pro
何时使用哪一个 — 经过整理的结论，而非基准测试表格
当输入包含图像或视频时选 Dola-Seed-2.0-pro，因为它接受文本、图像和视频而 deepseek-v4-pro 只处理文本，且它 $0.5 的输入费率比 deepseek-v4-pro 每百万 $1.608 便宜约 3.2 倍。超长单次任务选 deepseek-v4-pro：1000000 token 上下文和 393216 token 最大输出，对比 256000 和 131072；重放大提示词时，$0.0134 的缓存读取比 $0.1 便宜约 7.5 倍。输出价格接近（$3 对 $3.216），两者都做对话、代码、推理和工具，思考都可关闭。
定价
| Dola Seed 2.0 Pro | DeepSeek V4 Pro | Δ | |
|---|---|---|---|
| 输入 / 1M tokens | $0.5 | $1.608 | 0.31× |
| 输出 / 1M tokens | $3 | $3.216 | 0.93× |
| 缓存读取 / 1M tokens | $0.1 | $0.0134 | 7.5× |
| 缓存写入 | — | 不单独收费 | — |
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 63 个 聊天 模型的 每 1M token 的输入价格（对数刻度）
能力
| Dola Seed 2.0 Pro | DeepSeek V4 Pro | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可配置 | 可配置 |
| 结构化输出 | — | 是 |
| 提示词缓存 | 隐式 + 显式 | 隐式（自动） |
| 缓存生存时间 | 未公开 | no fixed TTL (evicted when unused) |
| 最小缓存前缀 | 1024 个 token | 未公开 |
规格
| Dola Seed 2.0 Pro | DeepSeek V4 Pro | |
|---|---|---|
| 输入模态 | 文本 图像 视频 | 文本 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-02-14 | 2026-04-24 |
| 上下文窗口 | 256K | 1M |
| 最大输出 | 131K | 393K |
| 思考参数 |
|
|
| 允许的值 | thinking.type
reasoning_effort
| thinking.type
reasoning_effort
|
| 默认值 | enabled, with reasoning_effort medium and thinking summary on | enabled, with reasoning_effort high some complex agent requests are automatically set to max |
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： Dola Seed 2.0 Pro · DeepSeek V4 Pro
单个 Prompt，两个模型 —— 通过网关实测
Dola Seed 2.0 Pro 通过 · 3 sentences
Reasoning-enabled LLMs do not generate final answers in one pass: they run hundreds of unshown internal trial, cross-check, and correction steps for every user request, and every one of these hidden steps consumes billable GPU time just like the visible output text. Unlike our prior standard chat workload which only processed exactly what users typed, reasoning mode automatically executes these validation loops by default, with no visible indicator to end users that extra compute is being consumed. This 2x increase is not a billing error or rogue usage spike: industry benchmarks consistently put reasoning workloads at 1.7-2.6x the compute cost of equivalent standard LLM requests, and this pricing dynamic is almost never clearly disclosed on vendor rate cards.
输出 563 tok (+416 思考) 延迟 9.4 s
DeepSeek V4 Pro 通过 · 3 sentences
Enabling reasoning causes the model to generate a lengthy internal chain‑of‑thought before it delivers a visible answer, and we are billed for every one of those hidden reasoning tokens just as we are for the final output. In most interactions, the volume of these unseen tokens at least equals the visible response, effectively doubling the total tokens consumed per query. That doubled token consumption translates directly into a doubled per‑query cost, which is why our overall LLM bill doubled.
输出 641 tok (+545 思考) 延迟 11.5 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。
Dola Seed 2.0 Pro 通过 · 8/8 cases
### Corrected Implementation ```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 ``` --- ### Bug Explanation The original code has three critical linked flaws: 1. It initialized run counters `best` and `cur` to `0` instead of `1`, failing to account for the fact that *every individual element is already a valid run of length 1* -- this causes single-element inputs to incorrectly return `0`. 2. `cur` only counted how many extra matching elements followed the start of a run, so it always tracked one less than the actual run length, and it reset to `0` (instead of `1`) when a new run began, preserving this off-by-one error for all runs. 3. […]
输出 1278 tok (+951 思考) 延迟 16.7 s
DeepSeek V4 Pro 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 best = max(best, cur) else: cur = 1 return best ``` **Explanation:** The bug stems from initializing `cur = 0` and only incrementing it when two adjacent elements match, which counts the number of *additional* equal elements rather than the actual run length (which is `cur + 1`). As a result, the returned `best` is always one less than the true longest run—most obviously returning 0 for a single-element list instead of 1. […]
输出 2418 tok (+2131 思考) 延迟 35.6 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。
Dola Seed 2.0 Pro 通过 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
输出 506 tok (+452 思考) 延迟 8.6 s
DeepSeek V4 Pro 通过 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null }
输出 1204 tok (+1153 思考) 延迟 19.9 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。
Dola Seed 2.0 Pro 通过 · 123 words, 0 banned, 1 question
Today we’re rolling out cross-provider prompt caching for our API gateway. For engineering teams routing LLM requests across OpenAI, Anthropic, Mistral and open source models, this feature stores identical prompt payloads at the gateway layer, rather than relying on per-provider cache implementations limited to single endpoints. How much time and compute could your team save by avoiding redundant token processing for repeated system prompts, context windows, or common user queries? Cache hits return responses in under 10ms, with configurable TTL, granular purge controls, and per-application cache partitioning. Early access teams running support bots, batch inference and internal assistants recorded 42-67% lower LLM spend. This feature is live for all gateway users today, with no required code changes to existing routing workflows. (120 words)
输出 1041 tok (+872 思考) 延迟 11.4 s
DeepSeek V4 Pro 没有可供评分的回答 · no answer text within 16,384 tokens (all of it went to thinking)
模型未返回回答文本 —— 整个 token 预算都用在了隐藏思考上。
输出 8193 tok (+8192 思考) 延迟 106.2 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="Dola-Seed-2.0-pro",
# model="deepseek-v4-pro", # 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: "Dola-Seed-2.0-pro",
// model: "deepseek-v4-pro", // 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": "Dola-Seed-2.0-pro",
# "model": "deepseek-v4-pro", # 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: "Dola-Seed-2.0-pro",
// Model: "deepseek-v4-pro", // 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("Dola-Seed-2.0-pro")
// .model("deepseek-v4-pro") // 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
Dola Seed 2.0 Pro 和 DeepSeek V4 Pro 哪个更便宜？
在 输入 / 1m tokens 方面，Dola Seed 2.0 Pro 更便宜（$0.5 对比 $1.608，相差 3.2×）。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 Dola Seed 2.0 Pro 和 DeepSeek V4 Pro 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。
Dola Seed 2.0 Pro 和 DeepSeek V4 Pro 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。