Dola Seed 2.0 Lite vs Claude Opus 5.5
什么时候选哪个
Dola-Seed-2.0-lite 是更便宜、输入更广泛的选项，每百万 token 输入 $0.25，输出 $2，接受文本、图像、视频和音频，具有 256000 token 窗口和关闭思考的能力。claude-opus-5-5 的输入成本高 16 倍（$4），输出成本高 10 倍（$20），且仅接受文本和图像，但它提供了 1000000 token 上下文，外加无法禁用的显式思考和推理标志。对于大批量或多模态提取工作，且您希望控制深思过程时，请选择 Dola 模型；当您需要超长上下文和始终开启的推理时，请选择 claude-opus-5-5。
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 | Claude Opus 5.5 | Δ | |
|---|---|---|---|
| 输入 / 1M token | $0.25 | $4 | 0.063× |
| 输出 / 1M token | $2 | $20 | 0.1× |
| 缓存读取 / 1M token | $0.05 | $0.2 | 0.25× |
| 缓存写入 | - | 1.25x (5m) / 2x (1h) | - |
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 | Claude Opus 5.5 | |
|---|---|---|
| 工具调用 | 是 | 是 |
| 思考控制 | 可配置 | 始终开启 |
| 结构化输出 | 是 | 是 |
| 提示词缓存 | 隐式 + 显式 | 显式（由你标记前缀） |
| 缓存有效期 | 未公布 | 5m default, 1h option |
| 最小缓存前缀 | 1024 个 token | 1024 个 token |
规格
| Dola Seed 2.0 Lite | Claude Opus 5.5 | |
|---|---|---|
| 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 | 2026-02-14 | 2026-09-22 |
| 知识截止日期 | - | 2026-06 |
| 上下文窗口 | 256K | 1M |
| 最大输出 | 131K | 128K |
| 思考参数 |
| output_config.effort |
| 可选值 | thinking.type
reasoning_effort
| output_config.effort
|
| 默认值 | enabled, with reasoning_effort 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： Dola Seed 2.0 Lite · Claude Opus 5.5
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
Claude Opus 5.5 通过 · 3 sentences
When we turned on reasoning, the model began "thinking out loud" before each answer, generating thousands of internal working tokens that users never see but that we are billed for just like the final response. Those hidden tokens are charged at the output rate, typically the most expensive rate, so the same question with the same visible answer now costs roughly twice as much or more. We can bring the bill back down without losing the quality gains by capping the reasoning budget and reserving reasoning mode for the complex tasks that actually benefit from it.
输出 263 tok (+102 思考) 延迟 5.7 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
Claude Opus 5.5 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 original code counted *matching adjacent pairs* rather than *items in a run*. Both `cur` and `best` started at 0, and `cur` reset to 0 on a mismatch, so a run of length *n* was reported as *n − 1*. For a single-element list the loop never executes, so it returned 0 instead of 1. For `[5, 5]` it returned 1 instead of 2. The fix treats every element as the start of a run of length 1: `best` and `cur` start at 1, and `cur` resets to 1 (not 0) when the value changes. An explicit check returns 0 for an empty list. The "misses runs at the last index" reports are the same off-by-one bug rather than a separate one. […]
输出 723 tok (+303 思考) 延迟 10.4 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
Claude Opus 5.5 通过 · 5/5 fields, guidance "Withheld pending Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` **Judgment calls to check:** - **quarter**: Kept as "September quarter" rather than "Q3", since the fiscal calendar isn't stated. The text mentions an upcoming "Q4 close," so it may be fiscal Q3, but that's not certain. - **revenue_usd_m**: The text says only "412 for the topline." It doesn't state currency or units, and the figure is on a constant-currency basis, not reported USD. USD millions is an assumption. Use `null` if you need strictly stated values. - **growth_pct**: The source says "eleven and change," so the true figure is slightly above 11. 11 is an approximation. - **guidance**: Guidance was explicitly withheld. I recorded that rather than `null`, since the text addresses it directly.
输出 829 tok (+488 思考) 延迟 10.9 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
Claude Opus 5.5 通过 · 120 words, 0 banned, 1 question
Today we're launching Cross-Provider Prompt Caching for our API gateway. Why pay twice for the same context? Our gateway now stores repeated prompt prefixes, system instructions, and document chunks, then reuses them whether your request goes to OpenAI, Anthropic, Google, or an open-source model. Teams running retrieval pipelines or long agent loops can cut token costs by up to 60 percent and trim latency on every cached call. Setup takes one configuration flag; no code changes are required. Cache rules respect tenant boundaries, expire on your schedule, and log every hit for audit review. Dashboards show savings by provider, route, and application in real time. Cross-Provider Prompt Caching is available on all Pro and Enterprise plans. Turn it on today.
输出 1263 tok (+1007 思考) 延迟 13.5 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="claude-opus-5-5", # 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: "claude-opus-5-5", // 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": "claude-opus-5-5", # 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: "claude-opus-5-5", // 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("claude-opus-5-5") // 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 Claude Opus 5.5 哪个更便宜？
按「输入 / 1M token」算，Dola Seed 2.0 Lite 更便宜（$0.25 对 $4，相差 16×）。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 Claude Opus 5.5 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 Claude Opus 5.5 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。