Claude Opus 5 vs DeepSeek V4 Pro
何時該用哪一個 — 綜合評斷,而非基準測試數據表
兩者共享 1000000 token 脈絡且都能關閉思考,所以差別在模態和價目表:claude-opus-5 在文字之外接受影像輸入並帶明確的思考能力,而 deepseek-v4-pro 只處理文字,但輸入便宜約 3.1 倍($1.608 對 $5)、輸出便宜約 7.8 倍($3.216 對 $25)、快取讀取便宜約 37 倍($0.0134 對 $0.5)。當提示詞裡包含截圖或圖表時選 claude-opus-5;大批量文字和程式碼工作選 deepseek-v4-pro,它 393216 token 的最大輸出也超過 128000 的上限。
定價
| Claude Opus 5 | DeepSeek V4 Pro | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $5 | $1.608 | 3.1× |
| 輸出 / 1M tokens | $25 | $3.216 | 7.8× |
| 快取讀取 / 1M tokens | $0.5 | $0.0134 | 37× |
| 快取寫入 | 1.25x (5m) / 2x (1h) | 不額外計費 | — |
費率取自建置時的即時目錄;各模型頁面皆附有目前的費率卡。
它們的相對位置 — 在此計費單位下,所有 63 個 聊天 模型的 每 1M tokens 的輸入價格(對數尺度)
能力
| Claude Opus 5 | DeepSeek V4 Pro | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可配置 | 可配置 |
| 結構化輸出 | 是 | 是 |
| 提示快取 | 顯式(由您標記前綴) | 隱式(自動) |
| 快取生命週期 | 5m default, 1h option | no fixed TTL (evicted when unused) |
| 最小快取前綴 | 1024 個 token | 未公開 |
規格
| Claude Opus 5 | DeepSeek V4 Pro | |
|---|---|---|
| 輸入模態 | 文字 影像 | 文字 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-07-24 | 2026-04-24 |
| 知識截止日期 | 2026-05 | — |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 128K | 393K |
| 思考參數 |
|
|
| 可接受的值 | thinking.type
effort
| thinking.type
reasoning_effort
|
| 預設值 | thinking on effort
| enabled, with reasoning_effort high some complex agent requests are automatically set to max |
規格摘錄自各供應商的文件;供應商未發布的資料列會直接省略,而非自行推測。 完整來源: Claude Opus 5 · DeepSeek V4 Pro
單一提示詞,兩款模型 — 經由閘道測量
Claude Opus 5 通過 · 3 sentences
When reasoning is enabled, the model generates a hidden chain of "thinking" tokens before it writes the visible answer, and those internal tokens are billed at the output rate even though no one ever sees them — commonly 3–10x more tokens than the response itself. Because output tokens are priced several times higher than input tokens, that added volume hits us at our most expensive rate, which is why the bill roughly doubled while our request count stayed flat. The spend is controllable: we can cap the reasoning budget per call, route only genuinely hard requests to reasoning mode and cheaper models for the rest, and track cost per successfully completed task rather than cost per token, since a single correct answer usually beats three cheap retries plus human cleanup.
輸出 529 tok 延遲 9.0 s
DeepSeek V4 Pro 通過 · 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.
輸出 641 tok (+545 思考) 延遲 11.5 s
指令遵循(恰好三句,可數)、受眾適配(面向 CFO 的語氣),以及下方 token 計量所暴露的隱藏思考計費缺口。
Claude Opus 5 通過 · 8/8 cases
## Fixed version ```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 if cur > best: best = cur return best ``` ## The bug The counters were tracking the wrong quantity: `cur` counted *matching adjacent pairs* rather than *elements in the current run*, and both counters started at `0` instead of `1`. Since a run of length *n* contains only *n − 1* adjacent equal pairs, every result came back one too small — `[1, 1]` returned 1, `[3, 3, 3]` returned 2 — and a single-element list returned 0 because the loop body never executed and `best` kept its initial value of `0`. […]
輸出 1094 tok 延遲 16.1 s
DeepSeek V4 Pro 通過 · 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. […]
輸出 2418 tok (+2131 思考) 延遲 35.6 s
修復是否真的正確(可執行)、解釋的資訊密度,以及在一個邊界明確的任務上的 token 效率。
Claude Opus 5 通過 · 5/5 fields, guidance "Withheld pending Q4 close"
```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` Notes on judgment calls: - **quarter**: "September quarter" is labeled Q3 because the text refers to an upcoming "Q4 close," implying a calendar-aligned fiscal year. No fiscal year is given. - **revenue_usd_m**: 412 is stated as constant-currency and hedged ("call it"), so it's approximate; the currency is not explicitly stated as USD. - **growth_pct**: "up eleven and change" means slightly more than 11, so 11 is a floor rather than an exact figure.
輸出 539 tok 延遲 7.6 s
DeepSeek V4 Pro 通過 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null }
輸出 1204 tok (+1153 思考) 延遲 19.9 s
schema 服從度(不臆造欄位)、幻覺壓力(guidance 明確被暫緩給出),以及結構化輸出路徑的差異。
Claude Opus 5 通過 · 123 words, 0 banned, 1 question
**Cross-Provider Prompt Caching** We're introducing Cross-Provider Prompt Caching in the API Gateway. Repeated system prompts, long documents, and few-shot examples are stored once at the gateway layer and reused across OpenAI, Anthropic, Google, and self-hosted models. Instead of paying full input token costs on every request, your application sends a cache reference, and the gateway rehydrates the context before forwarding it downstream. Why does that matter? Teams running high-volume agents and retrieval pipelines typically see input token spend fall 40 to 70 percent, with median latency dropping by several hundred milliseconds. Caches are scoped per project, encrypted at rest, and invalidated automatically when a prompt template changes. Enable it with a single header, and see the docs for TTL tuning and per-route controls.
輸出 1593 tok 延遲 19.1 s
DeepSeek V4 Pro 沒有可評分的回答 · no answer text within 16,384 tokens (all of it went to thinking)
模型未回傳任何回答文字——所有 token 額度皆耗費於隱藏思考。
輸出 8193 tok (+8192 思考) 延遲 106.2 s
約束服從度(字數預算、禁用詞表、唯一的那句問句)、文風指紋,以及長度控制。
只需一行程式碼即可在兩者間切換
以下每個頁籤中都有這兩個 ID — 醒目提示的這兩行是唯一的修改處。相同的端點,相同的金鑰,相同的請求結構。
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="claude-opus-5",
# model="deepseek-v4-pro", # 取消註解此行,並註解上一行
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: "claude-opus-5",
// model: "deepseek-v4-pro", // 取消註解此行,並註解上一行
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": "claude-opus-5",
# "model": "deepseek-v4-pro", # 取消註解此行,並註解上一行
"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: "claude-opus-5",
// Model: "deepseek-v4-pro", // 取消註解此行,並註解上一行
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("claude-opus-5")
// .model("deepseek-v4-pro") // 取消註解此行,並註解上一行
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常見問題
Claude Opus 5 和 DeepSeek V4 Pro 哪個比較便宜?
DeepSeek V4 Pro 在 輸入 / 1m tokens 上較便宜($1.608 對比 $5,相差 3.1×)。其他項目可能呈現相反結果 — 上表提供完整資訊,實際成本取決於您的使用組合。
我可以在不進行兩次整合的情況下,對 Claude Opus 5 和 DeepSeek V4 Pro 進行 A/B 測試嗎?
可以。兩者皆透過同一個相容 OpenAI 的端點提供服務,並使用同一把 API 金鑰 — 切換只需更改一行的模型字串,因此您可以將部分流量分別導向兩者並直接比較帳單。
Claude Opus 5 與 DeepSeek V4 Pro 支援提示快取嗎?
是的 — 兩者的快取讀取費率皆低於其輸入費率,因此具有暖前綴的工作負載成本會低於牌價所示。確切的快取讀取列請見上方的定價表。