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Claude Opus 5 vs DeepSeek V4 Flash (0731)

vs

什么时候选哪个

两者均共享 1,000,000-token 的上下文窗口,因此区分点在于模态、输出长度和价格:claude-opus-5 接受图像和文本输入,并允许您关闭其思考模式,而 deepseek-v4-flash-0731 仅支持文本,但允许高达 393,216 个输出 token,对比 Opus 5 的 128,000。在费率方面,deepseek-v4-flash-0731 每百万输入和输出的价格分别为 $0.44 和 $1.32,对比对方的 $5 和 $25,大约分别便宜 11x 和 19x。当图像或可切换的思考预算很重要时,请选择 claude-opus-5;对于低成本、大批量的超长文本生成工作,请选择 deepseek-v4-flash-0731。

Benchmark 成绩

领先高于同类均值无更高分Claude Opus 51138 / 468 / 46DeepSeek V4 Flash (0731)010 / 280 / 28

11 项两边都有成绩。

Claude Opus 5 DeepSeek V4 Flash (0731) 其他参测模型 同类均值 ★ 没有模型得分更高
DeepSWE 1.1
68.8%
54.4%
BioMysteryBench hard
49.4%
N/A
OSWorld 2.0
没有模型得分更高 70.6%
N/A
ExploitGym
22.1%
1.8%
HealthBench Professional
59.8%
N/A
Finance Agent v2
58.6%
N/A
Legal Agent Benchmark
6.7%
N/A
GPQA Diamond
93.4%
89.9%
Agents' Last Exam
28.6%
25.2%
LVBench
75.4%
N/A

供应商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

价格

Claude Opus 5 DeepSeek V4 Flash (0731) Δ
输入 / 1M token $5 $0.44 11×
输出 / 1M token $25 $1.32 19×
缓存读取 / 1M token $0.5 $0.044 11×
缓存写入 1.25x (5m) / 2x (1h) 不单独收费 -

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

Claude Opus 5 DeepSeek V4 Flash (0731)
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 显式(由你标记前缀) 隐式(自动)
缓存有效期 5m default, 1h option no fixed TTL (evicted when unused)
最小缓存前缀 1024 个 token 未公布

规格

Claude Opus 5 DeepSeek V4 Flash (0731)
输入模态 文本 图像 文本
输出模态 文本 文本
发布日期 2026-07-24 2026-07-31
知识截止日期 2026-05 -
上下文窗口 1M 1M
最大输出 128K 393K
思考参数
  • thinking.type
  • output_config.effort
-
可选值
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max
-
默认值

thinking on

effort
  • high (Claude API and Claude Code)
-

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: Claude Opus 5 · DeepSeek V4 Flash (0731)

同一条提示词,两个模型,经网关实测

提示词 Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. 检查 恰好 3 句

Claude Opus 5 通过 · 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.

输出 529 tok 延迟 9.0 s

DeepSeek V4 Flash (0731) 通过 · 3 sentences

Enabling reasoning mode prompts the model to generate extensive internal chain-of-thought tokens before answering, often multiplying total token output per query by several times. This directly inflates costs because we are billed per token, and these reasoning tokens are processed at a premium compute tier that carries a higher price per million tokens. However, this spend buys measurably higher accuracy and fewer failed calls, so we are tracking it against specific error-reduction KPIs to ensure the ROI justifies the increase.

输出 365 tok (+266 思考) 延迟 5.1 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。

提示词 This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. 检查 修复通过测试

Claude Opus 5 通过 · 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`. […]

输出 1094 tok 延迟 16.1 s

DeepSeek V4 Flash (0731) 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 ``` The original bug stems from initializing `cur` to 0 and only incrementing it when adjacent elements are equal, which counts the number of *equal transitions* rather than the actual run length—so every run of length L is reported as L−1, and a single‑element run (with no transitions) yields 0. Moreover, because `best` is updated only inside the equality branch, it never accounts for a run that ends at the last element if that run is longer than any previously seen (though the real issue remains the off‑by‑one). […]

输出 1268 tok (+1016 思考) 延迟 14.0 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。

提示词 Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. 检查 合法 JSON,严格符合 schema

Claude Opus 5 通过 · 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.

输出 539 tok 延迟 7.6 s

DeepSeek V4 Flash (0731) 通过 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": null, "guidance": null }

输出 1119 tok (+1072 思考) 延迟 12.3 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。

提示词 Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. 检查 120 词,0 个禁用词

Claude Opus 5 通过 · 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.

输出 1593 tok 延迟 19.1 s

DeepSeek V4 Flash (0731) 未通过 · 141 words, 0 banned, 1 question

**Introducing Prompt Cache for API Gateway** We are pleased to announce Prompt Cache, a new feature that stores and reuses identical prompt prefixes across all connected LLM providers. By automatically recognizing repeated requests, this cache reduces token consumption and lowers latency without altering your application logic. Whether you use OpenAI, Anthropic, or open-source models, the cache operates transparently, ensuring consistent behavior while cutting costs. Developers can configure cache TTL and scoping rules via a simple API call, giving you precise control over data freshness and privacy. This feature also minimizes redundant upstream calls, freeing provider rate limits for more critical workloads. Start caching today through the dashboard or CLI, and watch your operational expenses drop significantly. […]

输出 254 tok (+80 思考) 延迟 4.4 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="claude-opus-5",
    # model="deepseek-v4-flash-0731",  # 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)

获取 API key →

常见问题

Claude Opus 5 和 DeepSeek V4 Flash (0731) 哪个更便宜?

按「输入 / 1M token」算,DeepSeek V4 Flash (0731) 更便宜($0.44 对 $5,相差 11×)。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 Claude Opus 5 和 DeepSeek V4 Flash (0731) 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。

Claude Opus 5 和 DeepSeek V4 Flash (0731) 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。

相关对比

我们的实测研究