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DeepSeek V4 Pro (0813) vs GLM-5.2

vs

什么时候选哪个

两者都有 1,000,000 token 上下文和文本进、文本出画像,支持聊天、代码、推理和工具,所以分野其实是输出长度和价格,而 deepseek-v4-pro-0813 现在两项都领先:输入 $1.32 对 $1.4,输出 $3.96 对 $4.4,都略低于 glm-5.2;缓存读取 $0.132 对 $0.26,便宜约 2 倍;输出上限 393216 token 对 131072。单次回复必须很长或缓存复用很重时选 deepseek-v4-pro-0813。想要长上下文标志或按请求关闭思考时选 glm-5.2。

Benchmark 成绩

领先高于同类均值无更高分DeepSeek V4 Pro (0813)1813 / 211 / 21GLM-5.2025 / 801 / 80

18 项两边都有成绩。

DeepSeek V4 Pro (0813) GLM-5.2 其他参测模型 同类均值 ★ 没有模型得分更高
DeepSWE 1.1
62.7%
46.2%
Cybergym
83.3%
77.2%
GDPval-AA v2 Elo · 1508-1769 据 Z.ai 公布 · 2026-09-04
1590
1508
Harvey Lab-AA
N/A
91%
GPQA Diamond
92.4%
91.2%
Agents' Last Exam
25.7%
23.8%

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

价格

DeepSeek V4 Pro (0813) GLM-5.2 Δ
输入 / 1M token $1.32 $1.4 0.94×
输出 / 1M token $3.96 $4.4 0.9×
缓存读取 / 1M token $0.132 $0.26 0.51×
缓存写入 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)

DeepSeek V4 Pro (0813) · $1.32 GLM-5.2 · $1.4
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

DeepSeek V4 Pro (0813) GLM-5.2
工具调用 是 是
思考控制 始终开启 可配置
结构化输出 是 是
提示词缓存 隐式(自动) 隐式(自动)
缓存有效期 no fixed TTL (evicted when unused) 未公布
最小缓存前缀 未公布 未公布

规格

DeepSeek V4 Pro (0813) GLM-5.2
输入模态 文本 文本
输出模态 文本 文本
发布日期 2026-08-13 2026-06-16
上下文窗口 1M 1M
最大输出 393K 131K
思考参数 reasoning_effort
  • thinking.type
  • reasoning_effort
可选值
reasoning_effort
  • the model card documents low
  • high
  • max
thinking.type
  • enabled
  • disabled
reasoning_effort
  • none
  • minimal
  • low
  • medium
  • high
  • xhigh
  • max (none and minimal skip thinking, low and medium map to high, xhigh maps to max)
默认值 - enabled, with reasoning_effort at max: the only GLM with an effort dial, and it defaults to the top of it

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: DeepSeek V4 Pro (0813) · GLM-5.2

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

DeepSeek V4 Pro (0813) 通过 · 3 sentences

Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.

输出 308 tok (+226 思考) 延迟 5.1 s

GLM-5.2 通过 · 3 sentences

Enabling reasoning means the LLM now generates thousands of invisible "thinking" tokens to systematically work through complex problems before producing a final answer. Because our cloud providers bill for these internal processing steps at the same rate as standard output, our billable token volume per query has doubled. While this increases our direct API costs, it drastically reduces error rates and manual review labor, ultimately lowering our total cost per resolved transaction.

输出 1223 tok (+1138 思考) 延迟 17.1 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

DeepSeek V4 Pro (0813) 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 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.

输出 1315 tok (+1130 思考) 延迟 16.9 s

GLM-5.2 未通过 · 1/8 cases (fails [1])

```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 […]

输出 4097 tok (+4036 思考) 延迟 58.4 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。

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

DeepSeek V4 Pro (0813) 通过 · 5/5 fields, guidance null

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}

输出 2155 tok (+2121 思考) 延迟 26.0 s

GLM-5.2 通过 · 5/5 fields, guidance "withheld"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld" } ```

输出 1947 tok (+1893 思考) 延迟 30.9 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。

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

DeepSeek V4 Pro (0813) 通过 · 121 words, 0 banned, 1 question

Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.

输出 2845 tok (+2694 思考) 延迟 25.5 s

GLM-5.2 通过 · 120 words, 0 banned, 1 question

We are introducing Caching for our API Gateway, the smartest way to optimize your workflows. Why pay for the exact same response twice? Now, you can automatically store and reuse prompt results across multiple AI providers, drastically reducing latency and overall operational costs. If a user submits a duplicate query, the gateway serves the cached answer instantly, regardless of whether you route to OpenAI, Anthropic, or others. This directly translates to faster applications and significantly lower monthly API bills. You can easily configure your specific caching rules within the developer dashboard and watch your efficiency soar. Stop wasting your valuable tokens on completely redundant computations. Upgrade to the latest gateway version today and experience the future of intelligent prompt management.

输出 11125 tok (+10984 思考) 延迟 114.8 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="deepseek-v4-pro-0813",
    # model="glm-5.2",  # 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

DeepSeek V4 Pro (0813) 和 GLM-5.2 哪个更便宜?

按「输入 / 1M token」算,DeepSeek V4 Pro (0813) 更便宜($1.32 对 $1.4,相差 1.1×)。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 DeepSeek V4 Pro (0813) 和 GLM-5.2 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。

DeepSeek V4 Pro (0813) 和 GLM-5.2 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