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Claude Opus 5 vs GLM-5

vs

什么时候选哪个

claude-opus-5 接受图像和文本输入,上下文窗口 1,000,000 token,是 glm-5 的 200000 的五倍,所以含图像或整库上下文的提示选它。glm-5 只接受文本,但便宜得多:输入低约 5 倍($1 对 $5),输出低 7.8 倍($3.2 对 $25),缓存读取 $0.2 对 $0.5,最大输出略大,131072 token 对 128000。两者都可关闭思考。能装进 200000 token 的大批量文本、代码和工具工作选 glm-5。

Benchmark 成绩

领先高于同类均值无更高分Claude Opus 5538 / 468 / 46GLM-5015 / 522 / 52

5 项两边都有成绩。

Claude Opus 5 GLM-5 其他参测模型 同类均值 ★ 没有模型得分更高
NL2Repo
没有模型得分更高 75.3%
35.9%
BioMysteryBench hard
49.4%
N/A
OSWorld 2.0
没有模型得分更高 70.6%
N/A
Cybergym
N/A
43.2%
HealthBench Professional
59.8%
N/A
Finance Agent v2
58.6%
N/A
Legal Agent Benchmark
6.7%
N/A
GPQA Diamond
93.4%
86%
BrowseComp
90.8%
62%
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 GLM-5 Δ
输入 / 1M token $5 $1 5×
输出 / 1M token $25 $3.2 7.8×
缓存读取 / 1M token $0.5 $0.2 2.5×
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 78 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)

Claude Opus 5 · $5 GLM-5 · $1
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

Claude Opus 5 GLM-5
工具调用 是 是
思考控制 可配置 可配置
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 未公布
最小缓存前缀 1024 个 token 未公布

规格

Claude Opus 5 GLM-5
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 2026-02-12
知识截止日期 2026-05 -
上下文窗口 1M 200K
最大输出 128K 131K
思考参数
  • thinking.type
  • output_config.effort
thinking.type
可选值
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max
thinking.type
  • enabled
  • disabled
默认值

thinking on

effort
  • high (Claude API and Claude Code)
enabled, and on GLM-5 the model automatically determines whether to think

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 · GLM-5

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

GLM-5 通过 · 3 sentences

Enabling reasoning forces the model to perform multiple hidden steps to "think" through a problem, drastically increasing the volume of data processed per query compared to a standard response. Because our billing is directly tied to this processing volume, the additional computational work required for higher accuracy has proportionally doubled our consumption. Essentially, the spike reflects a trade-off where we are paying a higher variable cost to secure significantly better analytical outcomes.

输出 1393 tok (+1308 思考) 延迟 36.0 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

GLM-5 通过 · 8/8 cases

Here is the fixed code: ```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 `best` and `cur` to zero, which causes the function to count the number of *transitions* between adjacent elements rather than the total number of items in the run, resulting in a return value that is always one less than the actual length (reporting 0 for a single element and 1 for a pair). By initializing the counters to 1 and resetting `cur` to 1 when a streak breaks, the logic correctly counts the items themselves; adding an explicit check for an empty list ensures the function handles the zero-element edge case correctly.

输出 2907 tok (+2694 思考) 延迟 73.7 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

GLM-5 通过 · 5/5 fields, guidance "withheld pending the Q4 close"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld pending the Q4 close" } ```

输出 3620 tok (+3561 思考) 延迟 91.8 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

GLM-5 通过 · 119 words, 0 banned, 1 question

We are thrilled to introduce Global Prompt Caching, a powerful new capability within our API gateway designed to optimize your AI operations. By intelligently storing prompt responses across every supported provider, this feature drastically reduces latency and cuts operational costs. Instead of processing identical requests repeatedly, our system serves cached results instantly, ensuring consistent performance even during high-traffic periods. Why pay full price for repeated inference on the same inputs? This update gives developers fine-grained control over cache lifetimes and hit rates, allowing for predictable budgeting and faster application response times. You can activate this functionality directly in your dashboard settings today. Start maximizing your efficiency now and deliver a snappier experience to your end-users without unnecessary API expenditure.

输出 715 tok (+571 思考) 延迟 18.9 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="glm-5",  # 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 GLM-5 哪个更便宜?

按「输入 / 1M token」算,GLM-5 更便宜($1 对 $5,相差 5.0×)。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 GLM-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。

Claude Opus 5 和 GLM-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。

相关对比

我们的实测研究