Claude Fable 5 vs GLM-5.2
Claude Fable 5 已从我们的目录下线。下方是它最后公布的价格;这个模型已无法调用,对比中的另一个模型仍可正常调用。
什么时候选哪个
两个模型都有 1,000,000 token 上下文窗口和相近的最大输出(claude-fable-5 为 128000,glm-5.2 为 131072),所以分野在输入和成本:claude-fable-5 接受图像加文本且始终开启思考,而 glm-5.2 只接受文本且可关闭思考。按价目表,glm-5.2 输入便宜约 7.1 倍($1.4 对 $10),输出便宜约 11 倍($4.4 对 $50),缓存读取便宜近 3.8 倍($0.26 对 $1)。需要图像输入或强制推理时选 claude-fable-5;大批量长上下文文本工作选 glm-5.2。
Benchmark 成绩
43 项两边都有成绩,其中 1 项打平。
供应商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
价格
| Claude Fable 5 | GLM-5.2 | Δ | |
|---|---|---|---|
| 输入 / 1M token | $10 | $1.4 | 7.1× |
| 输出 / 1M token | $50 | $4.4 | 11× |
| 缓存读取 / 1M token | $1 | $0.26 | 3.8× |
| 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 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 Fable 5 | 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 | 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 Fable 5 | GLM-5.2 | |
|---|---|---|
| 输入模态 | 文本 图像 | 文本 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-06-09 | 2026-06-16 |
| 知识截止日期 | 2026-01 | - |
| 上下文窗口 | 1M | 1M |
| 最大输出 | 128K | 131K |
| 思考参数 | output_config.effort (thinking.type is adaptive-only and needs no configuration) |
|
| 可选值 | effort
both "enabled" and "disabled" return 400 | thinking.type
reasoning_effort
|
| 默认值 | thinking always on (adaptive) effort
| 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: Claude Fable 5 · GLM-5.2
同一条提示词,两个模型,经网关实测
Claude Fable 5 通过 · 3 sentences
When we enabled reasoning, the model began generating extensive internal "thinking" tokens before each answer, and those tokens are billed as output even though users never see them. Output tokens typically cost several times more than input tokens, so adding thousands of hidden reasoning tokens per request effectively multiplied our per-query cost. In short, we're paying for a much longer response behind the scenes in exchange for higher answer quality, and we can manage this by tuning reasoning effort levels or reserving reasoning mode for complex tasks only.
输出 228 tok 延迟 6.7 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。
Claude Fable 5 通过 · 8/8 cases
## Fixed code ```python def longest_run(xs): if not xs: return 0 best = 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 ``` ## Explanation The original function counts **adjacent equal pairs** rather than **items in a run**: a run of length *n* contains only *n − 1* equal neighbor pairs, so every result is off by one — a single-element list yields 0 instead of 1, and `[1, 2, 2]` yields 1 instead of 2 (which callers perceive as "missing" the run at the end). The fix is to initialize both `cur` and `best` to 1 (any nonempty list has a run of at least length 1), reset `cur` to 1 rather than 0 when
输出 531 tok 延迟 12.5 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。
Claude Fable 5 通过 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "Q3 (September quarter)", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on interpretation: "up eleven and change" is rendered as 11 (the precise figure isn't stated), and guidance is null because it was explicitly withheld.
输出 192 tok 延迟 6.5 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。
Claude Fable 5 通过 · 120 words, 0 banned, 1 question
**Introducing Cross-Provider Prompt Caching for our API Gateway** Starting today, your repeated prompts are cached once and reused across OpenAI, Anthropic, Google, and any other provider you route through the gateway. Instead of paying full token costs every time your application sends the same system prompt or context block, the gateway detects matching prefixes and serves cached results in milliseconds. Why keep spending money and latency on identical requests? Early adopters report cost reductions of up to 60 percent on high-volume workloads, along with faster median response times. Configuration is simple: enable caching in your dashboard, set a TTL, and choose which routes participate. Cache entries are encrypted at rest and never shared between accounts. Available now on all plans.
输出 1173 tok 延迟 18.2 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="claude-fable-5",
# 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)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-fable-5",
// 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",
});
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-fable-5",
# "model": "glm-5.2", # 取消注释此行,注释上一行
"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-fable-5",
// Model: "glm-5.2", // 取消注释此行,注释上一行
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-fable-5")
// .model("glm-5.2") // 取消注释此行,注释上一行
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常见问题
Claude Fable 5 和 GLM-5.2 哪个更便宜?
按「输入 / 1M token」算,GLM-5.2 更便宜($1.4 对 $10,相差 7.1×)。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 Claude Fable 5 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。
Claude Fable 5 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。