Claude Fable 5 vs MiniMax M3
何时使用哪一个 — 经过整理的结论,而非基准测试表格
两个模型均具有 1000000-token 的上下文窗口并接受文本和图像输入,因此真正的区别在于价格与形态:minimax-m3 收费为 $0.3 输入和 $1.2 输出,对比 claude-fable-5 的 $10 和 $50,大约便宜 33x 和 42x,同时它还接受视频输入,允许高达 524288 个输出 token,并支持关闭思考功能。当你希望在聊天、代码和工具使用中开启 Anthropic 始终运行的推理模式,并且能够接受较高费率时,请选择 claude-fable-5;对于成本和可切换的思考功能更为重要的大批量、长上下文或视频工作,请选择 minimax-m3。
定价
| Claude Fable 5 | MiniMax M3 | Δ | |
|---|---|---|---|
| 输入 / 1M tokens | $10 | $0.3 | 33× |
| 输出 / 1M tokens | $50 | $1.2 | 42× |
| 缓存读取 / 1M tokens | $1 | $0.06 | 17× |
| 缓存写入 | 1.25x (5m) / 2x (1h) | 不单独收费 | — |
费率取自构建时的实时目录;每个模型页面均附有当前的费率卡。
它们的位置 — 以该计费单位计费的所有 63 个 聊天 模型的 每 1M token 的输入价格(对数刻度)
能力
| Claude Fable 5 | MiniMax M3 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 始终开启 | 可配置 |
| 结构化输出 | 是 | — |
| 提示词缓存 | 显式(由您标记前缀) | 隐式(自动) |
| 缓存生存时间 | 5m default, 1h option | 未公开 |
| 最小缓存前缀 | 1024 个 token | 512 个 token |
规格
| Claude Fable 5 | MiniMax M3 | |
|---|---|---|
| 输入模态 | 文本 图像 | 文本 图像 视频 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-06-09 | 2026-06-01 |
| 知识截止日期 | 2026-01 | — |
| 上下文窗口 | 1M | 1M |
| 最大输出 | 128K | 524K |
| 思考参数 | output_config.effort (thinking.type is adaptive-only and needs no configuration) |
|
| 允许的值 | effort
both "enabled" and "disabled" return 400 | thinking.type
reasoning_split
|
| 默认值 | thinking always on (adaptive) effort
| adaptive: thinking on, with the model deciding when extra reasoning helps |
规格转录自各供应商的文档;若供应商未发布某项数据,则直接省略该行,而非进行推断。 完整来源: Claude Fable 5 · MiniMax M3
单个 Prompt,两个模型 —— 通过网关实测
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
MiniMax M3 通过 · 3 sentences
Reasoning-enabled models deliberately produce extensive internal "chain-of-thought" tokens before answering, often consuming 5–10x more tokens per query than a standard model. Because LLM providers bill per token for both that reasoning trace and the final response, your cost-per-query rises proportionally—and that multiplier is the primary driver of the doubled bill. The right response isn't to disable reasoning, but to route only genuinely complex queries to it while keeping routine traffic on the cheaper standard tier, which typically recovers 60–80% of the cost without sacrificing quality where it matters.
输出 665 tok (+548 思考) 延迟 5.7 s
指令遵循(恰好三句,可数)、受众适配(面向 CFO 的语气),以及下方 token 计量所暴露的隐藏思考计费缺口。
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
MiniMax M3 通过 · 8/8 cases
The bug is in the loop bound: `range(1, len(xs))` iterates from index 1 up to but not including `len(xs) - 1`... wait, actually it goes up to `len(xs) - 1`, so the last index is included. The real problems are two: 1. **Starting `best` and `cur` at 0** and iterating from index 1 means a single-element list (`len(xs) == 1`) never enters the loop, so the function returns `0` instead of `1`. The fix is to either seed `best = cur = 1` when `xs` is non-empty, or include index 0 in the loop logic. 2. **Missing the final run**: the function only updates `best` *inside* the loop when it finds a continuation. If the longest run ends at the very last element, the loop body never gets to compare it after the increment — actually it does, but only if the run is longer than the previous `best`. […]
输出 464 tok 延迟 7.9 s
修复是否真的正确(可运行)、解释的信息密度,以及在一个边界明确的任务上的 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
MiniMax M3 通过 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ```
输出 2212 tok (+2161 思考) 延迟 23.3 s
schema 服从度(不臆造字段)、幻觉压力(guidance 明确被暂缓给出),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
MiniMax M3 通过 · 120 words, 0 banned, 1 question
Introducing PromptCache, our new API gateway feature that caches prompts across multiple LLM providers. Developers can store prompt-completion pairs centrally, reducing redundant inference calls and lowering costs without sacrificing quality. How will your workflow change when repeated requests resolve instantly from a shared cache layer? The gateway intercepts outgoing requests, checks for matching prompt fingerprints, and serves previously generated completions when available, falling back to the original provider on misses. Compatible with OpenAI, Anthropic, Cohere, and custom endpoints, PromptCache integrates with existing routing rules and supports TTL, versioning, and per-tenant namespaces. Teams now gain predictable latency, reduced token spend, and improved throughput during traffic spikes today. […]
输出 3256 tok (+2892 思考) 延迟 21.5 s
约束服从度(字数预算、禁用词表、唯一的那句问句)、文风指纹,以及长度控制。
只需一行代码即可在它们之间切换
两个 ID 都包含在下方的每个选项卡中 — 高亮显示的两行是唯一的修改。相同的端点,相同的密钥,相同的请求结构。
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="minimax-m3", # 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: "minimax-m3", // 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": "minimax-m3", # 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: "minimax-m3", // 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("minimax-m3") // 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 MiniMax M3 哪个更便宜?
在 输入 / 1m tokens 方面,MiniMax M3 更便宜($0.3 对比 $10,相差 33×)。其他行可能得出相反的结论——上方表格提供了完整信息,实际成本取决于你的组合使用情况。
我可以在不进行两次集成的情况下,对 Claude Fable 5 和 MiniMax M3 进行 A/B 测试吗?
可以。两者均通过同一个兼容 OpenAI 的端点提供服务,并使用同一把 API 密钥——切换只需更改一行模型字符串,因此你可以将一部分流量路由到各个模型并直接比较账单。
Claude Fable 5 和 MiniMax M3 支持提示词缓存吗?
是的 —— 两者对缓存读取的计费均低于其输入费率,因此热前缀工作负载的成本低于标价。准确的缓存读取行位于上方的定价表中。