MiniMax M3 vs Qwen3.7 Plus
何时使用哪一个 — 经过整理的结论，而非基准测试表格
这两者纸面上很接近：minimax-m3 和 qwen3.7-plus 都提供 1000000 token 上下文、文本／图像／视频输入与文本输出、同样的对话、代码、推理、工具和长上下文标志，思考都可关闭，而且都在 2026-06-01 发布。区分点是价目表和输出上限：qwen3.7-plus 在输入（$0.4 对 $0.3）、输出（$1.6 对 $1.2）和缓存读取（$0.08 对 $0.06）上都约为 minimax-m3 的 1.33 倍，而 minimax-m3 允许 524288 最大输出 token 而非 65536，即 8 倍的空间。更便宜的运行和超长单次生成选 minimax-m3；偏好阿里作为厂商则选 qwen3.7-plus。
定价
| MiniMax M3 | Qwen3.7 Plus | Δ | |
|---|---|---|---|
| 输入 / 1M tokens | $0.3 | $0.4 | 0.75× |
| 输出 / 1M tokens | $1.2 | $1.6 | 0.75× |
| 缓存读取 / 1M tokens | $0.06 | $0.08 | 0.75× |
| 缓存写入 | 不单独收费 | 1.25x | — |
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
| MiniMax M3 | Qwen3.7 Plus | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可配置 | 可配置 |
| 结构化输出 | — | 是 |
| 提示词缓存 | 隐式（自动） | 隐式 + 显式 |
| 缓存生存时间 | 未公开 | explicit: 5m, reset on hit |
| 最小缓存前缀 | 512 个 token | 1024 个 token |
规格
| MiniMax M3 | Qwen3.7 Plus | |
|---|---|---|
| 输入模态 | 文本 图像 视频 | 文本 图像 视频 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-06-01 | 2026-06-01 |
| 上下文窗口 | 1M | 1M |
| 最大输出 | 524K | 66K |
| 思考参数 |
|
|
| 允许的值 | thinking.type
reasoning_split
| enable_thinking
thinking_budget
preserve_thinking
|
| 默认值 | adaptive: thinking on, with the model deciding when extra reasoning helps | on the Qwen3.7 Plus series is hybrid thinking with thinking enabled by default, and preserve_thinking is off |
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： MiniMax M3 · Qwen3.7 Plus
单个 Prompt，两个模型 —— 通过网关实测
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
Qwen3.7 Plus 通过 · 3 sentences
Enabling reasoning requires the model to generate extensive internal "thinking" tokens to process complex logic before delivering a final answer. Because our vendor charges us per token consumed, this hidden computational overhead effectively doubles our output volume for the exact same number of user requests. Consequently, our costs have doubled because we are now paying for the model's increased processing time to achieve higher-quality, more accurate results.
输出 1428 tok (+1345 思考) 延迟 25.2 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。
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
Qwen3.7 Plus 通过 · 8/8 cases
## 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 else: cur = 1 best = max(best, cur) return best ``` ## Bug Explanation The function has two related bugs rooted in how it counts run lengths. First, `cur` is initialized to `0` and only incremented on matches, so it counts the number of *transitions* between equal elements rather than the total elements in the run—a run of N identical items only reaches `cur = N − 1`, undercounting by one. This also causes the single-element case to return `0`, since the loop never executes and `cur` never increments. […]
输出 2317 tok (+2005 思考) 延迟 40.5 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。
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
Qwen3.7 Plus 通过 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
输出 1807 tok (+1750 思考) 延迟 31.6 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。
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
Qwen3.7 Plus 通过 · 120 words, 0 banned, 1 question
Introducing PromptCache, our new API gateway feature designed to store and reuse prompts across multiple artificial intelligence providers. Have you ever noticed how repeatedly sending identical queries drains your budget and increases latency? PromptCache solves this by intelligently storing responses at the gateway level. When your application requests the same prompt from a different provider, the system instantly returns the cached result. This drastically reduces API costs and accelerates response times for your users. You can configure custom expiration times and set specific fallback rules for each vendor. You must stop paying twice for the exact same computation. Please upgrade your entire infrastructure today and experience much faster and cheaper integrations without changing a single line of your application code.
输出 4453 tok (+4312 思考) 延迟 76.8 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="minimax-m3",
# model="qwen3.7-plus", # 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: "minimax-m3",
// model: "qwen3.7-plus", // 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": "minimax-m3",
# "model": "qwen3.7-plus", # 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: "minimax-m3",
// Model: "qwen3.7-plus", // 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("minimax-m3")
// .model("qwen3.7-plus") // 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
MiniMax M3 和 Qwen3.7 Plus 哪个更便宜？
在 输入 / 1m tokens 方面，MiniMax M3 更便宜（$0.3 对比 $0.4，相差 1.3×）。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 MiniMax M3 和 Qwen3.7 Plus 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。
MiniMax M3 和 Qwen3.7 Plus 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。