MiniMax-M2.7-highspeed vs MiniMax-M2.5-highspeed
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
MiniMax 的 MiniMax-M2.7-highspeed 与 MiniMax 的 MiniMax-M2.5-highspeed 是当前备受关注的两款主力 AI 模型。MiniMax-M2.7-highspeed 提供 204K Token 上下文窗口,API 定价为每百万 Token 输入 $0.25 / 输出 $1.00;而 MiniMax-M2.5-highspeed 提供 204K Token 上下文窗口,定价为每百万 Token 输入 $0.15 / 输出 $0.60。以下为两者的基准测试、延迟与成本全面对比。
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MiniMax-M2.7-highspeed
超高速低延迟推理MiniMax 推出的 MiniMax-M2.7-highspeed 是一款面向实时交互对话、高并发路由与极低延迟生产环境的超高速大模型,具备 204K 上下文窗口、140 Token/秒生成速度与 82/100 综合智能指数(每百万 Token $0.25 输入 / $1 输出)。
$0.25 / 1M
$1.00 / 1M
204K tokens
N/A
MiniMax-M2.5-highspeed
自主智能体与工具专家MiniMax 推出的 MiniMax-M2.5-highspeed 是一款专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的大模型,具备 204K 上下文窗口、150 Token/秒生成速度与 78/100 综合智能指数(每百万 Token $0.15 输入 / $0.6 输出)。
$0.15 / 1M
$0.60 / 1M
204K tokens
N/A
核心差异一览
- MiniMax-M2.5-highspeed is 1.7x cheaper for API usage (input + output tokens combined).
- MiniMax-M2.5-highspeed is faster at 150 tok/s vs 140 tok/s.
| Feature | MiniMax-M2.7-highspeed | MiniMax-M2.5-highspeed |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 82 | 78 |
Provider | MiniMax | MiniMax |
Input Price Cost per million input tokens | $0.25 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $1.00 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.17 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 204K tokens~306 A4 pages of text | 204K tokens~306 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 140 tok/s | 150 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | 63% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.01 / 1M |
各项指标谁更胜一筹?
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
MiniMax-M2.7-highspeed · Strengths & Key Advantages
- 提供 204K 上下文窗口与最高 8K 输出容量,核心基准表现优异(GPQA Diamond 63% · MMLU-Pro 78%)
- 高达 140 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 290 毫秒
- 极具竞争力的 API 定价(每百万 Token $0.25 输入 / $1 输出),原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型,不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上,深度略逊于顶配重型旗舰推理模型
MiniMax-M2.5-highspeed · Strengths & Key Advantages
- 提供 204K 上下文窗口与最高 8K 输出容量,核心基准表现优异(MMLU-Pro 74% · Arena Elo 1295)
- 高达 150 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 270 毫秒
- 极具竞争力的 API 定价(每百万 Token $0.15 输入 / $0.6 输出),原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型,不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上,深度略逊于顶配重型旗舰推理模型
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | MiniMax-M2.7-highspeed | MiniMax-M2.5-highspeed |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.06 | $0.04 |
Output Tokens / Task Tokens generated per task | 15K | 12K |
Reasoning Tokens / Task Thinking tokens burned per task | 2K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | MiniMax-M2.7-highspeed | MiniMax-M2.5-highspeed |
|---|---|---|
| Official API Model ID (SDK) | minimax-m2-7-highspeed | minimax-m2-5-highspeed |
| API Protocol | MiniMax REST API (OpenAI-Compatible) | MiniMax REST API (OpenAI-Compatible) |
| Release Date | 2026-03 | 2026-02 |
| Knowledge Cutoff | January 2026 | December 2025 |
| Architecture | High-Throughput Hosted Cloud Accelerator | Latency-Optimized Hosted Cloud Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.25/1M in) across full 204K window | Flat rate ($0.15/1M in) across full 204K window |
| Prompt Cache Policy & TTL | Cached input at $0.025/1M tokens | Cached input at $0.015/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | MiniMax Production Safety & Alignment Verified | MiniMax Production Safety & Alignment Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text |
| Output Modalities | text | text |
MiniMax-M2.7-highspeed vs MiniMax-M2.5-highspeed: 常见问题解答 (FAQ)
Which is more intelligent, MiniMax-M2.7-highspeed or MiniMax-M2.5-highspeed?
MiniMax-M2.7-highspeed is more intelligent. It scores 82 compared with 78 on the composite Intelligence Index.
Which model is faster?
MiniMax-M2.5-highspeed is faster, generating 150 tokens per second compared with 140 tokens per second.
Which model is cheaper to run?
MiniMax-M2.5-highspeed is cheaper at $0.10 per 1M tokens blended, compared with $0.17 (7:2:1 cache:input:output ratio).
Which model has lower latency?
MiniMax-M2.5-highspeed has lower latency, with a time to first token of 0.27s compared with 0.29s.
综合结论
综合来看,MiniMax-M2.7-highspeed(适合:面向实时交互对话、高并发路由与极低延迟生产环境的超高速任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。)与 MiniMax-M2.5-highspeed(适合:专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。)各具优势:MiniMax-M2.7-highspeed 在综合智能与编程基准测试中表现更强,MiniMax-M2.5-highspeed 拥有更快的 Token 生成速度,而 MiniMax-M2.5-highspeed 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面,MiniMax-M2.7-highspeed 和 MiniMax-M2.5-highspeed 哪个更强?+
在综合智能指数与编程基准评测中,MiniMax-M2.7-highspeed 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
MiniMax-M2.7-highspeed 与 MiniMax-M2.5-highspeed 的 API 价格哪个更便宜?+
MiniMax-M2.7-highspeed 的 API 价格为输入 $0.25 / 输出 $1.00(每百万 Token),MiniMax-M2.5-highspeed 为输入 $0.15 / 输出 $0.60。综合混合成本来看,MiniMax-M2.5-highspeed 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度?+
MiniMax-M2.7-highspeed 支持 204K Token 上下文窗口,而 MiniMax-M2.5-highspeed 支持 204K Token。在流式生成速度方面,MiniMax-M2.5-highspeed 的吞吐表现更快。
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
数据来源: MiniMax 与 MiniMax 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。