MiniMax-M3 vs Kimi K2.7 Code
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
MiniMax 的 MiniMax-M3 与 Moonshot AI 的 Kimi K2.7 Code 是当前备受关注的两款主力 AI 模型。MiniMax-M3 提供 524.3K Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 Kimi K2.7 Code 提供 262.1K Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。
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MiniMax-M3
原生多模态视觉模型MiniMax 推出的 MiniMax-M3 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 524K 上下文窗口、70 Token/秒生成速度与 89/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
524.3K tokens
65%
Kimi K2.7 Code
开源权重编程专家Moonshot AI 推出的 Kimi K2.7 Code 是一款面向本地私有化部署与代码生成的开源权重编程大模型，具备 262K 上下文窗口、75 Token/秒生成速度与 89/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
262.1K tokens
62%
核心差异一览
- Both models cost the same under this workload — choose on capability, not price.
- MiniMax-M3 has a larger context window (524K vs 262K tokens).
- Kimi K2.7 Code is faster at 75 tok/s vs 70 tok/s.
- MiniMax-M3 leads on SWE-bench coding (65.0% vs 62.0%).
| Feature | MiniMax-M3 | Kimi K2.7 Code |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 89 | 89 |
Provider | MiniMax | Moonshot AI |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 524.3K tokens~786 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 32K | 32K |
Throughput Speed Tokens generated per second | 70 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 65% | 62% |
GPQA Diamond Graduate-level reasoning | 72% | 73% |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
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.
Contamination-free competitive programming problems collected continuously over time.
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-M3 · Strengths & Key Advantages
- 提供 524K 上下文窗口与最高 32K 输出容量，核心基准表现优异（SWE-bench Verified 65% · GPQA Diamond 72%）
- 高达 70 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 560 毫秒
- 开放模型权重 (Confidential / 300B MoE)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 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
Kimi K2.7 Code · Strengths & Key Advantages
- 提供 262K 上下文窗口与最高 32K 输出容量，核心基准表现优异（SWE-bench Verified 62% · GPQA Diamond 73%）
- 高达 75 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 480 毫秒
- 开放模型权重 (1.5T total (40B MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 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-M3 | Kimi K2.7 Code |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 28K | 26K |
Reasoning Tokens / Task Thinking tokens burned per task | 10K | 5K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | MiniMax-M3 | Kimi K2.7 Code |
|---|---|---|
| Official API Model ID (SDK) | minimax-m3 | kimi-k2-7-code |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-06 | 2026-06 |
| Knowledge Cutoff | February 2026 | April 2026 |
| Architecture | Multimodal Long-Context Sparse MoE Transformer | Code-Optimized Sparse Mixture-of-Experts Transformer |
| Parameters | Confidential / 300B MoE | 1.5T total (40B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 186 GB VRAM (4-bit Quantized) · 645 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 930 GB VRAM (4-bit Quantized) · 3225 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
MiniMax-M3 vs Kimi K2.7 Code: 常见问题解答 (FAQ)
Which model is faster?
Kimi K2.7 Code is faster, generating 75 tokens per second compared with 70 tokens per second.
Which model has lower latency?
Kimi K2.7 Code has lower latency, with a time to first token of 0.48s compared with 0.56s.
Which model has a larger context window?
MiniMax-M3 has a larger context window, supporting 524.3K tokens compared with 262.1K tokens.
综合结论
综合来看，MiniMax-M3（适合：原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、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。）与 Kimi K2.7 Code（适合：面向本地私有化部署与代码生成的开源权重编程任务、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-M3 在综合智能与编程基准测试中表现更强，Kimi K2.7 Code 拥有更快的 Token 生成速度，而 MiniMax-M3 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-M3 和 Kimi K2.7 Code 哪个更强？+
在综合智能指数与编程基准评测中，MiniMax-M3 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-M3 与 Kimi K2.7 Code 的 API 价格哪个更便宜？+
MiniMax-M3 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Kimi K2.7 Code 为输入 $0.00 / 输出 $0.00。综合混合成本来看，MiniMax-M3 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-M3 支持 524.3K Token 上下文窗口，而 Kimi K2.7 Code 支持 262.1K Token。在流式生成速度方面，Kimi K2.7 Code 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 Moonshot AI 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。