GLM-4.6V vs MiniMax-M2.1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Zhipu AI 的 GLM-4.6V 与 MiniMax 的 MiniMax-M2.1 是当前备受关注的两款主力 AI 模型。GLM-4.6V 提供 131.1K Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 MiniMax-M2.1 提供 204K 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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GLM-4.6V
原生多模态视觉模型Zhipu AI 推出的 GLM-4.6V 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 131K 上下文窗口、80 Token/秒生成速度与 77/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
MiniMax-M2.1
自主智能体与工具专家MiniMax 推出的 MiniMax-M2.1 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、88 Token/秒生成速度与 80/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
204K tokens
N/A
核心差异一览
- Both models cost the same under this workload — choose on capability, not price.
- MiniMax-M2.1 has a larger context window (204K vs 131K tokens).
- MiniMax-M2.1 is faster at 88 tok/s vs 80 tok/s.
| Feature | GLM-4.6V | MiniMax-M2.1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 77 | 80 |
Provider | Zhipu AI | MiniMax |
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 | 131.1K tokens~197 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 | 80 tok/s | 88 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | N/A | 61% |
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.
GLM-4.6V · Strengths & Key Advantages
- 提供 131K 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 1280）
- 高达 80 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 510 毫秒
- 开放模型权重 (24B VLM)，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
MiniMax-M2.1 · 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 61% · MMLU-Pro 76.5%）
- 高达 88 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 420 毫秒
- 开放模型权重 (Confidential / 130B 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 | GLM-4.6V | MiniMax-M2.1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 14K | 16K |
Reasoning Tokens / Task Thinking tokens burned per task | 3K | 4K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.6V | MiniMax-M2.1 |
|---|---|---|
| Official API Model ID (SDK) | glm-4-6v | minimax-m2-1 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-12 | 2025-12 |
| Knowledge Cutoff | October 2025 | October 2025 |
| Architecture | High-Resolution Open Vision-Language Transformer | Refined Agentic Mixture-of-Experts Transformer |
| Parameters | 24B VLM | Confidential / 130B 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 | 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 | 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) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 15 GB VRAM (4-bit Quantized) · 52 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 81 GB VRAM (4-bit Quantized) · 280 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 |
| Output Modalities | text | text |
GLM-4.6V vs MiniMax-M2.1: 常见问题解答 (FAQ)
Which is more intelligent, GLM-4.6V or MiniMax-M2.1?
MiniMax-M2.1 is more intelligent. It scores 80 compared with 77 on the composite Intelligence Index.
Which model is faster?
MiniMax-M2.1 is faster, generating 88 tokens per second compared with 80 tokens per second.
Which model has lower latency?
MiniMax-M2.1 has lower latency, with a time to first token of 0.42s compared with 0.51s.
Which model has a larger context window?
MiniMax-M2.1 has a larger context window, supporting 204K tokens compared with 131.1K tokens.
综合结论
综合来看，GLM-4.6V（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 MiniMax-M2.1（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-M2.1 在综合智能与编程基准测试中表现更强，MiniMax-M2.1 拥有更快的 Token 生成速度，而 GLM-4.6V 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，GLM-4.6V 和 MiniMax-M2.1 哪个更强？+
在综合智能指数与编程基准评测中，MiniMax-M2.1 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。
GLM-4.6V 与 MiniMax-M2.1 的 API 价格哪个更便宜？+
GLM-4.6V 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），MiniMax-M2.1 为输入 $0.00 / 输出 $0.00。综合混合成本来看，GLM-4.6V 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？+
GLM-4.6V 支持 131.1K Token 上下文窗口，而 MiniMax-M2.1 支持 204K Token。在流式生成速度方面，MiniMax-M2.1 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: Zhipu AI 与 MiniMax 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。