模型对比
M3 和 MiniMax M3 都是 AI 领域的模型。以下是基于 traeai 收录的真实报道数据的全面对比。
模型
也叫：Minimax M3
MiniMax 推出的新一代大语言模型，用于编码等任务。
4 篇相关报道
模型
也叫：M3
多模态大模型，支持长程上下文与多模态任务。
9 篇相关报道
4
M3 相关
0
共同提及
9
MiniMax M3 相关
基于 traeai 收录材料自动更新
M3 与 MiniMax M3 的差异，最好从真实材料覆盖、共同语境和高频标签一起判断。traeai 会根据已收录内容持续更新这组对比。
Minimax M3 combines 1M context, multimodality, open weights, and agentic coding — currently free via Open Code; scored 4/10 on complex UI tasks, ideal for toolchain validation but not high-fidelity state systems.
入选理由：M3 支持 100 万 token 上下文窗口，采用稀疏注意力架构控制成本，专为编码代理设计。
The M3 model by MiniMax is now available on Ollama Cloud, deployed in the US with zero data retention, optimized for coding and agentic tasks. It achieves 59.0%+ on SWE-Bench Pro and supports up to 1M context length via sparse attention.
入选理由：M3 在 SWE-Bench Pro 基准中取得 59.0% 正确率，优于多数开源模型。
MiniMax M3 is now available on Vercel AI Gateway, offering a 1M-token context window and native multimodal support via MSA architecture, enhancing engineering efficiency and tool integration.
入选理由：M3模型支持1M令牌上下文窗口，显著提升长文本处理能力。
MiniMax M3 is about to launch; a developer will conduct deep testing using their own coding agent and harness, with a review to follow. Free trial available on OpenCode.
入选理由：MiniMax M3 即将发布，预计在 OpenCode 平台上线。
MiniMax M3 has debuted in Code Arena, ranking #7 in the frontend track with a score of 1,531, tying with GLM-5.1. It advances the Pareto frontier in its price class at $0.60/ $2.40 per Mtoken.
入选理由：Code Arena 前端排名第7，得分1531，与GLM-5.1并列。
Together AI optimized the deployment of MiniMax M3, achieving 81–125% throughput improvements through architectural and engineering innovations.
入选理由：MiniMax M3 supports 1M-token context and native multimodality, making it suitable for complex real-world tasks.
MiniMax-M3 has launched on OpenRouter — a frontier-class open-weight model supporting 1M-token context, agentic performance, and native multimodality (image & video), marking a major leap in long-context, autonomous-agent, and multi-modal AI capabilities.
入选理由：MiniMax-M3 支持1M-token上下文窗口，显著超越主流模型如GPT-4o的32K限制。
Real-world testing shows that MiniMax M3 outperforms M2.7 in multimodal long-range tasks, with a 30% increase in inference speed and a 15% increase in accuracy.
入选理由：MiniMax M3在多模态长文本生成任务中准确率较M2.7提升15%。
The open-weight model MiniMax M3 has reached performance comparable to GPT-5.5 and Opus 4.7, outperforming Gemini 3.1 Pro in coding tasks, and costs 10x less to use, with weights to be released on Hugging Face next week.
入选理由：MiniMax M3在SWE Bench Pro上与GPT-5.5性能相当
MiniMax M3 is the first open-weight model supporting text, vision, document, and code tasks, excelling in benchmarks like SWE-Bench Pro with 1M context length.
入选理由：MiniMax M3 在 SWE-Bench Pro 达到 59.0%，Terminal Bench 2.1 达 66.0%，是当前开源模型中编程能力最强之一。
MiniMax M3 ranks #14 in Document Arena, a leaderboard for document analysis and long-context reasoning, shifting the Pareto frontier at its price point.
入选理由：MiniMax M3 在 Document Arena 排名第 14，评估维度为文档分析与长文本推理能力。
MiniMax M3 achieved a 26% performance improvement on BU Bench, reaching the level of Claude 4.6-sonnet and Gemini 3.5 Flash, but test details are not disclosed.
入选理由：MiniMax M3在BU Bench上实现26%的性能提升，具体测试方法未详述。
