Qwen3.7-Max Not Released with Open Weights, but Still Cost-Effective for Enterprise Agents
TL;DR · AI Summary
Qwen3.7-Max was not released with open weights, but due to its high cost-effectiveness and strong performance in enterprise agent scenarios, it's worth watching.
Key Takeaways
- Qwen3.7-Max scored 69.7 on Terminal-Bench 2.0, 60.6 on SWE-Pro, and 80.4 on SWE-
- The model supports a 35-hour autonomous coding run with 1,158 tool calls, suitab
- Its pricing is more competitive: $1.7/input M token and $5/output M token, compa
Outline
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Qwen3.7-Max is not open-sourced but is considered valuable for enterprise agents.
The model performs well on multiple benchmarks, especially for agent workflows.
Supports a 35-hour autonomous coding session with 1,158 tool calls.
More cost-effective than Claude Sonnet with lower input/output costs.
Milvus enhances agent efficiency by providing retrieval and memory layers.
Mindmap
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查看大纲文本（无障碍 / 无 JS 友好）
- Qwen3.7-Max 企业代理应用
- 性能表现
- Terminal-Bench 2.0: 69.7
- SWE-Pro: 60.6
- SWE-Verified: 80.4
- 成本效益
- 输入定价: $1.7/M token
- 输出定价: $5/M token
- 对比 Claude Sonnet
- Milvus 协同
- 向量数据库支持
- 减少无效上下文传输
- 提升执行效率
Highlights
Key sentences worth saving and sharing.
Qwen3.7-Max scored 69.7 on Terminal-Bench 2.0, 60.6 on SWE-Pro, and 80.4 on SWE-Verified.
It supports a 35-hour autonomous coding run with 1,158 tool calls, ideal for complex agent tasks.
Pricing is $1.7/M input token and $5/M output token, cheaper than Claude Sonnet ($3/$15).
The model is clearly aimed at agent workflows: 69.7 on Terminal-Bench 2.0, 60.6 on SWE-Pro, 80.4 on SWE-Verified, plus a https://t.co/DpkaQvFf4h" / X
Milvus on X: "This time, 𝗤𝘄𝗲𝗻𝟯.𝟳-𝗠𝗮𝘅 was not released with open weights. But for enterprise agents, it is still one of the most cost-effective models to watch. The model is clearly aimed at agent workflows: 69.7 on Terminal-Bench 2.0, 60.6 on SWE-Pro, 80.4 on SWE-Verified, plus a https://t.co/DpkaQvFf4h" / X
Don’t miss what’s happening
This time, 𝗤𝘄𝗲𝗻𝟯.𝟳-𝗠𝗮𝘅 was not released with open weights. But for enterprise agents, it is still one of the most cost-effective models to watch. The model is clearly aimed at agent workflows: 69.7 on Terminal-Bench 2.0, 60.6 on SWE-Pro, 80.4 on SWE-Verified, plus a reported 35-hour autonomous coding run with 1,158 tool calls. The pricing also stands out: roughly $1.7 per 1M input tokens and $5 per 1M output tokens, compared with Claude Sonnet at $3 input and $15 output. But agent cost is not just model pricing. Once an agent starts working across documents, web results, tools, and long task histories, the expensive part is often the context you keep feeding back to the model. Most of it does not need to be at the prompt every time. That is where Milvus comes in. It gives agents a memory and retrieval layer, so they can find the right enterprise knowledge, past conversations, or tool outputs without dragging the whole history into every prompt. For models like Qwen3.7-Max, that means better economics in practice: fewer wasted tokens, lower latency, and more grounded execution. #Qwen3_7#Qwen#Milvus#Vectordatabase
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