GLM-5.1 vs MiniMax M2.7: Performance comparison for AI coding agents

Model performance comparison

A recent comparison between GLM-5.1 and MiniMax M2.7 reveals distinct performance profiles for different development tasks.

GLM-5.1 capabilities

GLM-5.1 demonstrates strength in complex problem-solving tasks:

• Reliable multi-file edits and cross-module refactors
• Test wiring and error handling cleanup
• Builds more and tests more in head-to-head runs
• Can solve complex problems "from scratch" using bare prompts

Benchmark results:

• SWE-bench-Verified: 77.8
• Terminal Bench 2.0: 56.2
• Both scores are highest among open-source models
• BrowseComp, MCP-Atlas, τ²-bench all at open-source SOTA

Limitations noted:

• Relatively slow performance
• Less reliable with tool calls
• Tends to hallucinate tools or generate nonsensical text on extended tasks

MiniMax M2.7 capabilities

MiniMax M2.7 excels in execution-oriented tasks:

• Fast responses with low TTFT (time to first token)
• High throughput
• Ideal for CI bots, batch edits, and tight feedback loops
• Often wins in minimal-change bugfix tasks

Usage patterns:

• Called via AtlasCloud.ai for 80-95% of daily work
• Swapped to heavier models only for complex tasks
• More execution-oriented than reflective
• Great at immediate tasks, weaker at system design and tricky debugging

Performance characteristics:

• On complex frontends and long reasoning chains, ranked below GLM-5.1
• For routine bug fixes, incremental backend work, and CI bots, good enough most of the time
• Fast performance makes it practical for everyday tasks

Practical recommendations

For complex engineering tasks, GLM-5.1 is worth the speed and cost trade-off despite its limitations. For most everyday development work, MiniMax M2.7 provides sufficient capability with significantly better performance characteristics.