In personal Claude Code scenarios, the community report found MiniMax M2 more efficient but lacking planning depth, while GLM 4.6 was more reliable; DeepSeek V3.2 still awaits hands-on testing. It also explicitly cautioned that SWE-bench is only a rough approximation and cannot replace a real workflow.
Environment: Claude Code, with open-weight models used locally or via API; the author had experience with MiniMax M2 and GLM 4.6 but had not yet personally tested V3.2.
Input/configuration: agentic coding tasks and discussion of SWE-bench rankings; no unified public issue set, sampling parameters, tool traces, or repeat count were provided.
Result format: a personal workflow report and cautious views on extrapolating from benchmarks.
The post did not include a complete prompt. A reusable experimental design is to run V3.2, GLM 4.6, and MiniMax separately in the same Claude Code/Agent harness, repository, and tool-permission setup, recording plan drift, tool calls, and test results rather than looking only at the leaderboard.
The author described MiniMax M2 as an efficient, compact, effective tool-calling agent, but said it could easily drift off course during complex planning; GLM 4.6 was better suited as a daily driver and, with appropriate guidance, came close to the author's experience with Sonnet 4.5.
At the time of collection, the author had not personally tested DeepSeek V3.2 and only speculated based on information from others that it might be slightly above or close to GLM 4.6; this was explicitly unverified speculation.
The author noted that SWE-bench is only a rough approximation of solving real GitHub issues, overlooking the uncertainty and benchmark error involved in real-world use.
The report's value is in highlighting that “open models' planning, tool calling, and stability in a real Agent harness may differ from their leaderboard results.” DeepSeek V3.2 should be validated on repository-level tasks and tool traces of your own, rather than using the post to infer which model wins.
The author did not actually test V3.2, so the speculation cannot be presented as an evaluation result.
The experience came from a single user and a specific Claude Code configuration, and the model versions being compared may also have differed.
No public inputs, outputs, success rates, costs, or statistical methods were provided, so the evidence level can only be classified as personal experience.
Fix the same Agent harness, model versions, tool permissions, context, and budget, and prepare at least 20 real issues.
Pre-register the completion criteria: plan coverage, patch correctness, passing tests, no unauthorized writes, and an accurate final report.
Save the complete prompts, reasoning/tool traces, patches, tests, and failure reasons, and independently blind-review plan drift.
Place the result alongside the 70% high result on the SWE-bench leaderboard, while explicitly noting harness differences.
DeepSeek V3.2
