MiniMax M3 vs GLM 5.1: Coding and Agentic Comparison
MiniMax M3 vs GLM 5.1 on SWE-Bench Pro, Terminal-Bench, BrowseComp, and MCP Atlas, where the gaps show up, community reception, and how to evaluate both for real work.
MiniMax M3 vs GLM 5.1: A Close Coding Race
MiniMax M3 vs GLM 5.1 is one of the tighter comparisons in the public MiniMax release table. MiniMax lists M3 at 59.0% on SWE-Bench Pro, 66.0% on Terminal-Bench 2.1, 83.5% on BrowseComp, 74.2% on MCP Atlas, and 28.8% on KernelBench Hard. GLM 5.1 is listed at 58.4% on SWE-Bench Pro, 63.5% on Terminal-Bench 2.1, 79.3% on BrowseComp, and 71.8% on MCP Atlas. GLM 5.1's KernelBench Hard and context length are not reported in the cited comparison source.
Where the benchmark gap shows up
The SWE-Bench Pro gap is small (0.6 points), but it widens on BrowseComp, MCP Atlas, and Terminal-Bench 2.1, where M3 is ahead in the cited table. For a site like M-Chat, the broader M3 story matters too: MiniMax describes M3 as natively multimodal with text, image, and video input, a 1M-token context window, and an open-weight release. If GLM 5.1 is in your internal model set, evaluate both on the same prompts and keep the failure modes separate — hallucinated tool results, incomplete repository edits, context drift, and latency under longer prompts each fail differently.
Where this sits in the wider field
Pulling back, M3's 59.0% on SWE-Bench Pro narrowly leads the open-access group it is usually compared against (GLM 5.1 at 58.4%, Kimi K2.6 at 58.6%), and MiniMax reports it just ahead of GPT-5.5. Frontier closed models still lead on raw coding — third-party reports put Claude Opus 4.8 near 69.2% on SWE-Bench Pro — but at many times the cost. So MiniMax M3 vs GLM 5.1 is best read as a contest inside the value tier, where small benchmark gaps matter less than context length, multimodality, latency, and price.
What the community is saying
GLM and MiniMax both have engaged open-model audiences, so the comparison is being run in public. M3 hit the Hacker News front page at launch, where the discussion focused on its MiniMax Sparse Attention (MSA) design and long-horizon agent demos, and The Information framed the release as part of an intensifying open-source coding battle. A common, fair caveat — raised by outlets like Open Source For You — is that M3's "open-weight" release is not a full open-source license. For GLM users, that licensing nuance and your existing tooling often matter as much as a half-point benchmark difference.
How to use the comparison
Do not turn a comparison table into an automatic routing rule. The right takeaway is that MiniMax M3 deserves a direct test for code and agentic tasks, especially where 1M context and multimodal input are on your roadmap. GLM 5.1 remains a meaningful comparison point, and fields absent from the official MiniMax source should stay not reported here rather than be filled in for the sake of a complete-looking table.
Task types worth evaluating together
Because the public scores are close, split the evaluation by task type. First, repo-level understanding: have each model read several files and explain the risks. Second, terminal tasks: ask for runnable commands based on a failure log. Third, browsing and tool use: check whether the model folds external information into its final answer. Fourth, long-context tasks: keep a design doc, meeting notes, and code in one session and keep asking follow-ups. If your eval is only short Q&A, you will likely miss the product differences M3 is built around — 1M context, MSA efficiency, and multimodal input.