GLM-5.2 vs MiniMax M3: Which is Better in 2026?
GLM-5.2 (Zhipu AI, 753B parameters) and MiniMax M3 (MiniMax, 428B parameters) are both frontier-class models competing for the same developer and enterprise audience in 2026. This page compiles benchmarks, task grades, and practical guidance to help you decide which model fits your workflow.
Last updated: October 2, 2026
Quick Verdict
Across the benchmarks where both models have published scores, GLM-5.2 leads on 4 of 5 shared evaluation tasks. MiniMax M3 remains competitive, particularly in areas aligned with its training focus. For general-purpose quality, GLM-5.2 currently holds an edge — but the right choice depends heavily on your specific use case, budget, and whether you need API access or self-hosted deployment.
Side-by-Side Comparison
The table below covers every benchmark for which at least one model has a published score. Higher scores are better on all metrics except where noted. Bold values indicate the higher score in each row.
| Benchmark | GLM-5.2 | MiniMax M3 | Winner |
|---|---|---|---|
| GPQA Diamond | 91.2 | 90.9 | GLM-5.2 |
| SWE-bench Verified | 78.7 | 80.5 | MiniMax M3 |
| Arena Elo (Text) | 1470 | 1432 | GLM-5.2 |
| Arena Elo (WebDev) | 1605 | 1482 | GLM-5.2 |
| AIME 2024–25 (Epoch AI) | 86.4 | 71.1 | GLM-5.2 |
| SimpleQA Verified | 34.2 | — | GLM-5.2 |
| Arena Elo (Vision) | — | 1254 | MiniMax M3 |
Task Performance
Per-task grades are sourced from the id8 LLM leaderboard evaluations. Each grade reflects observed output quality across real-world prompts in that category. A dash (—) means grades are not yet published for that model.
| Task | GLM-5.2 | MiniMax M3 |
|---|---|---|
| Coding | S | S |
| Math | A | A |
| Content Writing | A | B |
| Reasoning | A | A |
| Studying | A | A |
| Chat / Conversation | A | B |
| Summarization | B | B |
| Agents / Tool Use | A | A |
| Vision / Multimodal | B | B |
| Data Analysis | B | B |
| Hindi / Multilingual | C | B |
| Interview Prep | A | A |
| overall | A | A |
Grade scale: S = Exceptional A = Strong B = Good C = Fair D = Weak
Key Differences
- GLM-5.2 is developed by Zhipu AI while MiniMax M3 comes from MiniMax — both are closed-source API models, so pricing and rate limits are set by their respective providers.
- In terms of raw scale, GLM-5.2 (753B parameters) is significantly larger than MiniMax M3 (428B parameters). Larger parameter counts often correlate with stronger reasoning, though efficiency improvements mean smaller models can punch above their weight.
- On SWE-Bench (real-world software engineering tasks), MiniMax M3 scores higher (80.5%), making it the stronger pick for autonomous coding and pull-request generation workflows.
- For graduate-level science and reasoning (GPQA Diamond), GLM-5.2 leads with 91.2%, indicating stronger performance on expert-level knowledge tasks.
- GLM-5.2 earns an S-grade (exceptional) in: Coding — making it the top choice for those workflows.
Which Should You Choose?
Choose GLM-5.2 if your primary use cases involve overall, Coding, Math. GLM-5.2 scores highly on these tasks and is especially well-suited for teams that need consistent, high-quality outputs at scale through Zhipu AI's API.
Choose MiniMax M3 if your work centres on overall, Coding, Math, and self-hosting a 428B-parameter model is feasible for your infrastructure. It offers a strong value proposition for those use cases.
Still unsure? The LLM Leaderboard lets you sort and filter models by benchmark — useful for narrowing down the right model for a specific workload.
Explore Further
Use these tools to dig deeper into either model's hardware requirements and leaderboard ranking.
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