Two agent-first MIT models at very different scales. GLM-5.2 (753B/40B active, 1M context) is the premium agent brain; MiniMax-M2 (230B/10B active) is the high-throughput executor. M2 serves roughly 4× cheaper per token; GLM handles harder tasks and vastly longer context. Sophisticated stacks route between them.
MiniMax-M2 vs GLM-5.2 at a glance
| MiniMax-M2 | GLM-5.2 | |
|---|---|---|
| Vendor | MiniMax (China) | Zhipu AI (Z.ai) (China) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | MIT — weights free to download, self-host, fine-tune, and use commercially | MIT — weights free to download, self-host, fine-tune, and use commercially |
| Parameters | 230B (10B active) | 753B (~40B active) |
| Context window | ~200K tokens | 1M tokens |
| Modalities | text | text |
| Pricing | Varies by hosting provider (open weights) | Varies by hosting provider (open weights) |
| Released | 2025-10-27 | 2026-06-13 |
Specs and pricing verified July 2026.
About MiniMax-M2
MiniMax-M2 is the tool-use specialist of the open-weight field: 230B total parameters with just 10B active, MIT-licensed, and tuned specifically for agentic loops — 69.4% SWE-bench Verified, 46.3 Terminal-Bench, 83 LiveCodeBench. Mid-2026 roundups still rank it the best open model for tool-driven agents.
The 10B active footprint is the strategic choice: agent workloads burn tokens on long multi-step loops, and M2 delivers near-frontier agentic quality at interactive speed and low serving cost. For teams building autonomous agents rather than chat products, M2 is frequently the efficiency-optimal pick.
Full specs, benchmarks, and hardware guidance: the MiniMax-M2 page.
About GLM-5.2
GLM-5.2 is Zhipu AI's June 13, 2026 flagship: a ~753B-parameter mixture-of-experts model with ~40B active parameters, a 1-million-token context window, up to 128K output tokens, and MIT-licensed weights on Hugging Face. It is built deliberately as a coding and agent model — configurable thinking effort, native tool calling, MCP integration, structured JSON output — rather than a general-purpose chat model.
The engineering hook is IndexShare, Zhipu's sparse-attention refinement that reuses indexers across sparse-attention layers to cut per-token compute at long context. That is what makes the 1M-token window usable in practice for long-horizon coding agents, not just impressive on a spec sheet.
Full specs, benchmarks, and hardware guidance: the GLM-5.2 page.
Choosing between them
Choose MiniMax-M2 for:
- Tool-calling agents and terminal/computer-use workloads
- High-throughput agent loops where per-token cost compounds
- Self-hosting on a modest multi-GPU node (MIT license)
Choose GLM-5.2 for:
- Long-horizon coding agents (1M-token context built for repository-scale work)
- Teams that want an MIT-licensed Claude-for-coding alternative
- Tool-driven agentic workloads (native tool calling, MCP, JSON output)
Frequently asked questions
Is MiniMax-M2 better than GLM-5.2?
Two agent-first MIT models at very different scales. GLM-5.2 (753B/40B active, 1M context) is the premium agent brain; MiniMax-M2 (230B/10B active) is the high-throughput executor. M2 serves roughly 4× cheaper per token; GLM handles harder tasks and vastly longer context. Sophisticated stacks route between them.
Which is cheaper: MiniMax-M2 or GLM-5.2?
MiniMax-M2: Varies by hosting provider (open weights). GLM-5.2: Varies by hosting provider (open weights). Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host MiniMax-M2 and GLM-5.2?
MiniMax-M2: yes — weights are published under MIT. GLM-5.2: yes — weights are published under MIT.