MiniMax M3 vs GLM 5.2
Two fast-iterating challengers, MiniMax's M3 against GLM 5.2.
Figures as of , read live from the model registry Zeplik bills against.
What the numbers say
- MiniMax M3 is about 5.7x cheaper on output tokens (14.4 vs 81.6 credits per 1M).
- GLM 5.2 takes noticeably more context: 1.0M vs 524K tokens.
- MiniMax M3 accepts images; GLM 5.2 is text-only.
- GLM 5.2 is the newer release (June 2026 vs May 2026).
Derived from the live registry Zeplik routes and bills against. Credits: 1 credit = $0.10, computed from the raw provider rate with a 1.20x margin.
Side by side
MiniMax M3
MiniMax
MiniMax-M3 is a multimodal foundation model from MiniMax.
- Provider
- MiniMax
- Context window
- 524K tokens
- Input price
- 3.6 credits / 1M tokens ($0.30 raw)
- Output price
- 14.4 credits / 1M tokens ($1.20 raw)
- Vision (image input)
- Yes
- Tool calling
- Yes
- Extended reasoning
- No
Best for
- High-volume everyday tasks where speed and cost matter: summaries, drafts, quick questions
- Very long documents and codebases: a 524K-token window fits entire books or repositories in one conversation
- Working with images: screenshots, charts, photos and scanned documents alongside text
- Tool use and agents: reliably calls functions, so it can search, run skills and drive workflows
GLM 5.2
Z.ai
GLM 5.2 is a large-scale reasoning model from Z.ai.
- Provider
- Z.ai
- Context window
- 1.0M tokens
- Input price
- 0.072 credits / 1M tokens ($0.0060 raw)
- Output price
- 81.6 credits / 1M tokens ($6.80 raw)
- Vision (image input)
- No
- Tool calling
- Yes
- Extended reasoning
- No
Best for
- Hard problems where answer quality matters more than cost: strategy, analysis, difficult writing
- Very long documents and codebases: a 1.0M-token window fits entire books or repositories in one conversation
- Tool use and agents: reliably calls functions, so it can search, run skills and drive workflows
Try both on Zeplik
The honest answer to most model debates is to run your own prompt on both. Zeplik puts MiniMax M3 and GLM 5.2 in the same chat, so you can switch mid-conversation and compare answers on the work you actually do.
Frequently asked questions
- What is the difference between MiniMax M3 and GLM 5.2?
- MiniMax M3 is about 5.7x cheaper on output tokens (14.4 vs 81.6 credits per 1M). GLM 5.2 takes noticeably more context: 1.0M vs 524K tokens. MiniMax M3 accepts images; GLM 5.2 is text-only. GLM 5.2 is the newer release (June 2026 vs May 2026).
- Is MiniMax M3 or GLM 5.2 cheaper?
- MiniMax M3 is cheaper: 14.4 credits per million output tokens against 81.6. 1 credit = $0.10, computed from the raw provider rate with a 1.20x margin.
- Which has the larger context window, MiniMax M3 or GLM 5.2?
- GLM 5.2, at 1.0M tokens against 524K.
- Can I use both MiniMax M3 and GLM 5.2 on Zeplik?
- Yes. Zeplik is a multi-model chat, so both sit behind one account and you can switch between them mid-conversation to compare answers on your own prompt.
- Do MiniMax M3 and GLM 5.2 need a paid plan?
- Both need a paid plan (Plus or above). Usage is then billed in credits from your monthly allowance.
Related comparisons
- MiniMax M3 vs DeepSeek V4 Pro 0423MiniMax's M3 against the open-weight benchmark everyone measures by.
- DeepSeek V4 Pro 0423 vs GLM 5.2DeepSeek against Z.ai's GLM, both open-weight lines with strong coding results.
- MiniMax M3 vs MiniMax M2.7MiniMax's M3 against the best of the M2 line.
- Qwen3.7 Max vs GLM 5.2Qwen's capability tier against the GLM 5.2 generation.
- MiniMax M3.1 Flash vs MiniMax M3MiniMax's newest flash tier against the M3 it extends: where throughput is bought and what it costs in depth.
- GLM 5.2 vs GLM 5.1GLM 5.2 against its immediate predecessor, at a third less per token and five times the context window.
Explore Zeplik
- 468 AI skillsReady-to-run expert methods that run on MiniMax M3, GLM 5.2 or any other model.
- 997 integrationsConnect Gmail, Slack, GitHub, Notion and hundreds more so the assistant can work in your apps.
- Simple pricingPlus, Pro and Max plans with a monthly credit allowance that works across every model.