GLM-5.2 vs MiniMax M3: The Open-Weights Coding Showdown (2026)

GLM-5.2 vs MiniMax M3: at a glance

GLM-5.2 (from Z.ai) and MiniMax M3 (from Shanghai lab MiniMax) are two of the most-discussed open-weight releases of mid-2026. Both target the same buyer — teams that want strong coding without paying closed-frontier API rates — but they got there with very different design choices. Here is the shape of the matchup before we go deep.

How do the coding benchmarks actually compare?

Start with the caveat that matters most: the two labs have not run on a single shared harness, and they even report different Terminal-Bench versions. So treat any "M3 beats GLM by X points" claim — including ones you see on Twitter — with suspicion. What we can do honestly is line up each model's own published numbers, keep the harnesses separate, and read the direction of travel.

Read it carefully: the GLM and MiniMax Terminal-Bench figures are on different versions of the harness and are not comparable as a single row, which is why they sit on separate lines above. What you can say is that on the cross-model leaderboards where GLM-5.2 actually appears — the Artificial Analysis Intelligence Index and Design Arena — it currently holds the top open-weights position. MiniMax has not posted numbers on those same boards, so M3's standing there is not established either way.

MiniMax M3 publishes its own respectable coding scores (59.0% SWE-Bench Pro, 66.0% Terminal-Bench 2.1) and a notably strong agentic-browsing result (83.5% BrowseComp). GLM-5.2 doesn't publish SWE-Bench Pro or BrowseComp, so those aren't head-to-head either — they're simply areas where M3 has a number on the board and GLM doesn't. The practical takeaway: for terminal-style and UI coding measured on the boards GLM appears on, GLM-5.2 is the front-runner; for multimodal and agentic-browsing tasks, M3 is the one with the published evidence.

Is the 1M-token context window actually useful?

Both models advertise a 1,000,000-token context window, which on paper means you can fit a large codebase into a single request. In practice the two get there differently, and that difference shows up on your bill and your latency graph.

GLM-5.2 serves 1M context in its glm-5.2[1m] variant with up to 131,072 output tokens per response — a 5× jump over GLM-5.1's 200K window. It is a strong long-context model, but the KV cache at 1M is large, so most long-context use runs through Z.ai's API or a provisioned cluster rather than a single workstation.

MiniMax M3's headline architectural claim is MiniMax Sparse Attention (MSA): a sparse-attention scheme built on a Grouped-Query Attention backbone that does block-level selection over real, uncompressed key-values. MiniMax reports roughly 9.7× faster prefill and 15.6× faster decoding at 1M-token context versus the previous generation, with per-token compute around one-twentieth of MiniMax M2. If your workload routinely fills the context window — long agent traces, whole-repo refactors, multi-document analysis — M3's long-context economics are meaningfully better on the numbers MiniMax has published.

What do the token economics look like?

This is where MiniMax M3 lands its biggest advantage. On OpenRouter, M3 is priced at $0.30 / 1M input and $1.20 / 1M output during its 50%-off launch promotion (list price $0.60 / $2.40). GLM-5.2's metered API runs $1.40 / 1M input and $4.40 / 1M output, with cached input at $0.26 / 1M.

At list prices, M3 is roughly 4–5× cheaper per output token than GLM-5.2. For high-volume agent workloads where output tokens dominate, that gap compounds quickly. GLM-5.2's counterweight on cost is its $0.26 cached-input rate: agent loops that re-send the same system prompt and codebase context on every turn can recover a large fraction of the difference. Both also undercut the closed-frontier APIs by a wide margin on published per-token pricing; our GLM-5.2 complete guide breaks down the cost math in detail.

The honest read: if raw cost-per-token is your binding constraint, M3 wins. If your agent is cache-heavy and you want the model topping the open-weights coding boards, GLM-5.2's higher sticker price is partly offset by caching.

Self-hosting: the two paths compared

Both models are open-weight, so you can run either on your own hardware — but the bill of materials differs because the models are different sizes.

• GLM-5.2 (~744B / 40B active). Unsloth's dynamic GGUF quants make it tractable: per Unsloth's dynamic-quant benchmarks, a 2-bit dynamic quant retains most of its full-precision quality after shrinking the weights to about 238 GB, which fits a 256 GB unified-memory Mac or a single 24 GB GPU with CPU offload. Full-precision serving wants an 8×H100-class cluster. Day-0 support landed in SGLang, vLLM, and llama.cpp.
• MiniMax M3 (~428B / 23B active). Fewer total and active parameters means a lighter footprint and faster decode at the same quant level; Unsloth shipped local quants shortly after the weights landed, and the model runs through llama.cpp / Unsloth Studio. The 23B active-parameter count keeps decode fast on modest hardware.

If you want the model topping the open-weights coding boards and you have the memory, self-host GLM-5.2. If you want the lighter, faster, multimodal model that's kinder to a single workstation, M3 is the easier self-host.

Does multi-modal matter here?

For a lot of real coding work in 2026, yes — and this is M3's structural advantage. MiniMax M3 accepts text, image, and video input natively; GLM-5.2 is text-only. If your workflow includes screenshot-to-code, debugging from a screen recording, reading design mockups, or building UI from a Figma export, M3 can see the input and GLM-5.2 cannot. With GLM-5.2 you would otherwise have to bolt a separate vision model onto the pipeline.

If your coding is pure text — repos, terminals, logs, specs — GLM-5.2's lack of vision costs you nothing, and you get the higher coding-leaderboard standing in return.

Who should pick GLM-5.2?

• You want the open-weights model that currently tops the coding leaderboards it appears on, for text work.
• Your work is text-first: repositories, CLIs, refactors, test generation, spec-to-code.
• You run cache-heavy agent loops where the $0.26 cached-input rate offsets the higher sticker price.
• You're building web or UI and want the current Design Arena #1.
• You have (or rent) the memory to self-host a ~744B MoE and want MIT-licensed weights you control.

Who should pick MiniMax M3?

• Your workflow is multimodal — screenshots, design mockups, video, or mixed image+code tasks.
• Cost-per-token is the binding constraint and you ship high output volume.
• You fill the 1M context routinely and want MSA's faster, cheaper long-context inference.
• You want a lighter self-host (fewer active parameters) on a single workstation.
• You lean on agentic browsing, where M3's published 83.5% BrowseComp is strong.

The decision in a line

Pick GLM-5.2 for the top open-weights coding standing on text; pick MiniMax M3 for multimodality, lower cost, and faster long-context inference. Many teams will run both — GLM-5.2 as the default coding agent and M3 for anything that touches images or has to hit a tight token budget.

Architecture details that matter for capacity planning

GLM-5.2 is a Mixture-of-Experts model with approximately 744 billion total parameters and about 40 billion active per token (some vendor write-ups quote 753B total). It exposes a usable 1M-token context in the glm-5.2[1m] variant. The weights are MIT-licensed on Hugging Face under zai-org/GLM-5.2; the training code and full technical report were not published at launch, so "open weights" is more precise than "open source."

MiniMax M3 is a ~428B-total / ~23B-active MoE whose defining feature is MSA — sparse attention over uncompressed KV on a GQA backbone, tuned for cheap 1M-context inference. MiniMax positions M3 as the first open-weight model to combine strong coding, a 1M context window, and native multimodality in one checkpoint.

FAQ

Is GLM-5.2 or MiniMax M3 better for coding?

For pure text coding — terminals, repos, agents — GLM-5.2 is currently the front-runner: it's the first open model past 80% on Terminal-Bench (per Cline) and sits #1 among open weights on the Artificial Analysis Intelligence Index. MiniMax M3 publishes its own coding numbers on different harnesses (59.0% SWE-Bench Pro, 66.0% Terminal-Bench 2.1) and is the model with native multimodal support and a published agentic-browsing result (83.5% BrowseComp). The two haven't run on a shared harness, so it's a directional read, not a decided scoreboard.

Which is cheaper, GLM-5.2 or MiniMax M3?

MiniMax M3. At list pricing ($0.60 / $2.40 per 1M tokens, currently $0.30 / $1.20 on a launch promo) it's roughly 4–5× cheaper per output token than GLM-5.2 ($1.40 / $4.40). GLM-5.2's $0.26 cached-input rate narrows the gap for cache-heavy agent loops.

Do both models support a 1M-token context window?

Yes. Both advertise 1,000,000-token context. MiniMax M3 uses its MSA sparse attention for faster, cheaper long-context inference; GLM-5.2 serves 1M context in its glm-5.2[1m] variant with up to 131K output tokens.

Does GLM-5.2 support image or video input?

No. GLM-5.2 is text-only. MiniMax M3 natively accepts text, image, and video, which is the main reason to choose M3 for screenshot-to-code or design-to-code work.

Can I run both models locally?

Yes. Both are open-weight. GLM-5.2 (~744B) runs via Unsloth dynamic GGUF — a 2-bit quant near 238 GB fits a 256 GB Mac or a 24 GB GPU with offload. MiniMax M3 (~428B / 23B active) is a lighter self-host thanks to fewer active parameters.

Are these benchmark comparisons apples-to-apples?

No. The labs report different Terminal-Bench versions and haven't run on a shared harness, so the tables above keep each model's published numbers separate rather than asserting a single-harness head-to-head. Treat any precise "X beats Y by N points" coding claim with caution.

Related reading

• GLM-5.2 complete guide (2026) — architecture, benchmarks, and the three ways to run it.
• MiniMax M3: developer guide to the open-weight 1M-context frontier.
• DeepSeek V4 complete guide (2026) — the other MIT-licensed MoE giant.
• Open-source LLMs landscape (2026) — where GLM-5.2 and M3 sit in the wider field.