Gemma 4 vs GLM-5.2: Can You Even Compare Them Locally?

Search interest in "glm-5.2" has spiked alongside "gemma 4" queries recently, and the pairing makes sense on the surface — both are open-weight models from major AI labs, released within two months of each other in 2026. But before building a spec-for-spec comparison, there's a more honest question to answer first: are these actually competing for the same job?

Quick verdict: No, not for local use. GLM-5.2 is a 753-billion-parameter Mixture-of-Experts model built for hosted, agentic coding work, with a real-world minimum footprint north of 220 GB even at aggressive quantization. Every Gemma 4 model — including the 31B flagship — runs on a single consumer GPU or a Mac with 16–48 GB of unified memory. If your bar for "compare" means "which one can I actually run on my machine," Gemma 4 wins by default because GLM-5.2 mostly can't compete in that arena. If your bar is "which one is the stronger model, full stop, cost be damned," GLM-5.2 wins on the benchmarks that overlap. Read on for why both of those statements are true at once.

What Is GLM-5.2?

GLM-5.2 is Zhipu AI's (branded Z.ai) flagship open-weight model, released June 13, 2026 as a successor to GLM-5.1 (Eigent.ai overview). It's a sparse Mixture-of-Experts model with roughly 753 billion total parameters and about 40 billion active per token, and it ships under the permissive MIT license — genuinely open weights, no usage restrictions (Hugging Face model card).

The headline feature is a 1-million-token context window, paired with a sparse-attention technique Zhipu calls "IndexShare," designed to keep inference costs from exploding at that context length (Eigent.ai). GLM-5.2 is explicitly positioned as a coding and agentic-workflow model — long-horizon software engineering tasks, tool calling, and terminal-based agents are its target use case, not general chat or multimodal reasoning (VentureBeat).

One modality note worth flagging up front: unlike Gemma 4, GLM-5.2 is text-only — no image, audio, or video input (Hugging Face model card). If your use case needs vision or audio, this comparison ends here in Gemma 4's favor regardless of anything else.

Spec Comparison

Spec	GLM-5.2	Gemma 4 12B	Gemma 4 26B A4B	Gemma 4 31B
Architecture	MoE	Dense	MoE	Dense
Total params	~753B	12B	25.2B	30.7B
Active params	~40B	12B	3.8B	30.7B
Context window	1M tokens	256K	256K	256K
License	MIT	Apache 2.0	Apache 2.0	Apache 2.0
Modalities	Text only	Text, image, audio	Text, image	Text, image
Release date	June 13, 2026	June 3, 2026	April 2026	April 2026

Sources: GLM-5.2 model card, Gemma 4 model card.

The size gap is the story here: GLM-5.2's total parameter count is roughly 24x larger than Gemma 4 31B and 63x larger than Gemma 4 12B. Even comparing active parameters — the number that determines per-token compute — GLM-5.2's 40B active is still bigger than any single Gemma 4 model's total parameter count. These aren't models built for the same hardware tier, and that's before quantization enters the picture.

Benchmark Comparison: Where the Numbers Actually Overlap

Most benchmarks GLM-5.2's model card reports are coding- and agent-specific tests (SWE-bench Pro, Terminal-Bench 2.1, MCP-Atlas) that Google doesn't run against Gemma 4 the same way, so a clean row-by-row match across the full suite isn't possible. But three benchmarks do overlap between the two official model cards:

Benchmark	GLM-5.2	Gemma 4 31B	Edge
GPQA Diamond (expert science)	91.2%	84.3%	GLM-5.2 (+6.9)
AIME 2026 (competition math)	99.2%	89.2%	GLM-5.2 (+10.0)
Humanity's Last Exam (with tools)	54.7%	26.5%	GLM-5.2 (+28.2)

Sources: GLM-5.2 model card, Gemma 4 31B benchmarks, ArtificialAnalysis.ai comparison.

GLM-5.2 wins all three by a wide margin, and the Humanity's Last Exam gap is the largest of any comparison on this site. That's not surprising given the parameter count — a 753B model with 40B active parameters has far more raw capacity than a 31B dense model. We could not find published GLM-5.2 scores for MMLU-Pro, LiveCodeBench, or MMMU-Pro — the model card simply doesn't report them, likely because Zhipu is optimizing marketing around coding and agentic benchmarks rather than the general knowledge/vision suite Google emphasizes. We're not filling those gaps with estimates; if Zhipu publishes them later, we'll update this table.

For coding specifically — GLM-5.2's actual specialty — it reports SWE-bench Pro at 62.1% and Terminal-Bench 2.1 around 81–82.7% (Hugging Face model card), putting it within a few points of Claude Opus 4.8 on agentic coding tasks (VentureBeat). Gemma 4 doesn't publish directly comparable SWE-bench or Terminal-Bench numbers, so this half of the comparison is one-directional — GLM-5.2 is clearly strong here, but there's no Gemma number to put next to it.

Local Deployment Reality

This is where the comparison actually matters for anyone reading a site about running models on their own hardware.

Gemma 4 ships official Q4_0 weight sizes ranging from 5 GB (E4B) to 17.4 GB (31B) — see our hardware requirements guide for the full breakdown. Every model in the family fits on a single consumer GPU (12–24 GB) or an Apple Silicon Mac with 16 GB or more of unified memory.

GLM-5.2 is a different universe. At full BF16 precision the weights run to roughly 1.5 TB. Even Unsloth's aggressive dynamic quantization only gets you this far (Unsloth documentation):

Quantization	File size	Practical minimum
1-bit (UD-IQ1_S)	~223 GB	223 GB RAM
2-bit (UD-IQ2_M)	~239 GB	245 GB
4-bit (UD-Q4_K_XL)	372–475 GB	372–475 GB
8-bit (UD-Q8_K_XL)	~810 GB	810 GB

Even the smallest usable quant — 1-bit, with meaningful quality loss — needs roughly 223 GB of addressable memory, more than 12x Gemma 4 31B's full weight size and more than 33x Gemma 4 12B's. Unsloth's own guidance says the 2-bit quant "can directly fit on a 256GB unified memory Mac" or run on "1x24GB GPU and 256GB of RAM with MoE offloading" (Unsloth documentation), with reported speeds around 3–9 tokens/second on that kind of setup. That's a top-tier Mac Studio or a server-class workstation — not the 16–32 GB laptops and desktops this site's audience typically runs Gemma 4 on.

Licensing is a wash — both MIT (GLM-5.2) and Apache 2.0 (Gemma 4's license) are permissive, no-restriction licenses for commercial use. Format support is also fine on both sides: GLM-5.2 has community GGUF quants from Unsloth, and Gemma 4 has official GGUF releases plus broad Ollama/LM Studio support (see our guides on running Gemma 4 with Ollama and LM Studio). The blocker isn't format or license — it's raw memory.

Which to Pick for What

• You want to run a model on a laptop, Mac Mini, or single consumer GPU: Gemma 4, no contest. Pick the size that fits your memory — see the hardware requirements guide — and consider the 12B for the best quality-per-GB if you have 16 GB free.
• You need vision or audio input: Gemma 4 is your only option here. GLM-5.2 doesn't support any modality besides text.
• You have access to a 256 GB+ Mac Studio, a multi-GPU workstation, or you're comfortable running hosted inference: GLM-5.2 is a legitimately stronger model for coding and agentic tasks, backed by benchmark numbers that beat Gemma 4 by wide margins on the tests both cards report.
• You want frontier-class reasoning without the memory bill: most people in this position should look at hosted GLM-5.2 API access (roughly $1.20–1.40 input / $4.10–4.40 output per million tokens) rather than trying to self-host it — the API cost is still far cheaper than acquiring 256 GB of RAM. See our Gemma 4 vs Qwen 3.5 benchmarks for a comparison where both sides are actually local-hardware-realistic.
• You want the strongest fully local model regardless of family: stick with Gemma 4 31B or explore the full benchmark breakdown — it's the ceiling for what fits on prosumer hardware today.

FAQ

Is GLM-5.2 better than Gemma 4? On the benchmarks where both publish comparable numbers (GPQA Diamond, AIME 2026, Humanity's Last Exam with tools), GLM-5.2 scores meaningfully higher. But "better" only matters if you can run it — and for the vast majority of people reading this on a laptop or single-GPU desktop, GLM-5.2 isn't runnable at all.

Can GLM-5.2 run on a 24 GB GPU? Not on its own. Unsloth's guidance pairs a 24 GB GPU with 256 GB of system RAM for MoE expert offloading — a 24 GB GPU with typical desktop RAM (32–64 GB) cannot run GLM-5.2 at usable quality.

Does GLM-5.2 support images or audio? No. GLM-5.2 is text-only. Every Gemma 4 model supports at least text and image, and E4B/12B add native audio.

Why are "glm-5.2" and "gemma 4" trending together if they're not really comparable? Both are prominent 2026 open-weight releases launched within two months of each other, and search engines naturally group "open-weight model" queries together. The trend reflects general interest in the open-weight landscape more than a genuine like-for-like buying decision — which is why this article exists.

Is there a smaller GLM-5.2 that's easier to run locally? As of this writing, Zhipu hasn't released a smaller variant of GLM-5.2 comparable in size to Gemma 4's lineup. If one ships, we'll cover it.

What's Next?

• Find the Gemma 4 size that fits your hardware: Gemma 4 Hardware Requirements
• A comparison where both sides actually run locally: Gemma 4 vs Qwen 3.5 Benchmarks
• Full Gemma 4 benchmark breakdown: Gemma 4 Benchmarks: Arena Elo, MMLU & Coding Scores