Gemma4All logoGemma4All
Gemma 4GLM-5.2Comparison

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

GLM-5.2 is rising alongside Gemma 4 searches, but it's a 753B MoE coding model — not a local rival. Specs, benchmarks, and memory reality compared.

July 10, 20269 min read

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

SpecGLM-5.2Gemma 4 12BGemma 4 26B A4BGemma 4 31B
ArchitectureMoEDenseMoEDense
Total params~753B12B25.2B30.7B
Active params~40B12B3.8B30.7B
Context window1M tokens256K256K256K
LicenseMITApache 2.0Apache 2.0Apache 2.0
ModalitiesText onlyText, image, audioText, imageText, image
Release dateJune 13, 2026June 3, 2026April 2026April 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:

BenchmarkGLM-5.2Gemma 4 31BEdge
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):

QuantizationFile sizePractical minimum
1-bit (UD-IQ1_S)~223 GB223 GB RAM
2-bit (UD-IQ2_M)~239 GB245 GB
4-bit (UD-Q4_K_XL)372–475 GB372–475 GB
8-bit (UD-Q8_K_XL)~810 GB810 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?