free tool · model advisor · benchmark-aware
AI Model Comparison Tool
Find models that can give you similar practical results to GLM 5.2 Q1 on the hardware you actually have. This tool compares resident size, active parameter count, quantization level, context length, GPU/RAM fit, and benchmark evidence so you can see when a smaller higher-quant model is a better choice than a giant low-quant model.
Guided setup
Treats less than 64k context as a poor fit because repo agents can burn 30k-40k tokens before user content.
Context floor: 64k · preferred: 128k · usable GPU memory: 27.0 GB · RAM: 64.0 GB
Baseline
GLM 5.2 UD-IQ1_S
754B total · 40.0B active · UD-IQ1_S · 217 GB resident
Current best match
Qwen3 Coder 30B-A3B Q4
79 match score · GPU fit · est. 37.0 tok/s
Guidance
Similar results to GLM 5.2 Q1 usually come from either a smaller MoE at Q4 with a strong coding profile, or another giant MoE at Q2/Q3 if you can tolerate RAM and speed cost.
| Model | Fit | Context | Resident | Active | Quant | Score | Benchmark evidence |
|---|---|---|---|---|---|---|---|
Qwen3 Coder 30B-A3B Q4 Qwen · MOE · 30.5B total / 3.3B active | GPU fit est. 37.0 tok/s | 256k | 19.0 GB | 2.3 GB | Q4_K_M | 79 | Fits far more hardware than GLM and keeps long context for coding agents. Qwen model metadata + WideArea sizing estimate |
Qwen3 Coder Next 80B-A3B Q4 Qwen · MOE · 79.7B total / 3.0B active | MoE spill viable est. 40.7 tok/s | 256k | 49.0 GB | 2.1 GB | Q4_K_M / FP8 class | 76 | Higher quant fidelity and tiny active set make this a strong coding alternative when GLM Q1 quant loss is a concern. Model card architecture + WideArea sizing estimate |
Devstral Small 24B Q5/Q6 Mistral · DENSE · 24.0B total / 24.0B active | GPU fit est. 5.1 tok/s | 384k | 17.0 GB | 17.0 GB | Q5_K_M / Q6_K | 75 | Dense coding model with high quant fidelity and long context. Should fit fully on many GPUs. Model card architecture + WideArea sizing estimate |
GLM-4.5 Air Q4 GLM · MOE · 111B total / 12.0B active | MoE spill viable est. 10.2 tok/s | 128k | 67.0 GB | 7.3 GB | Q4_K_M | 71 | Same family, much smaller resident footprint, and higher quant fidelity than GLM 5.2 Q1. Model card architecture + WideArea sizing estimate |
Nemotron 3 Super 120B-A12B Q4 NVIDIA · MOE · 120B total / 12.0B active | too large est. 14.4 tok/s | 1M | 78.0 GB | 4.5 GB | Q4_K_XL / NVFP4 class | 44 | Public DGX Spark llama.cpp report shows about 14.4 tok/s for Nemotron 3 Super Q4_K_XL. NVIDIA forum report + NVIDIA model card |
GLM 5.2 UD-IQ1_S GLM · MOE · 754B total / 40.0B active | too large est. 3.5 tok/s | 1M | 217 GB | 11.5 GB | UD-IQ1_S | 30 | WideArea local GLM CPU NUMA run: 3.532 tok/s with selective expert cache; public Unsloth quality chart reports about 76.2% top-1 for dynamic 1-bit. WideArea local benchmark + Unsloth quant discussion |
DeepSeek V3.2 Q2/Q3 DeepSeek · MOE · 685B total / 37.0B active | too large est. 3.3 tok/s | 160k | 247 GB | 13.3 GB | Q2_K / Q3_K | 29 | Similar active scale to GLM but usually less context and a different quality profile. Model card architecture + public benchmark summaries |
Qwen option
Qwen3 Coder 30B-A3B Q4
Best practical local coding option on 16GB-24GB class GPUs if quality is good enough.
Qwen option
Qwen3 Coder Next 80B-A3B Q4
Likely much faster than GLM Q1 on mixed desktop GPUs. Comparable for coding, weaker for frontier reasoning.
Mistral option
Devstral Small 24B Q5/Q6
Not frontier-class like GLM, but dense full-GPU fit can beat spilled larger models in real latency.
GLM option
GLM-4.5 Air Q4
Good bridge option when GLM 5.2 Q1 is too slow or too quantized.
Public coding benchmark dataset
Quantized coding benchmark rows
These rows are normalized from public benchmark sources so model options can be compared by actual coding metrics, not just parameter count. The dataset keeps source labels and mixed harness notes because HumanEval, Aider Polyglot, KLD, and speed tests are not interchangeable.
| Model | Quant | Benchmark | Value | Size | Source |
|---|---|---|---|---|---|
| Qwen3.6-27B | UD-IQ2_XXS | HumanEval | 59.76% ± 3.84 | — | LLM Quant Bench |
| Qwen3.6-27B | UD-IQ2_XXS | HumanEval+ | 53.05% ± 3.91 | — | LLM Quant Bench |
| Qwen3.6-27B | UD-IQ2_M | HumanEval | 71.34% ± 3.54 | — | LLM Quant Bench |
| Qwen3.6-27B | UD-IQ2_M | HumanEval+ | 67.07% ± 3.68 | — | LLM Quant Bench |
| Qwen3.6-27B | Q3_K_M | HumanEval | 79.27% ± 3.18 | — | LLM Quant Bench |
| Qwen3.6-27B | Q3_K_M | HumanEval+ | 73.17% ± 3.47 | — | LLM Quant Bench |
| Qwen3.6-27B | Q4_K_S | HumanEval | 84.76% ± 2.82 | — | LLM Quant Bench |
| Qwen3.6-27B | Q4_K_S | HumanEval+ | 76.83% ± 3.3 | — | LLM Quant Bench |
| Qwen3.6-27B | Q4_K_M | HumanEval | 82.32% ± 2.99 | — | LLM Quant Bench |
| Qwen3.6-27B | Q4_K_M | HumanEval+ | 76.83% ± 3.3 | — | LLM Quant Bench |
| Qwen3.6-27B | Q6_K | HumanEval | 83.54% ± 2.9 | — | LLM Quant Bench |
| Qwen3.6-27B | Q6_K | HumanEval+ | 78.66% ± 3.21 | — | LLM Quant Bench |
| Qwen3.6-35B-A3B | UD-IQ1_M | HumanEval | 56.71% ± 3.88 | — | LLM Quant Bench |
| Qwen3.6-35B-A3B | UD-IQ2_M | HumanEval | 50% ± 3.92 | — | LLM Quant Bench |
| Qwen3.6-35B-A3B | UD-Q3_K_M | HumanEval | 61.59% ± 3.81 | — | LLM Quant Bench |
| Qwen3.6-35B-A3B | UD-Q4_K_S | HumanEval | 65.85% ± 3.71 | — | LLM Quant Bench |
| Qwen3.6-35B-A3B | UD-Q4_K_M | HumanEval | 59.15% ± 3.85 | — | LLM Quant Bench |
| Qwen3-Coder-30B-A3B-Instruct | Q3_K_M | Decode speed | 7.5tok/s | 13.7 GB | SmartTasks Qwen3-Coder-30B-A3B GGUF scorecard |
| Qwen3-Coder-30B-A3B-Instruct | Q4_K_M | Decode speed | 9.6tok/s | 17.3 GB | SmartTasks Qwen3-Coder-30B-A3B GGUF scorecard |
| Qwen3-Coder-30B-A3B-Instruct | Q5_K_M | Decode speed | 9.4tok/s | 20.2 GB | SmartTasks Qwen3-Coder-30B-A3B GGUF scorecard |
| Qwen3-Coder-30B-A3B-Instruct | Q8_0 | Decode speed | 7.2tok/s | 30.3 GB | SmartTasks Qwen3-Coder-30B-A3B GGUF scorecard |
| Qwen2.5-Coder-3B | Q4_K_M | HumanEval | 82.3% | 1.9 GB | EchoLabs Qwen2.5-Coder-3B HXQ benchmark |
| Qwen2.5-Coder-3B | Q4_K_M | HumanEval+ | 78% | 1.9 GB | EchoLabs Qwen2.5-Coder-3B HXQ benchmark |
| Qwen2.5-Coder-3B | Q5_K_M | HumanEval | 83.5% | 2.1 GB | EchoLabs Qwen2.5-Coder-3B HXQ benchmark |
| Qwen2.5-Coder-3B | Q5_K_M | HumanEval+ | 75.6% | 2.1 GB | EchoLabs Qwen2.5-Coder-3B HXQ benchmark |
| Qwen2.5-Coder-3B | Q6_K | HumanEval | 83.5% | 2.4 GB | EchoLabs Qwen2.5-Coder-3B HXQ benchmark |
| Qwen2.5-Coder-3B | Q6_K | HumanEval+ | 78% | 2.4 GB | EchoLabs Qwen2.5-Coder-3B HXQ benchmark |
| Qwen2.5-Coder-3B | HXQ_AF6 | HumanEval | 84.1% | 2.3 GB | EchoLabs Qwen2.5-Coder-3B HXQ benchmark |
| Qwen2.5-Coder-3B | HXQ_AF6 | HumanEval+ | 78% | 2.3 GB | EchoLabs Qwen2.5-Coder-3B HXQ benchmark |
| Qwen2.5-Coder-3B | Q4_K_M | Decode speed | 245.03tok/s | 1.9 GB | EchoLabs Qwen2.5-Coder-3B HXQ benchmark |
| Qwen2.5-Coder-3B | HXQ_AF6 | Decode speed | 226.53tok/s | 2.3 GB | EchoLabs Qwen2.5-Coder-3B HXQ benchmark |
| Qwen3.6-35B-A3B | UD-Q4_K_M | Aider Polyglot | 78.67% | 22.1 GB | little-coder Qwen3.6-35B-A3B Aider Polyglot run |
raw-dataset
LLM Quant Bench
Same-harness GGUF quantization comparisons using llama.cpp, lm-evaluation-harness, and inspect_ai. Coding rows include HumanEval and HumanEval+.
model-card
SmartTasks Qwen3-Coder-30B-A3B GGUF scorecard
Model-card scorecard with quant sizes, KLD versus FP16, and generation speed on CPU, RTX 3090, and GTX 1080 Ti.
model-card
EchoLabs Qwen2.5-Coder-3B HXQ benchmark
GGUF runtime benchmark on RTX 3090 comparing Q4/Q5/Q6/HXQ_AF6 with HumanEval, HumanEval+, perplexity, and decode speed.
agent-run
little-coder Qwen3.6-35B-A3B Aider Polyglot run
End-to-end local Aider Polyglot run with Qwen3.6-35B-A3B UD-Q4_K_M through llama.cpp.
How to compare models like GLM 5.2 Q1
The wrong way to compare local AI models is to sort by parameter count or file size. The practical question is: what quality can this hardware deliver at an acceptable speed and context window? GLM 5.2 Q1 is a good example. It is a huge model at a very low quantization, so it may preserve some frontier-model behavior while also suffering quantization loss and high memory pressure.
Resident size is not active size
A dense model and a mixture-of-experts model can have the same resident size in RAM and behave completely differently. A dense model touches most of its weights for every generated token. A MoE model keeps many experts resident but activates only a subset for each token. That makes active parameters a first-class field for CPU servers, mixed-GPU desktops, and any setup where some weights spill into RAM.
| Field | What it tells you | Why it matters |
|---|---|---|
| Resident GB | Memory needed to load the model | Fit and whether full NUMA or GPU placement is possible |
| Active GB | Approximate weights touched per token | Decode speed and memory bandwidth pressure |
| Context | Maximum usable prompt + history | Coding agents and RAG need large windows |
| Quant | Weight precision | Quality loss versus fit and speed |
| Benchmark source | Where the evidence came from | Separates measured data from estimates |
Why context length changes the recommendation
For casual chat, 8k or 16k context can be fine. For coding, less than 32k is usually a weak experience. For Claude Code-style repo agents, less than 64k is often a waste of time because tool instructions, repository summaries, and system prompts can consume 30k-40k tokens before the user task is fully represented. That is why the tool applies a use-case context floor before ranking models.
Rule of thumb: coding assistant = 32k minimum; Claude Code or repo agent = 64k minimum; long-document and RAG workflows should prefer 128k or more.
Dense spill versus MoE spill
If a dense model does not fit in GPU memory, every token may pull large dense layers across PCIe or system RAM. That usually hurts. MoE models are different: cold experts can live in RAM while hot experts and dense shared layers stay on GPU. This is why a desktop with a 16 GB GPU, an 11 GB GPU, and 64 GB of RAM may run some MoE models surprisingly well while still struggling with a dense model of similar resident size.
Using scraped and public benchmarks
Public benchmark data is useful, but it is messy. Some sources report quality scores, some report GPU throughput, some report cloud end-to-end speed, and some report local CPU numbers. The comparison tool stores each benchmark row with a source label and URL so scraped results can be mixed with Wide Area Intelligence local measurements without pretending they are all the same experiment.
GLM 5.2 Q1 versus Qwen higher quants
GLM 5.2 Q1 may be attractive because the base model is large and strong, but Q1 is a severe quantization. A Qwen coding model at Q4 can be much smaller, much faster, and more faithful to its original weights. For coding workloads, the higher-quant specialized model may be the better practical recommendation even if the full-precision GLM model is stronger in theory.
What this first version does
This first version starts with curated public and local benchmark rows: GLM 5.2 Q1 from our local NUMA tests plus public quant-quality data, Qwen speed and model-card data, Nemotron public throughput reports, and model architecture metadata for other comparable MoE options. The next step is to run a scheduled scraper that refreshes the same benchmark row format from model cards, discussions, docs pages, and our own lab results.
What benchmark rows are included now?
The current coding dataset includes HumanEval and HumanEval+ rows from LLM Quant Bench, Qwen3-Coder-30B-A3B GGUF speed and KLD rows from a public model-card scorecard, Qwen2.5-Coder-3B HXQ quant comparisons, and a local-agent Aider Polyglot run for Qwen3.6-35B-A3B. These sources do not all use the same harness, so the tool keeps source labels visible instead of merging every number into a single unqualified score.
Frequently asked questions
- What models are comparable to GLM 5.2 Q1?
- It depends on the workload. For coding, a higher-quant Qwen coder MoE may be a better practical match because it keeps more quantization fidelity and has a much smaller active set. For long-horizon reasoning, other large MoE models such as DeepSeek, Kimi, or Nemotron may be closer, but hardware fit and context length matter.
- Why does active parameter count matter?
- Decode speed depends on the weights touched for each generated token, not just the total model file size. A 300 GB dense model can be far slower than a 300 GB MoE model because the dense model reads most of its weights every token while the MoE model activates only selected experts.
- Is GLM 5.2 Q1 better than a smaller Q4 model?
- Not automatically. GLM 5.2 starts from a very strong base model, but Q1 quantization can damage quality. A smaller Q4 model can beat it on latency, reliability, and sometimes task quality, especially for coding if the smaller model is specialized.
- How much context do I need for Claude Code?
- Treat 64k as the practical floor. Claude Code-style repo agents can consume 30k-40k tokens before the user request is fully represented, so a 16k or 32k model can be technically usable but operationally frustrating.
- Can a model larger than my GPU run well?
- Sometimes. Dense models usually perform poorly when they spill heavily into system RAM. MoE models can be more forgiving because only a subset of experts is active per token, so a GPU plus RAM overflow strategy can work if the active set and hot experts are placed intelligently.
related tools
Related reading: GGUF quantization explained. Ready to use that hardware? Turn your GPU into an OpenAI-compatible endpoint — free for 2 nodes.
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