LLM Comparison
DiffusionGemma vs GLM-5.3-Flash
Side-by-side specs, pricing & capabilities · Updated August 2026
Price vs Intelligence
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2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 36 | 75 463 |
| Family | Gemma | GLM |
| Status | Current | Current |
| Release Date | Jun 2026 | Aug 2026 |
| Context Window | 256K tokens | 1.3M tokens |
| Input Price | Free | $0.07/M tokens |
| Output Price | Free | $0.25/M tokens |
| Pricing Notes | Open-weights model under Apache 2.0; API pricing depends on the host or local infrastructure used. | OpenRouter lists a limited-time discounted price of $0.075/M input and $0.25/M output. Standard first-party style pricing is commonly $0.15/M input and $0.50/M output; cache-read pricing may vary by provider. |
| Capabilities | textvisioncodereasoninglocal-inference | textvisionvideo-inputcodetool-usereasoninglong-contextopen-weights |
| Training Cutoff | — | Not disclosed |
| Max Output | 256 tokens | 131K tokens |
| API Identifier | google/diffusiongemma-26b-a4b-it | z-ai/glm-5.3-flash |
| Benchmarks | ||
| MMLU Pro | 77.6official-google-model-card | — |
| GPQA Diamond | 73.2official-google-model-card | — |
| LiveCodeBench v6 | 69.1official-google-model-card | — |
| MMMLU | 81.5official-google-model-card | — |
| HLE no tools | 11official-google-model-card | — |
| Artificial Analysis Intelligence Index v4.1.1 | — | 57artificial-analysis |
| MMLU | — | 88.1official |
| View DiffusionGemma | View GLM-5.3-Flash | |
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| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| DiffusionGemmaCheapest | $0.00 | $0.00 | $0.00 | — |
| GLM-5.3-Flash | $0.08 | $0.13 | $0.20 | +0% |
DiffusionGemma
DiffusionGemma is Google DeepMind’s experimental open-weights text-diffusion model based on Gemma 4 26B A4B. It uses discrete diffusion and parallel canvas denoising to trade some benchmark quality for much faster local generation on dedicated GPUs.
Z.ai
GLM-5.3-Flash
GLM-5.3-Flash is Z.ai’s open-weight GLM-5 model for efficient coding, long-horizon agent tasks, and long-context multimodal reasoning. It uses a hybrid sparse and linear attention design with 320B total parameters and 18B active parameters.
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