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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.
| Attribute | Nemotron 3 Super (Free)nemotron-3-super-120b-a12b:free | GLM 5.3 Flashglm-5.3-flash |
|---|---|---|
| Pricing | ||
| Input | $0 / 1M | $0.075 / 1M |
| Output | $0 / 1M | $0.25 / 1M |
| Cache Write | $0 / 1M | — |
| Cache Read | $0 / 1M | $0.075 / 1M |
| Cache Write (5m) | — | $0.075 / 1M |
| Cache Write (1h) | — | $0.075 / 1M |
| Web Search | — | $0 / 1M |
| Context | ||
| Max context | 262.1K | 1M |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | Yes |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | NVIDIA | Z.AI |
| Category | chat | chat |
| Charge type | Free | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid Mixture-of-Experts model designed for complex multi-agent and long-horizon reasoning workflows. It activates only 12B parameters per token, enabling high compute efficiency while maintaining strong accuracy on advanced tasks. Built on a hybrid Mamba–Transformer MoE architecture with multi-token prediction (MTP), the model delivers significantly higher token generation throughput than leading open models. It supports a 1M-token context window for long-context reasoning, cross-document analysis, and multi-step task planning. Trained with multi-environment reinforcement learning across diverse benchmarks—including AIME 2025, TerminalBench, and SWE-Bench Verified—Nemotron 3 Super achieves strong performance across reasoning and coding tasks. Released fully open with weights, datasets, and training recipes, it supports flexible customization and secure deployment from local workstations to cloud environments. | GLM-5.3-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads. |