Skip to content

Compare models

Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. GLM 5Z.AIRemove
  2. Gemini 4 ArgonGoogleRemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
glm-5 vs gemini-4-argon vs nemotron-3.5-super-vl:free
AttributeGLM 5glm-5Gemini 4 Argongemini-4-argonNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free
Pricing
Input$0.60 / 1M$2.00 / 1M$0 / 1M
Output$2.20 / 1M$10.00 / 1M$0 / 1M
Cache Write (5m)$0.60 / 1M$2.00 / 1M—
Cache Write (1h)$0.60 / 1M$2.00 / 1M—
Cache Read$0.60 / 1M$2.00 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max context202.8K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderZ.AIGoogleNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
Released———
Description
SummaryGLM-5 is Z.AI's flagship open-source foundation model, engineered for complex systems design and long-horizon agent workflows. Built with expert developers in mind, it delivers production-grade performance on large-scale programming tasks, rivaling leading closed-source models. With strong agentic planning, deep backend reasoning, and iterative self-correction capabilities, GLM-5 extends beyond traditional code generation to support full-system construction and autonomous execution, making it well suited for advanced engineering and agent-driven development environments.Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.