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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.

  1. GLM 5.3 FlashZ.AIRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. Nemotron 3.5 Content Safety (Free)NVIDIARemove
glm-5.3-flash vs deepseek-v4.1-flash vs nemotron-3.5-content-safety:free
AttributeGLM 5.3 Flashglm-5.3-flashDeepSeek V4.1 Flashdeepseek-v4.1-flashNemotron 3.5 Content Safety (Free)nemotron-3.5-content-safety:free
Pricing
Input$0.075 / 1M$0.30 / 1M$0 / 1M
Output$0.25 / 1M$1.20 / 1M$0 / 1M
Cache Write (5m)$0.075 / 1M$0.30 / 1M
Cache Write (1h)$0.075 / 1M$0.30 / 1M
Cache Read$0.075 / 1M$0.30 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M
Cache Write$0 / 1M
Context
Max context1M1M128K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderZ.AIDeepSeekNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
Released
Description
SummaryGLM-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.DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.NVIDIA Nemotron 3.5 Content Safety is a compact 4B-parameter multimodal guardrail model from NVIDIA, designed for content moderation, safety classification, and AI policy enforcement. Supporting text and image inputs with text output, it evaluates both user prompts and model responses, providing safe/unsafe classifications, safety category labels, and optional reasoning traces. Fine-tuned from Gemma-3-4B and supporting 12 languages with a 128K-token context window, the model is well suited for prompt moderation, response filtering, content classification, and enterprise safety pipelines. As part of the NVIDIA Nemotron family, it offers a configurable reasoning mode and integrates easily into agentic AI systems requiring robust guardrails and compliance controls.