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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. Perceptron Mk1.5PerceptronRemove
  2. GLM 5.3 FlashZ.AIRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
  4. Qwen3.8 27BAlibabaRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs glm-5.3-flash vs nemotron-3.5-super-vl vs qwen3.8-27b
AttributePerceptron Mk1.5perceptron-mk1.5GLM 5.3 Flashglm-5.3-flashNemotron 3.5 Super VLnemotron-3.5-super-vlQwen3.8 27Bqwen3.8-27b
Pricing
Input$0.15 / 1M$0.075 / 1M$0.10 / 1M$0.45 / 1M
Output$1.50 / 1M$0.25 / 1M$0.40 / 1M$3.20 / 1M
Cache Write (5m)$0.15 / 1M$0.075 / 1M$0.10 / 1M$0.45 / 1M
Cache Write (1h)$0.15 / 1M$0.075 / 1M$0.10 / 1M$0.45 / 1M
Cache Read$0.15 / 1M$0.075 / 1M$0.10 / 1M$0.45 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context36.9K1M1M262K
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderPerceptronZ.AINVIDIAAlibaba
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-25———
Description
SummaryPerceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.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.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.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.