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.

Qwen3.8 27BAlibaba Remove
Perceptron Mk1.5Perceptron Remove
Nemotron 3.5 Super VL (Free)NVIDIA Remove
GLM 5.3 FlashZ.AI Remove

4 is the maximum. Remove one to add another.

qwen3.8-27b vs perceptron-mk1.5 vs nemotron-3.5-super-vl:free vs glm-5.3-flash
Attribute	Qwen3.8 27Bqwen3.8-27b	Perceptron Mk1.5perceptron-mk1.5	Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free	GLM 5.3 Flashglm-5.3-flash
Pricing
Input	$0.45 / 1M	$0.15 / 1M	$0 / 1M	$0.075 / 1M
Output	$3.20 / 1M	$1.50 / 1M	$0 / 1M	$0.25 / 1M
Cache Write (5m)	$0.45 / 1M	$0.15 / 1M	—	$0.075 / 1M
Cache Write (1h)	$0.45 / 1M	$0.15 / 1M	—	$0.075 / 1M
Cache Read	$0.45 / 1M	$0.15 / 1M	$0 / 1M	$0.075 / 1M
Web Search	$0 / 1M	$0 / 1M	—	$0 / 1M
Cache Write	—	—	$0 / 1M	—
Context
Max context	262K	36.9K	1M	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	No	Yes	Yes
Function Calling	Yes	No	Yes	Yes
JSON Mode	Yes	No	Yes	Yes
Streaming	Yes	Yes	Yes	Yes
Catalogue
Provider	Alibaba	Perceptron	NVIDIA	Z.AI
Category	chat	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Free	Pay As You Go
Released	—	2026-09-25	—	—
Description
Summary	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.	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.	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.	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.