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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. Qwen3.8 27BAlibabaRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
  4. Muse Spark OpenMetaRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs qwen3.8-27b vs nemotron-3.5-super-vl vs muse-spark-open
AttributePerceptron Mk1.5perceptron-mk1.5Qwen3.8 27Bqwen3.8-27bNemotron 3.5 Super VLnemotron-3.5-super-vlMuse Spark Openmuse-spark-open
Pricing
Input$0.15 / 1M$0.45 / 1M$0.10 / 1M$1.25 / 1M
Output$1.50 / 1M$3.20 / 1M$0.40 / 1M$4.25 / 1M
Cache Write (5m)$0.15 / 1M$0.45 / 1M$0.10 / 1M$1.25 / 1M
Cache Write (1h)$0.15 / 1M$0.45 / 1M$0.10 / 1M$1.25 / 1M
Cache Read$0.15 / 1M$0.45 / 1M$0.10 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context36.9K262K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderPerceptronAlibabaNVIDIAMeta
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.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.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.Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.