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
Muse Spark 1.3Meta Remove
Perceptron Mk1.5Perceptron Remove
Gemini 4 ArgonGoogle Remove

4 is the maximum. Remove one to add another.

qwen3.8-27b vs muse-spark-1.3 vs perceptron-mk1.5 vs gemini-4-argon
Attribute	Qwen3.8 27Bqwen3.8-27b	Muse Spark 1.3muse-spark-1.3	Perceptron Mk1.5perceptron-mk1.5	Gemini 4 Argongemini-4-argon
Pricing
Input	$0.45 / 1M	$1.25 / 1M	$0.15 / 1M	$2.00 / 1M
Output	$3.20 / 1M	$4.25 / 1M	$1.50 / 1M	$10.00 / 1M
Cache Write (5m)	$0.45 / 1M	$1.25 / 1M	$0.15 / 1M	$2.00 / 1M
Cache Write (1h)	$0.45 / 1M	$1.25 / 1M	$0.15 / 1M	$2.00 / 1M
Cache Read	$0.45 / 1M	$1.25 / 1M	$0.15 / 1M	$2.00 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	262K	1M	36.9K	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	No	Yes
Function Calling	Yes	Yes	No	Yes
JSON Mode	Yes	Yes	No	Yes
Streaming	Yes	Yes	Yes	Yes
Catalogue
Provider	Alibaba	Meta	Perceptron	Google
Category	chat	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	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.	Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.	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.