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.

Perceptron Mk1.5Perceptron Remove
Claude Sonnet 5.5Anthropic Remove
S2 ProFish Audio Remove
DeepSeek V4.1 FlashDeepSeek Remove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs claude-sonnet-5.5 vs s2-pro vs deepseek-v4.1-flash
Attribute	Perceptron Mk1.5perceptron-mk1.5	Claude Sonnet 5.5claude-sonnet-5.5	S2 Pros2-pro	DeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input	$0.15 / 1M	$2.00 / 1M	$0 / 1M	$0.30 / 1M
Output	$1.50 / 1M	$10.00 / 1M	$0 / 1M	$1.20 / 1M
Cache Write (5m)	$0.15 / 1M	$2.50 / 1M	Not applicable	$0.30 / 1M
Cache Write (1h)	$0.15 / 1M	$4.00 / 1M	Not applicable	$0.30 / 1M
Cache Read	$0.15 / 1M	$0.20 / 1M	Not applicable	$0.30 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	36.9K	1M	N/A	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	No	Yes	No	Yes
Function Calling	No	Yes	No	Yes
JSON Mode	No	Yes	No	Yes
Streaming	Yes	Yes	No	Yes
Catalogue
Provider	Perceptron	Anthropic	Fish Audio	DeepSeek
Category	chat	chat	voice	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	2026-09-25	—	2026-07-29	—
Description
Summary	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.	Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor.	Fish Audio S2 Pro text-to-speech. Billed per UTF-8 byte of input text.	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.