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.

S1Fish Audio Remove
Claude Sonnet 5.5Anthropic Remove
Perceptron Mk1.5Perceptron Remove
DeepSeek V4.1 FlashDeepSeek Remove

4 is the maximum. Remove one to add another.

s1 vs claude-sonnet-5.5 vs perceptron-mk1.5 vs deepseek-v4.1-flash
Attribute	S1s1	Claude Sonnet 5.5claude-sonnet-5.5	Perceptron Mk1.5perceptron-mk1.5	DeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input	$0 / 1M	$2.00 / 1M	$0.15 / 1M	$0.30 / 1M
Output	$0 / 1M	$10.00 / 1M	$1.50 / 1M	$1.20 / 1M
Cache Write (5m)	Not applicable	$2.50 / 1M	$0.15 / 1M	$0.30 / 1M
Cache Write (1h)	Not applicable	$4.00 / 1M	$0.15 / 1M	$0.30 / 1M
Cache Read	Not applicable	$0.20 / 1M	$0.15 / 1M	$0.30 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	N/A	1M	36.9K	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	No	Yes	Yes	Yes
Catalogue
Provider	Fish Audio	Anthropic	Perceptron	DeepSeek
Category	voice	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	2026-07-29	—	2026-09-25	—
Description
Summary	Fish Audio S1 text-to-speech. Billed per UTF-8 byte of input text.	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.	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.	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.