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.

Claude Sonnet 5.5Anthropic Remove
MAI-Transcribe 2Microsoft Remove
Perceptron Mk1.5Perceptron Remove
DeepSeek V4.1 FlashDeepSeek Remove

4 is the maximum. Remove one to add another.

claude-sonnet-5.5 vs mai-transcribe-2 vs perceptron-mk1.5 vs deepseek-v4.1-flash
Attribute	Claude Sonnet 5.5claude-sonnet-5.5	MAI-Transcribe 2mai-transcribe-2	Perceptron Mk1.5perceptron-mk1.5	DeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input	$2.00 / 1M	$0 / 1M	$0.15 / 1M	$0.30 / 1M
Output	$10.00 / 1M	$0 / 1M	$1.50 / 1M	$1.20 / 1M
Cache Write (5m)	$2.50 / 1M	Not applicable	$0.15 / 1M	$0.30 / 1M
Cache Write (1h)	$4.00 / 1M	Not applicable	$0.15 / 1M	$0.30 / 1M
Cache Read	$0.20 / 1M	Not applicable	$0.15 / 1M	$0.30 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	N/A	36.9K	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	No	No	Yes
Function Calling	Yes	No	No	Yes
JSON Mode	Yes	No	No	Yes
Streaming	Yes	No	Yes	Yes
Catalogue
Provider	Anthropic	Microsoft	Perceptron	DeepSeek
Category	chat	voice	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	2026-09-03	2026-09-25	—
Description
Summary	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.	Microsoft MAI-Transcribe-2 speech-to-text. Billed per second of audio.	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.