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
Gemini 3.5 TranscribeGoogle Remove
Perceptron Mk1.5Perceptron Remove
MiniMax M2MiniMax Remove

4 is the maximum. Remove one to add another.

claude-sonnet-5.5 vs gemini-3.5-transcribe vs perceptron-mk1.5 vs minimax-m2
Attribute	Claude Sonnet 5.5claude-sonnet-5.5	Gemini 3.5 Transcribegemini-3.5-transcribe	Perceptron Mk1.5perceptron-mk1.5	MiniMax M2minimax-m2
Pricing
Input	$2.00 / 1M	— Not priced per input token	$0.15 / 1M	$0.15 / 1M
Output	$10.00 / 1M	— Not priced per output token	$1.50 / 1M	$0.45 / 1M
Cache Write (5m)	$2.50 / 1M	Not applicable	$0.15 / 1M	$0.15 / 1M
Cache Write (1h)	$4.00 / 1M	Not applicable	$0.15 / 1M	$0.15 / 1M
Cache Read	$0.20 / 1M	Not applicable	$0.15 / 1M	$0.15 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	98.3K	36.9K	196.6K
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	No	No	Yes
Streaming	Yes	No	Yes	Yes
Catalogue
Provider	Anthropic	Google	Perceptron	MiniMax
Category	chat	voice	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	2026-09-25	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.	Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.	MiniMax-M2 is a compact, high-efficiency model with 10B active (230B total) parameters, optimized for coding and agentic workflows. It delivers near-frontier reasoning and tool use, excels at multi-file coding tasks and compile-run-fix loops, and performs strongly on benchmarks like SWE-Bench and Terminal-Bench. It also handles long-horizon planning and recovery in agent evaluations, ranking among the top open models across reasoning domains. With fast inference and low cost, it’s ideal for large-scale agents and developer assistants — and works best when reasoning is preserved across turns.