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 Opus 5.5Anthropic Remove
Muse Spark 1.3Meta Remove
Gemini 3.5 TranscribeGoogle Remove
S2 ProFish Audio Remove

4 is the maximum. Remove one to add another.

claude-opus-5.5 vs muse-spark-1.3 vs gemini-3.5-transcribe vs s2-pro
Attribute	Claude Opus 5.5claude-opus-5.5	Muse Spark 1.3muse-spark-1.3	Gemini 3.5 Transcribegemini-3.5-transcribe	S2 Pros2-pro
Pricing
Input	$4.00 / 1M	$1.25 / 1M	$0 / 1M	$0 / 1M
Output	$20.00 / 1M	$4.25 / 1M	$0 / 1M	$0 / 1M
Cache Write (5m)	$5.00 / 1M	$1.25 / 1M	Not applicable	Not applicable
Cache Write (1h)	$8.00 / 1M	$1.25 / 1M	Not applicable	Not applicable
Cache Read	$0.40 / 1M	$1.25 / 1M	Not applicable	Not applicable
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	1M	98.3K	N/A
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	Yes	No
Function Calling	Yes	Yes	Yes	No
JSON Mode	Yes	Yes	No	No
Streaming	Yes	Yes	No	No
Catalogue
Provider	Anthropic	Meta	Google	Fish Audio
Category	chat	chat	voice	voice
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	Claude Opus 5.5 is Anthropic's flagship model for advanced reasoning, coding, and long-horizon agentic workflows, succeeding Opus 5. It excels at multi-step changes across large codebases, code review and bug detection, financial and scientific analysis, and understanding dense charts, diagrams, and screenshots, with stronger grounding when reporting figures and citing sources. Compared with Opus 5, it completes comparable tasks with fewer steps and lower token usage while providing clearer, more concise progress reporting. With adaptive thinking and configurable effort levels, Opus 5.5 can balance reasoning depth, latency, and cost, making it well suited for both demanding autonomous workflows and latency-sensitive professional tasks.	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.	Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.	Fish Audio S2 Pro text-to-speech. Billed per UTF-8 byte of input text.