Skip to content

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.

  1. Gemini 3.5 TranscribeGoogleRemove
  2. Muse Spark OpenMetaRemove
  3. Gemini 4 HydrogenGoogleRemove
gemini-3.5-transcribe vs muse-spark-open vs gemini-4-hydrogen
AttributeGemini 3.5 Transcribegemini-3.5-transcribeMuse Spark Openmuse-spark-openGemini 4 Hydrogengemini-4-hydrogen
Pricing
Input— Not priced per input token$1.25 / 1M$1.50 / 1M
Output— Not priced per output token$4.25 / 1M$7.50 / 1M
Cache Write (5m)Not applicable$1.25 / 1M$1.50 / 1M
Cache Write (1h)Not applicable$1.25 / 1M$1.50 / 1M
Cache ReadNot applicable$1.25 / 1M$1.50 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context98.3K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleMetaGoogle
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-25——
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.Gemini 4 Hydrogen is Google's high-efficiency multimodal model in the Gemini 4 family, optimized for high-throughput agentic workflows, coding, and multi-model routing. It delivers strong performance at lower cost and latency than Gemini 4 Carbon, supporting text, image, audio, video, and file inputs for versatile multimodal applications. Designed for scalable production workloads and long-horizon execution, Gemini 4 Hydrogen supports up to 1M output tokens through continuation and configurable reasoning effort levels (low, medium, high, and xhigh) to balance reasoning depth, speed, and cost. It is well suited for coding assistants, autonomous agents, complex workflow automation, and large-scale AI systems.