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. Kimi K3.1 ProMoonShot AIRemove
  3. Muse Spark 1.3MetaRemove
gemini-3.5-transcribe vs kimi-k3.1-pro vs muse-spark-1.3
AttributeGemini 3.5 Transcribegemini-3.5-transcribeKimi K3.1 Prokimi-k3.1-proMuse Spark 1.3muse-spark-1.3
Pricing
Input— Not priced per input token$2.00 / 1M$1.25 / 1M
Output— Not priced per output token$10.00 / 1M$4.25 / 1M
Cache Write (5m)Not applicable$2.00 / 1M$1.25 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$1.25 / 1M
Cache ReadNot applicable$2.00 / 1M$1.25 / 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
ProviderGoogleMoonShot AIMeta
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.Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model, building on Kimi K3 with enhanced post-training for advanced reasoning and autonomous task execution. It features a 1M-token context window and native image and video understanding, enabling complex, long-context multimodal workflows. Optimized for long-horizon coding, 3D creation, financial research, and end-to-end agentic knowledge work, Kimi K3.1 Pro is well suited for autonomous software engineering, multimodal analysis, and complex professional workflows that require sustained reasoning and reliable execution.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.