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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. S2 ProFish AudioRemove
  2. Muse Spark 1.3MetaRemove
  3. Gemini 3.5 TranscribeGoogleRemove
  4. DeepSeek V4.1 FlashDeepSeekRemove

4 is the maximum. Remove one to add another.

s2-pro vs muse-spark-1.3 vs gemini-3.5-transcribe vs deepseek-v4.1-flash
AttributeS2 Pros2-proMuse Spark 1.3muse-spark-1.3Gemini 3.5 Transcribegemini-3.5-transcribeDeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input$0 / 1M$1.25 / 1M$0 / 1M$0.30 / 1M
Output$0 / 1M$4.25 / 1M$0 / 1M$1.20 / 1M
Cache Write (5m)Not applicable$1.25 / 1MNot applicable$0.30 / 1M
Cache Write (1h)Not applicable$1.25 / 1MNot applicable$0.30 / 1M
Cache ReadNot applicable$1.25 / 1MNot applicable$0.30 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M98.3K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesNoYes
StreamingNoYesNoYes
Catalogue
ProviderFish AudioMetaGoogleDeepSeek
Categoryvoicechatvoicechat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-07-29—2026-09-25—
Description
SummaryFish Audio S2 Pro text-to-speech. Billed per UTF-8 byte of input text.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.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.