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. Nova-3DeepgramRemove
  2. Transcribe 1 ProFish AudioRemove
  3. DeepSeek V4.1 FlashDeepSeekRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

nova-3 vs transcribe-1-pro vs deepseek-v4.1-flash vs muse-spark-1.3
AttributeNova-3nova-3Transcribe 1 Protranscribe-1-proDeepSeek V4.1 Flashdeepseek-v4.1-flashMuse Spark 1.3muse-spark-1.3
Pricing
Input$0 / 1M$0 / 1M$0.30 / 1M$1.25 / 1M
Output$0 / 1M$0 / 1M$1.20 / 1M$4.25 / 1M
Cache Write (5m)Not applicableNot applicable$0.30 / 1M$1.25 / 1M
Cache Write (1h)Not applicableNot applicable$0.30 / 1M$1.25 / 1M
Cache ReadNot applicableNot applicable$0.30 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/AN/A1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoNoYesYes
Function CallingNoNoYesYes
JSON ModeNoNoYesYes
StreamingNoNoYesYes
Catalogue
ProviderDeepgramFish AudioDeepSeekMeta
Categoryvoicevoicechatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-07-152026-09-24——
Description
SummaryDeepgram Nova-3 speech-to-text. Billed per second of audio.Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.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.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.