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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. DeepSeek V4.1 FlashDeepSeekRemove
  2. Grok 4.6xAIRemove
  3. Muse Spark 1.3MetaRemove
deepseek-v4.1-flash vs grok-4.6 vs muse-spark-1.3
AttributeDeepSeek V4.1 Flashdeepseek-v4.1-flashGrok 4.6grok-4.6Muse Spark 1.3muse-spark-1.3
Pricing
Input$0.30 / 1M$2.00 / 1M$1.25 / 1M
Output$1.20 / 1M$6.00 / 1M$4.25 / 1M
Cache Write (5m)$0.30 / 1M$2.00 / 1M$1.25 / 1M
Cache Write (1h)$0.30 / 1M$2.00 / 1M$1.25 / 1M
Cache Read$0.30 / 1M$2.00 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M500K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderDeepSeekxAIMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryDeepSeek 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.Grok 4.6 is SpaceXAI's smartest frontier model, delivering top-tier performance across coding, knowledge work, and STEM reasoning. It is designed for demanding technical and professional workloads that require strong problem solving, accurate instruction following, and reliable execution. Optimized for software engineering, scientific analysis, and complex knowledge tasks, Grok 4.6 is well suited for advanced coding, research, and agentic workflows where high capability and reasoning quality are critical.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.