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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. Hy4 previewTencentRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. Muse Spark 1.3MetaRemove
  4. DeepSeek R1 ZeroDeepSeekRemove

4 is the maximum. Remove one to add another.

hy4-preview vs deepseek-v4.1-flash vs muse-spark-1.3 vs deepseek-r1-zero
AttributeHy4 previewhy4-previewDeepSeek V4.1 Flashdeepseek-v4.1-flashMuse Spark 1.3muse-spark-1.3DeepSeek R1 Zerodeepseek-r1-zero
Pricing
Input$0.834 / 1M$0.30 / 1M$1.25 / 1M$0 / 1M
Output$2.50 / 1M$1.20 / 1M$4.25 / 1M$0 / 1M
Cache Write (5m)$0.834 / 1M$0.30 / 1M$1.25 / 1M$0.00 / 1M
Cache Write (1h)$0.834 / 1M$0.30 / 1M$1.25 / 1M$0.00 / 1M
Cache Read$0.834 / 1M$0.30 / 1M$1.25 / 1M$0.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M163.8K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesNo
Function CallingYesYesYesNo
JSON ModeYesYesYesNo
StreamingYesYesYesNo
Catalogue
ProviderTencentDeepSeekMetaDeepSeek
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
SummaryTencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows.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.