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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. Hy4 previewTencentRemove
  3. Nemotron 3.5 Lightning (Free)NVIDIARemove
deepseek-v4.1-flash vs hy4-preview vs nemotron-3.5-lightning:free
AttributeDeepSeek V4.1 Flashdeepseek-v4.1-flashHy4 previewhy4-previewNemotron 3.5 Lightning (Free)nemotron-3.5-lightning:free
Pricing
Input$0.30 / 1M$0.834 / 1M$0 / 1M
Output$1.20 / 1M$2.50 / 1M$0 / 1M
Cache Write (5m)$0.30 / 1M$0.834 / 1M
Cache Write (1h)$0.30 / 1M$0.834 / 1M
Cache Read$0.30 / 1M$0.834 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M
Cache Write$0 / 1M
Context
Max context1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderDeepSeekTencentNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
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.Tencent 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.NVIDIA Nemotron 3.5 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key.