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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. Hy4 previewTencentRemove
  2. Muse Spark 1.3MetaRemove
  3. Mistral Medium 3.5MistralRemove
hy4-preview vs muse-spark-1.3 vs mistral-medium-3-5
AttributeHy4 previewhy4-previewMuse Spark 1.3muse-spark-1.3Mistral Medium 3.5mistral-medium-3-5
Pricing
Input$0.834 / 1M$1.25 / 1M$1.50 / 1M
Output$2.50 / 1M$4.25 / 1M$7.50 / 1M
Cache Write (5m)$0.834 / 1M$1.25 / 1M$1.50 / 1M
Cache Write (1h)$0.834 / 1M$1.25 / 1M$1.50 / 1M
Cache Read$0.834 / 1M$1.25 / 1M$1.50 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M262.1K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderTencentMetaMistral
Categorychatchatchat
Charge typePay 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.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.Mistral Medium 3.5 is a 128B dense instruction-following model from Mistral AI, supporting text and image inputs with text output. It is designed for agentic workflows, coding, and complex multi-step reasoning, with strong reliability in multi-tool orchestration and long-horizon tasks. The model features a 256K token context window, configurable reasoning effort per request, and a custom vision encoder that handles variable image sizes and aspect ratios. With support for self-hosting on as few as four GPUs and availability under open weights, it is well suited for scalable, production-grade deployments.