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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. GLM 4.7 FlashZ.AIRemove
  3. Muse Spark 1.3MetaRemove
deepseek-v4.1-flash vs glm-4.7-flash vs muse-spark-1.3
AttributeDeepSeek V4.1 Flashdeepseek-v4.1-flashGLM 4.7 Flashglm-4.7-flashMuse Spark 1.3muse-spark-1.3
Pricing
Input$0.30 / 1M$0.06 / 1M$1.25 / 1M
Output$1.20 / 1M$0.40 / 1M$4.25 / 1M
Cache Write (5m)$0.30 / 1M$0.06 / 1M$1.25 / 1M
Cache Write (1h)$0.30 / 1M$0.06 / 1M$1.25 / 1M
Cache Read$0.30 / 1M$0.06 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M200K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderDeepSeekZ.AIMeta
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.GLM-4.7-Flash is a state-of-the-art 30B-class model designed to strike a strong balance between performance and efficiency. It is specifically optimized for agentic coding scenarios, with enhanced capabilities in code generation, long-horizon task planning, and tool-based collaboration. Among open-source models of comparable size, GLM-4.7-Flash has achieved leading results on multiple public benchmark leaderboards, establishing itself as a competitive and practical choice for advanced developer and agent 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.