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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. GPT Image 2OpenAIRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. Muse Spark 1.3MetaRemove
gpt-image-2 vs deepseek-v4.1-flash vs muse-spark-1.3
AttributeGPT Image 2gpt-image-2DeepSeek V4.1 Flashdeepseek-v4.1-flashMuse Spark 1.3muse-spark-1.3
Pricing
Request$0.04 / request
BillingPay Per Request
Cache Write (5m)Not applicable$0.30 / 1M$1.25 / 1M
Cache Write (1h)Not applicable$0.30 / 1M$1.25 / 1M
Cache ReadNot applicable$0.30 / 1M$1.25 / 1M
Input$0.30 / 1M$1.25 / 1M
Output$1.20 / 1M$4.25 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context272K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAIDeepSeekMeta
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released
Description
SummaryGPT Image 2 combines OpenAI's GPT-5.4 with advanced image generation capabilities from GPT Image 2, enabling fully integrated multimodal workflows. It allows users to seamlessly transition between reasoning, coding, and visual generation within a single interaction, making it well suited for creative, development, and agent-driven applications that require both intelligence and visual output.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.