Skip to content

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-5 ProOpenAIRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. Muse Spark 1.3MetaRemove
gpt-5-pro vs deepseek-v4.1-flash vs muse-spark-1.3
AttributeGPT-5 Progpt-5-proDeepSeek V4.1 Flashdeepseek-v4.1-flashMuse Spark 1.3muse-spark-1.3
Pricing
Input$7.50 / 1M$0.30 / 1M$1.25 / 1M
Output$60.00 / 1M$1.20 / 1M$4.25 / 1M
Cache Write (5m)$7.50 / 1M$0.30 / 1M$1.25 / 1M
Cache Write (1h)$7.50 / 1M$0.30 / 1M$1.25 / 1M
Cache Read$7.50 / 1M$0.30 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context400K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIDeepSeekMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-5 Pro is OpenAI's top model, optimized for complex, high-stakes tasks that require careful step-by-step reasoning and precise instruction following. It delivers stronger code quality, clearer writing, and better factual reliability, with support for test-time routing and intent cues like “think hard about this.” It also reduces hallucinations and sycophancy while improving performance across coding, writing, and health-related workloads.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.