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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. Muse Spark OpenMetaRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. Gemini 4 ArgonGoogleRemove
  4. Perceptron Mk1.5PerceptronRemove

4 is the maximum. Remove one to add another.

muse-spark-open vs deepseek-v4.1-flash vs gemini-4-argon vs perceptron-mk1.5
AttributeMuse Spark Openmuse-spark-openDeepSeek V4.1 Flashdeepseek-v4.1-flashGemini 4 Argongemini-4-argonPerceptron Mk1.5perceptron-mk1.5
Pricing
Input$1.25 / 1M$0.30 / 1M$2.00 / 1M$0.15 / 1M
Output$4.25 / 1M$1.20 / 1M$10.00 / 1M$1.50 / 1M
Cache Write (5m)$1.25 / 1M$0.30 / 1M$2.00 / 1M$0.15 / 1M
Cache Write (1h)$1.25 / 1M$0.30 / 1M$2.00 / 1M$0.15 / 1M
Cache Read$1.25 / 1M$0.30 / 1M$2.00 / 1M$0.15 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M36.9K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesNo
Function CallingYesYesYesNo
JSON ModeYesYesYesNo
StreamingYesYesYesYes
Catalogue
ProviderMetaDeepSeekGooglePerceptron
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released———2026-09-25
Description
SummaryMuse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.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.Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.