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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. mai-ds-r1:freeNot in the catalogueRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. Perceptron Mk1.5PerceptronRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs deepseek-v4.1-flash vs perceptron-mk1.5 vs gemini-4-argon
Attributemai-ds-r1:freeNot in the catalogueDeepSeek V4.1 Flashdeepseek-v4.1-flashPerceptron Mk1.5perceptron-mk1.5Gemini 4 Argongemini-4-argon
Pricing
Input—$0.30 / 1M$0.15 / 1M$2.00 / 1M
Output—$1.20 / 1M$1.50 / 1M$10.00 / 1M
Cache Write (5m)—$0.30 / 1M$0.15 / 1M$2.00 / 1M
Cache Write (1h)—$0.30 / 1M$0.15 / 1M$2.00 / 1M
Cache Read—$0.30 / 1M$0.15 / 1M$2.00 / 1M
Web Search—$0 / 1M$0 / 1M$0 / 1M
Context
Max context—1M36.9K1M
Max output—N/AN/AN/A
Capabilities
Vision—YesNoYes
Function Calling—YesNoYes
JSON Mode—YesNoYes
Streaming—YesYesYes
Catalogue
Provider—DeepSeekPerceptronGoogle
Category—chatchatchat
Charge type—Pay As You GoPay As You GoPay As You Go
Released——2026-09-25—
Description
Summary—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.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.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.