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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. Gemini 3.5 TranscribeGoogleRemove
  3. mai-ds-r1:freeNot in the catalogueRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

deepseek-v4.1-flash vs gemini-3.5-transcribe vs mai-ds-r1:free vs muse-wonder
AttributeDeepSeek V4.1 Flashdeepseek-v4.1-flashGemini 3.5 Transcribegemini-3.5-transcribemai-ds-r1:freeNot in the catalogueMuse Wondermuse-wonder
Pricing
Input$0.30 / 1M— Not priced per input token—$4.00 / 1M
Output$1.20 / 1M— Not priced per output token—$20.00 / 1M
Cache Write (5m)$0.30 / 1MNot applicable—$4.00 / 1M
Cache Write (1h)$0.30 / 1MNot applicable—$4.00 / 1M
Cache Read$0.30 / 1MNot applicable—$4.00 / 1M
Web Search$0 / 1M$0 / 1M—$0 / 1M
Context
Max context1M98.3K—1M
Max outputN/AN/A—N/A
Capabilities
VisionYesYes—Yes
Function CallingYesYes—Yes
JSON ModeYesNo—Yes
StreamingYesNo—Yes
Catalogue
ProviderDeepSeekGoogle—Meta
Categorychatvoice—chat
Charge typePay As You GoPay As You Go—Pay As You Go
Released—2026-09-25——
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.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.—Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision.