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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 3.1 Flash Lite Image (Google Vertex AI)GoogleRemove
  3. Muse WonderMetaRemove
deepseek-v4.1-flash vs gemini-3.1-flash-lite-image-vertex vs muse-wonder
AttributeDeepSeek V4.1 Flashdeepseek-v4.1-flashGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexMuse Wondermuse-wonder
Pricing
Input$0.30 / 1M—$4.00 / 1M
Output$1.20 / 1M—$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
Request—$0.0336 / request—
Billing—Pay Per Request—
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderDeepSeekGoogleMeta
Categorychatimagechat
Charge typePay As You GoPay Per RequestPay As You Go
Released———
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.Gemini 3.1 Flash Lite Image served from Google Vertex AI. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 1K. A request for a larger size returns a 1K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size.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.