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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. Gemini 3.1 Flash Lite Image (Google Vertex AI)GoogleRemove
  2. GPT-5 CodexOpenAIRemove
  3. Muse WonderMetaRemove
gemini-3.1-flash-lite-image-vertex vs gpt-5-codex vs muse-wonder
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexGPT-5 Codexgpt-5-codexMuse Wondermuse-wonder
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$0.625 / 1M$4.00 / 1M
Cache Write (1h)Not applicable$0.625 / 1M$4.00 / 1M
Cache ReadNot applicable$0.625 / 1M$4.00 / 1M
Input—$0.625 / 1M$4.00 / 1M
Output—$5.00 / 1M$20.00 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A400K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleOpenAIMeta
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released———
Description
SummaryGemini 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.GPT-5 Codex is a coding-focused version of GPT-5 built for both interactive development and long autonomous engineering tasks. It can create projects, add features, debug, refactor, and review code, producing cleaner and more controllable outputs than GPT-5. It integrates with developer tools (CLI, IDEs, GitHub, cloud), supports adjustable reasoning effort, handles multimodal inputs, and uses tools for search and environment setup — making it purpose-built for agentic coding workflows.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.