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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. Codestral 2508Mistral AIRemove
  3. Muse WonderMetaRemove
gemini-3.1-flash-lite-image-vertex vs codestral-2508 vs muse-wonder
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexCodestral 2508codestral-2508Muse Wondermuse-wonder
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$0.45 / 1M$4.00 / 1M
Cache Write (1h)Not applicable$0.45 / 1M$4.00 / 1M
Cache ReadNot applicable$0.45 / 1M$4.00 / 1M
Input—$0.45 / 1M$4.00 / 1M
Output—$1.35 / 1M$20.00 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A256K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleMistral AIMeta
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.Codestral is Mistral's cutting-edge language model for coding, released in late July 2025. It is purpose-built for low-latency, high-frequency developer workflows, excelling at tasks such as fill-in-the-middle (FIM) code completion, code correction, and test generation. Optimized for responsiveness and precision, Codestral is well suited for real-time coding assistance, IDE integration, and automated development pipelines where speed and accuracy are critical.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.