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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. Codex MiniOpenAIRemove
  2. Muse WonderMetaRemove
  3. Muse Spark OpenMetaRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

codex-mini vs muse-wonder vs muse-spark-open vs gemini-4-argon
AttributeCodex Minicodex-miniMuse Wondermuse-wonderMuse Spark Openmuse-spark-openGemini 4 Argongemini-4-argon
Pricing
Input$0.75 / 1M$4.00 / 1M$1.25 / 1M$2.00 / 1M
Output$3.00 / 1M$20.00 / 1M$4.25 / 1M$10.00 / 1M
Cache Write (5m)$0.75 / 1M$4.00 / 1M$1.25 / 1M$2.00 / 1M
Cache Write (1h)$0.75 / 1M$4.00 / 1M$1.25 / 1M$2.00 / 1M
Cache Read$0.75 / 1M$4.00 / 1M$1.25 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context200K1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderOpenAIMetaMetaGoogle
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released————
Description
SummaryCodex Mini is a fine-tuned o4-mini model made for the Codex CLI. For most direct API usage, it’s better to use gpt-4.1 instead.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.Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.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.