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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 4 ArgonGoogleRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Phi-4MicrosoftRemove
  4. Muse Spark OpenMetaRemove

4 is the maximum. Remove one to add another.

gemini-4-argon vs gpt-6.1-terra vs phi-4 vs muse-spark-open
AttributeGemini 4 Argongemini-4-argonGPT-6.1 Terragpt-6.1-terraPhi-4phi-4Muse Spark Openmuse-spark-open
Pricing
Input$2.00 / 1M$1.00 / 1M$0.21 / 1M$1.25 / 1M
Output$10.00 / 1M$5.00 / 1M$0.42 / 1M$4.25 / 1M
Cache Write (5m)$2.00 / 1M$1.00 / 1M$0.21 / 1M$1.25 / 1M
Cache Write (1h)$2.00 / 1M$1.00 / 1M$0.21 / 1M$1.25 / 1M
Cache Read$2.00 / 1M$1.00 / 1M$0.21 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M16.4K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoYes
Function CallingYesYesNoYes
JSON ModeYesYesNoYes
StreamingYesNoNoYes
Catalogue
ProviderGoogleOpenAIMicrosoftMeta
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released————
Description
SummaryGemini 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.GPT-6.1 Terra is OpenAI’s mid-tier model in the GPT-6.1 series, designed to balance capability, speed, and cost efficiency. Positioned below GPT-6.1 Sol, it delivers strong performance across everyday agentic workflows, coding, and professional writing, with particular strengths in screenshot understanding and on-screen content analysis. Optimized for high-volume production workloads, GPT-6.1 Terra is well suited for coding assistants, visual interface analysis, workflow automation, and scalable agentic applications that require reliable performance at an accessible cost.—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.