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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. GPT-6.1 TerraOpenAIRemove
  2. Mistral Embed 2312Mistral AIRemove
  3. Gemini 4 ArgonGoogleRemove
gpt-6.1-terra vs mistral-embed-2312 vs gemini-4-argon
AttributeGPT-6.1 Terragpt-6.1-terraMistral Embed 2312mistral-embed-2312Gemini 4 Argongemini-4-argon
Pricing
Input$1.00 / 1M$0.125 / 1M$2.00 / 1M
Output$5.00 / 1M— Not priced per output token$10.00 / 1M
Cache Write (5m)$1.00 / 1M$0.125 / 1M$2.00 / 1M
Cache Write (1h)$1.00 / 1M$0.125 / 1M$2.00 / 1M
Cache Read$1.00 / 1M$0.125 / 1M$2.00 / 1M
Web Search$0 / 1M—$0 / 1M
Context
Max context1M8.2K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingNoNoYes
Catalogue
ProviderOpenAIMistral AIGoogle
Categorychatembeddingchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryGPT-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.Mistral Embed is Mistral AI's text embedding model, built for semantic search and RAG workflows. It generates 1024-dimensional vectors that capture meaningful relationships between pieces of text.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.