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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. Whisper 1OpenAIRemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
gpt-6.1-terra vs whisper-1 vs nemotron-3.5-super-vl:free
AttributeGPT-6.1 Terragpt-6.1-terraWhisper 1whisper-1Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free
Pricing
Input$1.00 / 1M$75.00 / 1M$0 / 1M
Output$5.00 / 1M$75.00 / 1M$0 / 1M
Cache Write (5m)$1.00 / 1MNot applicable—
Cache Write (1h)$1.00 / 1MNot applicable—
Cache Read$1.00 / 1MNot applicable$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesYesYes
StreamingNoNoYes
Catalogue
ProviderOpenAIOpenAINVIDIA
Categorychatvoicechat
Charge typePay As You GoPay As You GoFree
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.Whisper (whisper-1) is OpenAI's open-source automatic speech recognition (ASR) model, designed for audio transcription and translation. It supports 50+ languages and processes audio files up to 25 MB, accepting formats such as mp3, mp4, wav, and webm. Optimized for reliable speech-to-text conversion across diverse audio inputs, Whisper is priced per minute of audio, billed to the nearest second, making it well suited for transcription, localization, and voice-driven applications.NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.