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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. o4 Mini Deep ResearchOpenAIRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
gpt-6.1-terra vs o4-mini-deep-research vs nemotron-3.5-super-vl
AttributeGPT-6.1 Terragpt-6.1-terrao4 Mini Deep Researcho4-mini-deep-researchNemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input$1.00 / 1M$2.00 / 1M$0.10 / 1M
Output$5.00 / 1M$8.00 / 1M$0.40 / 1M
Cache Write (5m)$1.00 / 1M$2.00 / 1M$0.10 / 1M
Cache Write (1h)$1.00 / 1M$2.00 / 1M$0.10 / 1M
Cache Read$1.00 / 1M$2.00 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M200K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAIOpenAINVIDIA
Categorychatchatchat
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.o4-mini-deep-research is a faster, lower-cost version of OpenAI's deep-research model, designed for complex, multi-step investigations. It automatically relies on web_search for information gathering, which always adds extra usage cost.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.