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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. Nemotron 3.5 Super VL (Free)NVIDIARemove
  3. DeepSeek R1T ChimeraTNG TechRemove
gpt-6.1-terra vs nemotron-3.5-super-vl:free vs deepseek-r1t-chimera
AttributeGPT-6.1 Terragpt-6.1-terraNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:freeDeepSeek R1T Chimeradeepseek-r1t-chimera
Pricing
Input$1.00 / 1M$0 / 1M$0.30 / 1M
Output$5.00 / 1M$0 / 1M$1.20 / 1M
Cache Write (5m)$1.00 / 1M—$0.30 / 1M
Cache Write (1h)$1.00 / 1M—$0.30 / 1M
Cache Read$1.00 / 1M$0 / 1M$0.30 / 1M
Web Search$0 / 1M—$0 / 1M
Cache Write—$0 / 1M—
Context
Max context1M1M163.8K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAINVIDIATNG Tech
Categorychatchatchat
Charge typePay As You GoFreePay 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.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.DeepSeek-R1T-Chimera combines DeepSeek-R1 with V3-0324 using a MoE architecture, blending R1’s reasoning strengths with V3's token efficiency. Optimized for general text generation and instruction following, it balances reasoning, efficiency, and usability. The model is MIT-licensed and available for both research and commercial use.