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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. R1T ChimeraTNG TechRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
tng-r1t-chimera vs gpt-6.1-terra vs nemotron-3.5-super-vl
AttributeR1T Chimeratng-r1t-chimeraGPT-6.1 Terragpt-6.1-terraNemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input$0.30 / 1M$1.00 / 1M$0.10 / 1M
Output$1.20 / 1M$5.00 / 1M$0.40 / 1M
Cache Write (5m)$0.30 / 1M$1.00 / 1M$0.10 / 1M
Cache Write (1h)$0.30 / 1M$1.00 / 1M$0.10 / 1M
Cache Read$0.30 / 1M$1.00 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context163.8K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesNoYes
Catalogue
ProviderTNG TechOpenAINVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryTNG-R1T-Chimera is an experimental storytelling-focused LLM derived from the earlier DeepSeek-based Chimera model and available only through Chutes and OpenRouter. It has a friendlier creative personality, higher EQ-Bench scores, stronger intelligence (though slightly slower), clearer separation between reasoning and answers, and better tool calling. The creators also recommend following Microsoft's MAI-DS-R1 usage guidelines when working with the model.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.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.