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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 Sol ProOpenAIRemove
  2. Nemotron 3 Super (Free)NVIDIARemove
gpt-6-sol-pro vs nemotron-3-super-120b-a12b:free
AttributeGPT-6 Sol Progpt-6-sol-proNemotron 3 Super (Free)nemotron-3-super-120b-a12b:free
Pricing
Input$2.00 / 1M$0 / 1M
Output$10.00 / 1M$0 / 1M
Cache Write (5m)$2.00 / 1M—
Cache Write (1h)$2.00 / 1M—
Cache Read$2.00 / 1M$0 / 1M
Web Search$0 / 1M—
Cache Write—$0 / 1M
Context
Max context1.1M262.1K
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderOpenAINVIDIA
Categorychatchat
Charge typePay As You GoFree
Released——
Description
SummaryGPT-6 Sol Pro uses the same underlying model as GPT-6 Sol, but runs with reasoning.mode set to pro for higher-quality responses on complex and demanding tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is particularly well suited for agentic coding, long-horizon software engineering, professional analysis, and complex automated workflows where solution quality takes priority over latency and cost.NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid Mixture-of-Experts model designed for complex multi-agent and long-horizon reasoning workflows. It activates only 12B parameters per token, enabling high compute efficiency while maintaining strong accuracy on advanced tasks. Built on a hybrid Mamba–Transformer MoE architecture with multi-token prediction (MTP), the model delivers significantly higher token generation throughput than leading open models. It supports a 1M-token context window for long-context reasoning, cross-document analysis, and multi-step task planning. Trained with multi-environment reinforcement learning across diverse benchmarks—including AIME 2025, TerminalBench, and SWE-Bench Verified—Nemotron 3 Super achieves strong performance across reasoning and coding tasks. Released fully open with weights, datasets, and training recipes, it supports flexible customization and secure deployment from local workstations to cloud environments.