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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 SolOpenAIRemove
  2. Nemotron 3.5 LightningNVIDIARemove
  3. Muse WonderMetaRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

gpt-6.1-sol vs nemotron-3.5-lightning vs muse-wonder vs gemini-4-argon
AttributeGPT-6.1 Solgpt-6.1-solNemotron 3.5 Lightningnemotron-3.5-lightningMuse Wondermuse-wonderGemini 4 Argongemini-4-argon
Pricing
Input$2.00 / 1M— Not priced per input token$4.00 / 1M$2.00 / 1M
Output$10.00 / 1M— Not priced per output token$20.00 / 1M$10.00 / 1M
Cache Write (5m)$2.00 / 1M$0.00 / 1M$4.00 / 1M$2.00 / 1M
Cache Write (1h)$2.00 / 1M$0.00 / 1M$4.00 / 1M$2.00 / 1M
Cache Read$2.00 / 1M$0.00 / 1M$4.00 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderOpenAINVIDIAMetaGoogle
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released————
Description
SummaryGPT-6.1 Sol is an upgraded high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra. It is optimized for agentic coding, computer use, document-heavy professional work, and multi-step business automation, delivering near-Astra-level capability at significantly lower cost. Compared with GPT-6 Sol, it offers improved factual reliability and stronger adherence to explicit constraints and user intent, making it well suited for complex, long-running agentic workflows where accurate and dependable execution is critical.NVIDIA Nemotron 3.5 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key.Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision.Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.