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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 Super VL (Free)NVIDIARemove
  3. Qwen3.8 27BAlibabaRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

gpt-6.1-sol vs nemotron-3.5-super-vl:free vs qwen3.8-27b vs muse-wonder
AttributeGPT-6.1 Solgpt-6.1-solNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:freeQwen3.8 27Bqwen3.8-27bMuse Wondermuse-wonder
Pricing
Input$2.00 / 1M$0 / 1M$0.45 / 1M$4.00 / 1M
Output$10.00 / 1M$0 / 1M$3.20 / 1M$20.00 / 1M
Cache Write (5m)$2.00 / 1M—$0.45 / 1M$4.00 / 1M
Cache Write (1h)$2.00 / 1M—$0.45 / 1M$4.00 / 1M
Cache Read$2.00 / 1M$0 / 1M$0.45 / 1M$4.00 / 1M
Web Search$0 / 1M—$0 / 1M$0 / 1M
Cache Write—$0 / 1M——
Context
Max context1M1M262K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderOpenAINVIDIAAlibabaMeta
Categorychatchatchatchat
Charge typePay As You GoFreePay 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 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.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.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.