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.
- Muse WonderMetaRemove
- Gemini 4 ArgonGoogleRemove
- Perceptron Mk1.5PerceptronRemove
- Nemotron 3.5 Lightning (Free)NVIDIARemove
4 is the maximum. Remove one to add another.
| Attribute | Muse Wondermuse-wonder | Gemini 4 Argongemini-4-argon | Perceptron Mk1.5perceptron-mk1.5 | Nemotron 3.5 Lightning (Free)nemotron-3.5-lightning:free |
|---|---|---|---|---|
| Pricing | ||||
| Input | $4.00 / 1M | $2.00 / 1M | $0.15 / 1M | $0 / 1M |
| Output | $20.00 / 1M | $10.00 / 1M | $1.50 / 1M | $0 / 1M |
| Cache Write (5m) | $4.00 / 1M | $2.00 / 1M | $0.15 / 1M | — |
| Cache Write (1h) | $4.00 / 1M | $2.00 / 1M | $0.15 / 1M | — |
| Cache Read | $4.00 / 1M | $2.00 / 1M | $0.15 / 1M | $0 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | — |
| Cache Write | — | — | — | $0 / 1M |
| Context | ||||
| Max context | 1M | 1M | 36.9K | 1M |
| Max output | N/A | N/A | N/A | N/A |
| Capabilities | ||||
| Vision | Yes | Yes | No | Yes |
| Function Calling | Yes | Yes | No | Yes |
| JSON Mode | Yes | Yes | No | Yes |
| Streaming | Yes | Yes | Yes | Yes |
| Catalogue | ||||
| Provider | Meta | Perceptron | NVIDIA | |
| Category | chat | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go | Free |
| Released | — | — | 2026-09-25 | — |
| Description | ||||
| Summary | 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. | Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token. | 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. |