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.
- Kimi K3.1 Pro
- DeepSeek V4.1 Flash 1017
- Nemotron 3.5 Super VL
- Gemini 4 Argon
- Muse Wonder
- Gemini 3.1 Flash Lite Image (Google Vertex AI)
- Gemini 3.1 Flash Lite Image (Google AI Studio)Enterprise & partner access
- Gemini 3.1 Flash Image (Google Vertex AI)Enterprise & partner access
| Attribute | Muse Spark Openmuse-spark-open | Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free | Qwen3 30B A3Bqwen3-30b-a3b |
|---|---|---|---|
| Pricing | |||
| Input | $1.25 / 1M | $0 / 1M | $0.05 / 1M |
| Output | $4.25 / 1M | $0 / 1M | $0.15 / 1M |
| Cache Write (5m) | $1.25 / 1M | — | $0.05 / 1M |
| Cache Write (1h) | $1.25 / 1M | — | $0.05 / 1M |
| Cache Read | $1.25 / 1M | $0 / 1M | $0.05 / 1M |
| Web Search | $0 / 1M | — | $0 / 1M |
| Cache Write | — | $0 / 1M | — |
| Context | |||
| Max context | 1M | 1M | 41.0K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | No |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | Meta | NVIDIA | Alibaba |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Free | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential. | 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 is the latest generation in the Qwen large language model series, featuring both dense and Mixture-of-Experts (MoE) architectures designed to excel in reasoning, multilingual understanding, and advanced agentic tasks. A defining capability of Qwen3 is its ability to seamlessly switch between a thinking mode for complex, multi-step reasoning and a non-thinking mode for efficient, high-quality dialogue—delivering strong versatility across use cases. Compared with earlier models such as QwQ and Qwen2.5, Qwen3 demonstrates substantial performance gains in mathematics, coding, commonsense reasoning, creative writing, and interactive conversation. The Qwen3-30B-A3B variant comprises 30.5B total parameters with 3.3B activated, 48 layers, and 128 experts (with 8 activated per task). With support for up to 131K token context lengths via YaRN, it sets a new benchmark for open-source MoE models in both capability and efficiency. |