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.
| Attribute | Qwen3 Coder Nextqwen3-coder-next | Muse Spark 1.3muse-spark-1.3 |
|---|---|---|
| Pricing | ||
| Input | $0.175 / 1M | $1.25 / 1M |
| Output | $1.40 / 1M | $4.25 / 1M |
| Cache Write (5m) | $0.175 / 1M | $1.25 / 1M |
| Cache Write (1h) | $0.175 / 1M | $1.25 / 1M |
| Cache Read | $0.175 / 1M | $1.25 / 1M |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 262.1K | 1M |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | Yes |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | Alibaba | Meta |
| Category | chat | chat |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Qwen3-Coder-Next is an open-weight causal language model purpose-built for coding agents and local development workflows. It employs a sparse Mixture-of-Experts (MoE) architecture with 80B total parameters and only 3B activated per token, achieving performance comparable to models with 10–20× higher active compute. This efficiency makes it especially well suited for cost-sensitive, always-on agent deployments. Trained with a strong agentic focus, Qwen3-Coder-Next performs reliably on long-horizon coding tasks, complex tool interactions, and robust recovery from execution failures. With a native 256K context window, it integrates smoothly into real-world CLI and IDE environments and aligns well with common agent scaffolding used by modern coding tools. The model operates exclusively in non-thinking mode and does not emit <think> blocks, simplifying production integration for coding agents. | Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows. |