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 | Hy4 previewhy4-preview | GPT-6 Astra Progpt-6-astra-pro |
|---|---|---|---|
| Pricing | |||
| Input | $0.175 / 1M | $0.834 / 1M | $10.00 / 1M |
| Output | $1.40 / 1M | $2.50 / 1M | $50.00 / 1M |
| Cache Write (5m) | $0.175 / 1M | $0.834 / 1M | $10.00 / 1M |
| Cache Write (1h) | $0.175 / 1M | $0.834 / 1M | $10.00 / 1M |
| Cache Read | $0.175 / 1M | $0.834 / 1M | $10.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 262.1K | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | Alibaba | Tencent | OpenAI |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | 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. | Tencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows. | GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost. |