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 | Nemotron Nano 9B V2 (Free)nemotron-nano-9b-v2 | Hy4 previewhy4-preview | DeepSeek V4.1 Flashdeepseek-v4.1-flash |
|---|---|---|---|
| Pricing | |||
| Input | $0 / 1M | $0.834 / 1M | $0.30 / 1M |
| Output | $0 / 1M | $2.50 / 1M | $1.20 / 1M |
| Cache Write | $0 / 1M | — | — |
| Cache Read | $0 / 1M | $0.834 / 1M | $0.30 / 1M |
| Cache Write (5m) | — | $0.834 / 1M | $0.30 / 1M |
| Cache Write (1h) | — | $0.834 / 1M | $0.30 / 1M |
| Web Search | — | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 131.1K | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | No | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | NVIDIA | Tencent | DeepSeek |
| Category | chat | chat | chat |
| Charge type | Free | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | NVIDIA Nemotron Nano 9B v2 is a 9B-parameter language model trained from scratch by NVIDIA, designed to handle both reasoning and non-reasoning tasks. It can generate an internal reasoning trace before producing a final answer, and this behavior is configurable via system prompts—allowing developers to enable or suppress visible reasoning as needed. | 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. | DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion. |