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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Nemotron Nano 9B V2 (Free)NVIDIARemove
  2. DeepSeek V4.1 Flash 1017DeepSeekRemove
nemotron-nano-9b-v2 vs deepseek-v4.1-flash-1017
AttributeNemotron Nano 9B V2 (Free)nemotron-nano-9b-v2DeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017
Pricing
Input$0 / 1M$0.10 / 1M
Output$0 / 1M$0.50 / 1M
Cache Write$0 / 1M—
Cache Read$0 / 1M$0.10 / 1M
Cache Write (5m)—$0.10 / 1M
Cache Write (1h)—$0.10 / 1M
Web Search—$0 / 1M
Context
Max context131.1K1M
Max outputN/AN/A
Capabilities
VisionNoYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderNVIDIADeepSeek
Categorychatchat
Charge typeFreePay As You Go
Released——
Description
SummaryNVIDIA 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.DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek’s cost-efficient Mixture-of-Experts (MoE) model, built on its innovative Causal Encoder-Decoder (CED) architecture. Featuring 552B total parameters with 8B activated for input and 16B for output, it combines efficient inference with native multimodal understanding through jointly trained text and visual representations. Optimized for coding, terminal and computer-use agents, and long-horizon workflows, the model introduces compressed KV caching that reduces cache memory usage to approximately one-quarter of the previous Flash generation. DeepSeek reports that it surpasses V4 Pro in performance, inference speed, and task completion time, making it well suited for cost-efficient autonomous agents, complex software engineering, and long-context analysis.