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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. GLM 5.3Z.AIRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. Nemotron Nano 9B V2 (Free)NVIDIARemove
glm-5.3 vs deepseek-v4.1-flash vs nemotron-nano-9b-v2
AttributeGLM 5.3glm-5.3DeepSeek V4.1 Flashdeepseek-v4.1-flashNemotron Nano 9B V2 (Free)nemotron-nano-9b-v2
Pricing
Input$1.40 / 1M$0.30 / 1M$0 / 1M
Output$4.40 / 1M$1.20 / 1M$0 / 1M
Cache Write (5m)$1.40 / 1M$0.30 / 1M
Cache Write (1h)$1.40 / 1M$0.30 / 1M
Cache Read$1.40 / 1M$0.30 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M
Cache Write$0 / 1M
Context
Max context1M1M131.1K
Max outputN/AN/AN/A
Capabilities
VisionNoYesNo
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderZ.AIDeepSeekNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoFree
Released
Description
SummaryGLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.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.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.