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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. DeepSeek V4.1 FlashDeepSeekRemove
  2. GLM 5.3Z.AIRemove
  3. GPT-4o Audio PreviewOpenAIRemove
deepseek-v4.1-flash vs glm-5.3 vs gpt-4o-audio-preview
AttributeDeepSeek V4.1 Flashdeepseek-v4.1-flashGLM 5.3glm-5.3GPT-4o Audio Previewgpt-4o-audio-preview
Pricing
Input$0.30 / 1M$1.40 / 1M$0.875 / 1M
Output$1.20 / 1M$4.40 / 1M$3.50 / 1M
Cache Write (5m)$0.30 / 1M$1.40 / 1MNot applicable
Cache Write (1h)$0.30 / 1M$1.40 / 1MNot applicable
Cache Read$0.30 / 1M$1.40 / 1MNot applicable
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M128K
Max outputN/AN/AN/A
Capabilities
VisionYesNoNo
Function CallingYesYesNo
JSON ModeYesYesNo
StreamingYesYesYes
Catalogue
ProviderDeepSeekZ.AIOpenAI
Categorychatchatvoice
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryDeepSeek 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.GLM-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.gpt-4o-audio-preview adds support for audio inputs, allowing the model to understand nuances in audio recordings and enrich responses. It currently does not generate audio outputs, and audio input is billed per million audio tokens.