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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.

  1. DeepSeek V4.1 FlashDeepSeekRemove
  2. Gemma 3 4BGoogleRemove
  3. GLM 5.3Z.AIRemove
  4. Qwen3.8 27BAlibabaRemove

4 is the maximum. Remove one to add another.

deepseek-v4.1-flash vs gemma-3-4b-it vs glm-5.3 vs qwen3.8-27b
AttributeDeepSeek V4.1 Flashdeepseek-v4.1-flashGemma 3 4Bgemma-3-4b-itGLM 5.3glm-5.3Qwen3.8 27Bqwen3.8-27b
Pricing
Input$0.30 / 1M$0.02 / 1M$1.40 / 1M$0.45 / 1M
Output$1.20 / 1M$0.04 / 1M$4.40 / 1M$3.20 / 1M
Cache Write (5m)$0.30 / 1M$0.02 / 1M$1.40 / 1M$0.45 / 1M
Cache Write (1h)$0.30 / 1M$0.02 / 1M$1.40 / 1M$0.45 / 1M
Cache Read$0.30 / 1M$0.02 / 1M$1.40 / 1M$0.45 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M131.1K1M262K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoNoYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderDeepSeekGoogleZ.AIAlibaba
Categorychatchatchatchat
Charge typePay As You GoPay 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.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.