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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. Gemma 4 31B (Free)GoogleRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. GLM 5.3 FlashZ.AIRemove
gemma-4-31b-it:free vs deepseek-v4.1-flash vs glm-5.3-flash
AttributeGemma 4 31B (Free)gemma-4-31b-it:freeDeepSeek V4.1 Flashdeepseek-v4.1-flashGLM 5.3 Flashglm-5.3-flash
Pricing
Input$0 / 1M$0.30 / 1M$0.075 / 1M
Output$0 / 1M$1.20 / 1M$0.25 / 1M
Cache Write$0 / 1M
Cache Read$0 / 1M$0.30 / 1M$0.075 / 1M
Cache Write (5m)$0.30 / 1M$0.075 / 1M
Cache Write (1h)$0.30 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context262.1K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleDeepSeekZ.AI
Categorychatchatchat
Charge typeFreePay As You GoPay As You Go
Released
Description
SummaryGemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model, supporting text and image inputs with text outputs. It features a 256K token context window, configurable thinking/reasoning modes, native function calling, and broad multilingual support across 140+ languages. The model delivers strong performance in coding, reasoning, and document understanding, making it well suited for developer workflows, multilingual applications, and structured knowledge tasks.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.GLM-5.3-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads.