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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 26B A4B (Free)GoogleRemove
  2. GLM 5.3Z.AIRemove
  3. DeepSeek V4.1 FlashDeepSeekRemove
gemma-4-26b-a4b-it:free vs glm-5.3 vs deepseek-v4.1-flash
AttributeGemma 4 26B A4B (Free)gemma-4-26b-a4b-it:freeGLM 5.3glm-5.3DeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input$0 / 1M$1.40 / 1M$0.30 / 1M
Output$0 / 1M$4.40 / 1M$1.20 / 1M
Cache Write$0 / 1M
Cache Read$0 / 1M$1.40 / 1M$0.30 / 1M
Cache Write (5m)$1.40 / 1M$0.30 / 1M
Cache Write (1h)$1.40 / 1M$0.30 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context262.1K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleZ.AIDeepSeek
Categorychatchatchat
Charge typeFreePay As You GoPay As You Go
Released
Description
SummaryGemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind, featuring 25.2B total parameters with only 3.8B activated per token—delivering near 31B-class quality at a fraction of the compute cost. It supports multimodal inputs including text, images, and video (up to 60s at 1fps). The model includes a 256K token context window, native function calling, configurable thinking/reasoning modes, and structured output support. Released under the Apache 2.0 license, it is well suited for efficient, production-ready multimodal and agentic applications.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.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.