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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 V3.2 SpecialeDeepSeekRemove
  2. Claude Fable 5.1AnthropicRemove
  3. GLM 5.3 FlashZ.AIRemove
deepseek-v3.2-speciale vs claude-fable-5.1 vs glm-5.3-flash
AttributeDeepSeek V3.2 Specialedeepseek-v3.2-specialeClaude Fable 5.1claude-fable-5.1GLM 5.3 Flashglm-5.3-flash
Pricing
Input$0.28 / 1M$10.00 / 1M$0.075 / 1M
Output$0.40 / 1M$50.00 / 1M$0.25 / 1M
Cache Write (5m)$0.28 / 1M$12.50 / 1M$0.075 / 1M
Cache Write (1h)$0.28 / 1M$20.00 / 1M$0.075 / 1M
Cache Read$0.28 / 1M$1.00 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context163.8K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderDeepSeekAnthropicZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryDeepSeek-V3.2-Speciale is a high-compute edition of V3.2 built for top-tier reasoning and agent performance. Using DeepSeek Sparse Attention and extensive reinforcement learning, it surpasses GPT-5 on tough reasoning benchmarks and approaches Gemini 3 Pro–level capability, while still remaining strong at coding and tool use. It also draws on a large agent-training pipeline to boost reliability and generalization in interactive environments.Claude Fable 5.1 is an upgraded version of Fable 5, delivering broad improvements with particularly strong gains in agentic coding, long-running workflows, and professional knowledge work. It excels at large code refactors, front-end and visual code generation, financial analysis, and complex analytical tasks. Compared with Fable 5, it also produces more concise plans and summaries while maintaining strong performance across extended tasks, making it a natural upgrade for existing Fable workflows and a strong option alongside Opus 5 for reasoning-intensive applications.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.