Skip to content

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. Claude Fable 5.1AnthropicRemove
  2. GLM 5.3 FlashZ.AIRemove
  3. GPT OSS 120BOpenAIRemove
claude-fable-5.1 vs glm-5.3-flash vs gpt-oss-120b
AttributeClaude Fable 5.1claude-fable-5.1GLM 5.3 Flashglm-5.3-flashGPT OSS 120Bgpt-oss-120b
Pricing
Input$10.00 / 1M$0.075 / 1M$0.15 / 1M
Output$50.00 / 1M$0.25 / 1M$0.75 / 1M
Cache Write (5m)$12.50 / 1M$0.075 / 1M$0.15 / 1M
Cache Write (1h)$20.00 / 1M$0.075 / 1M$0.15 / 1M
Cache Read$1.00 / 1M$0.075 / 1M$0.15 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M131.1K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderAnthropicZ.AIOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryClaude 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.gpt-oss-120b is an open-weight 117B-parameter MoE model from OpenAI, built for advanced reasoning and production workloads. Only about 5.1B parameters are active per step, and it’s optimized to run on a single H100 using MXFP4 quantization. It supports adjustable reasoning depth, full chain-of-thought, and native agent features like tool use, function calling, browsing, and structured outputs.