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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. GLM 5V TurboZ.AIRemove
  2. Claude Sonnet 5.5AnthropicRemove
  3. GPT-6.1 SolOpenAIRemove
glm-5v-turbo vs claude-sonnet-5.5 vs gpt-6.1-sol
AttributeGLM 5V Turboglm-5v-turboClaude Sonnet 5.5claude-sonnet-5.5GPT-6.1 Solgpt-6.1-sol
Pricing
Input$1.20 / 1M$2.00 / 1M$2.00 / 1M
Output$4.00 / 1M$10.00 / 1M$10.00 / 1M
Cache Write (5m)$1.20 / 1M$2.50 / 1M$2.00 / 1M
Cache Write (1h)$1.20 / 1M$4.00 / 1M$2.00 / 1M
Cache Read$1.20 / 1M$0.20 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context202.8K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderZ.AIAnthropicOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryGLM-5V-Turbo is Z.ai's first native multimodal agent foundation model, designed for vision-based coding and agent-driven workflows. It natively supports image, video, and text inputs, enabling integrated multimodal reasoning and execution. The model excels at long-horizon planning, complex coding, and multi-step task execution, and works seamlessly with agents to complete the full loop of “perceive → plan → execute”, making it well suited for advanced multimodal automation and real-world agent systems.Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor.GPT-6.1 Sol is an upgraded high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra. It is optimized for agentic coding, computer use, document-heavy professional work, and multi-step business automation, delivering near-Astra-level capability at significantly lower cost. Compared with GPT-6 Sol, it offers improved factual reliability and stronger adherence to explicit constraints and user intent, making it well suited for complex, long-running agentic workflows where accurate and dependable execution is critical.