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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 5.3 FlashZ.AIRemove
  2. Gemini 3.1 Flash Image (Google Vertex AI)GoogleRemove
  3. GPT-6.1 SolOpenAIRemove
glm-5.3-flash vs gemini-3.1-flash-image-vertex vs gpt-6.1-sol
AttributeGLM 5.3 Flashglm-5.3-flashGemini 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertexGPT-6.1 Solgpt-6.1-sol
Pricing
Input$0.075 / 1M—$2.00 / 1M
Output$0.25 / 1M—$10.00 / 1M
Cache Write (5m)$0.075 / 1MNot applicable$2.00 / 1M
Cache Write (1h)$0.075 / 1MNot applicable$2.00 / 1M
Cache Read$0.075 / 1MNot applicable$2.00 / 1M
Web Search$0 / 1M—$0 / 1M
Request—$0.067 / request—
Billing—Pay Per Request—
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderZ.AIGoogleOpenAI
Categorychatimagechat
Charge typePay As You GoPay Per RequestPay As You Go
Released———
Description
SummaryGLM-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.Gemini 3.1 Flash Image served from Google Vertex AI. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI.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.