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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. Gemini 3 Pro Image (Google AI Studio)GoogleRemove
  2. GLM 5.3 FlashZ.AIRemove
  3. GPT-6.1 SolOpenAIRemove
gemini-3-pro-image-aistudio vs glm-5.3-flash vs gpt-6.1-sol
AttributeGemini 3 Pro Image (Google AI Studio)gemini-3-pro-image-aistudioGLM 5.3 Flashglm-5.3-flashGPT-6.1 Solgpt-6.1-sol
Pricing
Request$0.134 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$0.075 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$0.075 / 1M$2.00 / 1M
Cache ReadNot applicable$0.075 / 1M$2.00 / 1M
Input—$0.075 / 1M$2.00 / 1M
Output—$0.25 / 1M$10.00 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleZ.AIOpenAI
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released———
Description
SummaryGemini 3 Pro Image served from Google AI Studio. Billed per image at US$0.134. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI.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-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.