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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. Perceptron Mk1.5PerceptronRemove
  2. GPT-6.1 SolOpenAIRemove
  3. Transcribe 1 ProFish AudioRemove
  4. GLM 5V TurboZ.AIRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs gpt-6.1-sol vs transcribe-1-pro vs glm-5v-turbo
AttributePerceptron Mk1.5perceptron-mk1.5GPT-6.1 Solgpt-6.1-solTranscribe 1 Protranscribe-1-proGLM 5V Turboglm-5v-turbo
Pricing
Input$0.15 / 1M$2.00 / 1M— Not priced per input token$1.20 / 1M
Output$1.50 / 1M$10.00 / 1M— Not priced per output token$4.00 / 1M
Cache Write (5m)$0.15 / 1M$2.00 / 1MNot applicable$1.20 / 1M
Cache Write (1h)$0.15 / 1M$2.00 / 1MNot applicable$1.20 / 1M
Cache Read$0.15 / 1M$2.00 / 1MNot applicable$1.20 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context36.9K1MN/A202.8K
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesNoYes
Function CallingNoYesNoYes
JSON ModeNoYesNoYes
StreamingYesYesNoYes
Catalogue
ProviderPerceptronOpenAIFish AudioZ.AI
Categorychatchatvoicechat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-25—2026-09-24—
Description
SummaryPerceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.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.Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.GLM-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.