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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. GLM 5Z.AIRemove
  2. Transcribe 1 ProFish AudioRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

glm-5 vs transcribe-1-pro vs nemotron-3.5-super-vl vs muse-wonder
AttributeGLM 5glm-5Transcribe 1 Protranscribe-1-proNemotron 3.5 Super VLnemotron-3.5-super-vlMuse Wondermuse-wonder
Pricing
Input$0.60 / 1M— Not priced per input token$0.10 / 1M$4.00 / 1M
Output$2.20 / 1M— Not priced per output token$0.40 / 1M$20.00 / 1M
Cache Write (5m)$0.60 / 1MNot applicable$0.10 / 1M$4.00 / 1M
Cache Write (1h)$0.60 / 1MNot applicable$0.10 / 1M$4.00 / 1M
Cache Read$0.60 / 1MNot applicable$0.10 / 1M$4.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context202.8KN/A1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderZ.AIFish AudioNVIDIAMeta
Categorychatvoicechatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released—2026-09-24——
Description
SummaryGLM-5 is Z.AI's flagship open-source foundation model, engineered for complex systems design and long-horizon agent workflows. Built with expert developers in mind, it delivers production-grade performance on large-scale programming tasks, rivaling leading closed-source models. With strong agentic planning, deep backend reasoning, and iterative self-correction capabilities, GLM-5 extends beyond traditional code generation to support full-system construction and autonomous execution, making it well suited for advanced engineering and agent-driven development environments.Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision.