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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 3.5 TranscribeGoogleRemove
  2. Hy4 previewTencentRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
  4. Qwen3.8 27BAlibabaRemove

4 is the maximum. Remove one to add another.

gemini-3.5-transcribe vs hy4-preview vs nemotron-3.5-super-vl vs qwen3.8-27b
AttributeGemini 3.5 Transcribegemini-3.5-transcribeHy4 previewhy4-previewNemotron 3.5 Super VLnemotron-3.5-super-vlQwen3.8 27Bqwen3.8-27b
Pricing
Input— Not priced per input token$0.834 / 1M$0.10 / 1M$0.45 / 1M
Output— Not priced per output token$2.50 / 1M$0.40 / 1M$3.20 / 1M
Cache Write (5m)Not applicable$0.834 / 1M$0.10 / 1M$0.45 / 1M
Cache Write (1h)Not applicable$0.834 / 1M$0.10 / 1M$0.45 / 1M
Cache ReadNot applicable$0.834 / 1M$0.10 / 1M$0.45 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context98.3K1M1M262K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderGoogleTencentNVIDIAAlibaba
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-25———
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.Tencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows.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.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.