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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. Gemini 3.5 TranscribeGoogleRemove
  2. Nemotron 3.5 Super VLNVIDIARemove
  3. Qwen3.8 2.4T A95BAlibabaRemove
gemini-3.5-transcribe vs nemotron-3.5-super-vl vs qwen3.8-2.4t-a95b
AttributeGemini 3.5 Transcribegemini-3.5-transcribeNemotron 3.5 Super VLnemotron-3.5-super-vlQwen3.8 2.4T A95Bqwen3.8-2.4t-a95b
Pricing
Input— Not priced per input token$0.10 / 1M$1.80 / 1M
Output— Not priced per output token$0.40 / 1M$5.40 / 1M
Cache Write (5m)Not applicable$0.10 / 1M$1.80 / 1M
Cache Write (1h)Not applicable$0.10 / 1M$1.80 / 1M
Cache ReadNot applicable$0.10 / 1M$1.80 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context98.3K1M262K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleNVIDIAAlibaba
Categoryvoicechatchat
Charge typePay 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.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 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights.