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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. DeepSeek V4.1 Flash 1017DeepSeekRemove
  3. Nemotron 3.5 Lightning (Free)NVIDIARemove
gemini-3.5-transcribe vs deepseek-v4.1-flash-1017 vs nemotron-3.5-lightning:free
AttributeGemini 3.5 Transcribegemini-3.5-transcribeDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017Nemotron 3.5 Lightning (Free)nemotron-3.5-lightning:free
Pricing
Input— Not priced per input token$0.10 / 1M$0 / 1M
Output— Not priced per output token$0.50 / 1M$0 / 1M
Cache Write (5m)Not applicable$0.10 / 1M—
Cache Write (1h)Not applicable$0.10 / 1M—
Cache ReadNot applicable$0.10 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max context98.3K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleDeepSeekNVIDIA
Categoryvoicechatchat
Charge typePay As You GoPay As You GoFree
Released2026-09-25——
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek’s cost-efficient Mixture-of-Experts (MoE) model, built on its innovative Causal Encoder-Decoder (CED) architecture. Featuring 552B total parameters with 8B activated for input and 16B for output, it combines efficient inference with native multimodal understanding through jointly trained text and visual representations. Optimized for coding, terminal and computer-use agents, and long-horizon workflows, the model introduces compressed KV caching that reduces cache memory usage to approximately one-quarter of the previous Flash generation. DeepSeek reports that it surpasses V4 Pro in performance, inference speed, and task completion time, making it well suited for cost-efficient autonomous agents, complex software engineering, and long-context analysis.NVIDIA Nemotron 3.5 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key.