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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. Perceptron Mk1.5PerceptronRemove
  3. Claude Sonnet 5.5AnthropicRemove
  4. DeepSeek V4.1 FlashDeepSeekRemove

4 is the maximum. Remove one to add another.

gemini-3.5-transcribe vs perceptron-mk1.5 vs claude-sonnet-5.5 vs deepseek-v4.1-flash
AttributeGemini 3.5 Transcribegemini-3.5-transcribePerceptron Mk1.5perceptron-mk1.5Claude Sonnet 5.5claude-sonnet-5.5DeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input$0 / 1M$0.15 / 1M$2.00 / 1M$0.30 / 1M
Output$0 / 1M$1.50 / 1M$10.00 / 1M$1.20 / 1M
Cache Write (5m)Not applicable$0.15 / 1M$2.50 / 1M$0.30 / 1M
Cache Write (1h)Not applicable$0.15 / 1M$4.00 / 1M$0.30 / 1M
Cache ReadNot applicable$0.15 / 1M$0.20 / 1M$0.30 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context98.3K36.9K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeNoNoYesYes
StreamingNoYesYesYes
Catalogue
ProviderGooglePerceptronAnthropicDeepSeek
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-252026-09-25——
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor.DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.