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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. Claude Sonnet 5.5AnthropicRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. Gemini 3.5 TranscribeGoogleRemove
  4. DeepSeek V4.1 FlashDeepSeekRemove

4 is the maximum. Remove one to add another.

claude-sonnet-5.5 vs perceptron-mk1.5 vs gemini-3.5-transcribe vs deepseek-v4.1-flash
AttributeClaude Sonnet 5.5claude-sonnet-5.5Perceptron Mk1.5perceptron-mk1.5Gemini 3.5 Transcribegemini-3.5-transcribeDeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input$2.00 / 1M$0.15 / 1M$0 / 1M$0.30 / 1M
Output$10.00 / 1M$1.50 / 1M$0 / 1M$1.20 / 1M
Cache Write (5m)$2.50 / 1M$0.15 / 1MNot applicable$0.30 / 1M
Cache Write (1h)$4.00 / 1M$0.15 / 1MNot applicable$0.30 / 1M
Cache Read$0.20 / 1M$0.15 / 1MNot applicable$0.30 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M36.9K98.3K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoNoYes
StreamingYesYesNoYes
Catalogue
ProviderAnthropicPerceptronGoogleDeepSeek
Categorychatchatvoicechat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released—2026-09-252026-09-25—
Description
SummaryClaude 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.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.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.