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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. DeepSeek V4.1 FlashDeepSeekRemove
  4. Claude Opus 5.5AnthropicRemove

4 is the maximum. Remove one to add another.

gemini-3.5-transcribe vs perceptron-mk1.5 vs deepseek-v4.1-flash vs claude-opus-5.5
AttributeGemini 3.5 Transcribegemini-3.5-transcribePerceptron Mk1.5perceptron-mk1.5DeepSeek V4.1 Flashdeepseek-v4.1-flashClaude Opus 5.5claude-opus-5.5
Pricing
Input$0 / 1M$0.15 / 1M$0.30 / 1M$4.00 / 1M
Output$0 / 1M$1.50 / 1M$1.20 / 1M$20.00 / 1M
Cache Write (5m)Not applicable$0.15 / 1M$0.30 / 1M$5.00 / 1M
Cache Write (1h)Not applicable$0.15 / 1M$0.30 / 1M$8.00 / 1M
Cache ReadNot applicable$0.15 / 1M$0.30 / 1M$0.40 / 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
ProviderGooglePerceptronDeepSeekAnthropic
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.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.Claude Opus 5.5 is Anthropic's flagship model for advanced reasoning, coding, and long-horizon agentic workflows, succeeding Opus 5. It excels at multi-step changes across large codebases, code review and bug detection, financial and scientific analysis, and understanding dense charts, diagrams, and screenshots, with stronger grounding when reporting figures and citing sources. Compared with Opus 5, it completes comparable tasks with fewer steps and lower token usage while providing clearer, more concise progress reporting. With adaptive thinking and configurable effort levels, Opus 5.5 can balance reasoning depth, latency, and cost, making it well suited for both demanding autonomous workflows and latency-sensitive professional tasks.