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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 3.1 Flash Lite PreviewGoogleRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. GPT-6.1 SolOpenAIRemove
gemini-3.1-flash-lite-preview vs perceptron-mk1.5 vs gpt-6.1-sol
AttributeGemini 3.1 Flash Lite Previewgemini-3.1-flash-lite-previewPerceptron Mk1.5perceptron-mk1.5GPT-6.1 Solgpt-6.1-sol
Pricing
Input$0.25 / 1M$0.15 / 1M$2.00 / 1M
Output$1.50 / 1M$1.50 / 1M$10.00 / 1M
Cache Write (5m)$0.25 / 1M$0.15 / 1M$2.00 / 1M
Cache Write (1h)$0.25 / 1M$0.15 / 1M$2.00 / 1M
Cache Read$0.25 / 1M$0.15 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1.0M36.9K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesYesYes
Catalogue
ProviderGooglePerceptronOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released—2026-09-25—
Description
SummaryGemini 3.1 Flash Lite Preview is Google's high-efficiency model designed for high-volume and cost-sensitive workloads. It improves overall quality compared to Gemini 2.5 Flash Lite while approaching the performance of Gemini 2.5 Flash across key capabilities. The model delivers enhancements in audio input/ASR, RAG snippet ranking, translation, data extraction, and code completion, and supports configurable thinking levels (minimal, low, medium, high) for flexible cost–performance optimization. With pricing at roughly half the cost of Gemini 3 Flash, it is well suited for large-scale production deployments.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.GPT-6.1 Sol is an upgraded high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra. It is optimized for agentic coding, computer use, document-heavy professional work, and multi-step business automation, delivering near-Astra-level capability at significantly lower cost. Compared with GPT-6 Sol, it offers improved factual reliability and stronger adherence to explicit constraints and user intent, making it well suited for complex, long-running agentic workflows where accurate and dependable execution is critical.