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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. GPT-6 Sol ProOpenAIRemove
  3. MiniMax M2MiniMaxRemove
  4. Perceptron Mk1.5PerceptronRemove

4 is the maximum. Remove one to add another.

gemini-3.5-transcribe vs gpt-6-sol-pro vs minimax-m2 vs perceptron-mk1.5
AttributeGemini 3.5 Transcribegemini-3.5-transcribeGPT-6 Sol Progpt-6-sol-proMiniMax M2minimax-m2Perceptron Mk1.5perceptron-mk1.5
Pricing
Input— Not priced per input token$2.00 / 1M$0.15 / 1M$0.15 / 1M
Output— Not priced per output token$10.00 / 1M$0.45 / 1M$1.50 / 1M
Cache Write (5m)Not applicable$2.00 / 1M$0.15 / 1M$0.15 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$0.15 / 1M$0.15 / 1M
Cache ReadNot applicable$2.00 / 1M$0.15 / 1M$0.15 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context98.3K1.1M196.6K36.9K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesNo
Function CallingYesYesYesNo
JSON ModeNoYesYesNo
StreamingNoYesYesYes
Catalogue
ProviderGoogleOpenAIMiniMaxPerceptron
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-25——2026-09-25
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.GPT-6 Sol Pro uses the same underlying model as GPT-6 Sol, but runs with reasoning.mode set to pro for higher-quality responses on complex and demanding tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is particularly well suited for agentic coding, long-horizon software engineering, professional analysis, and complex automated workflows where solution quality takes priority over latency and cost.MiniMax-M2 is a compact, high-efficiency model with 10B active (230B total) parameters, optimized for coding and agentic workflows. It delivers near-frontier reasoning and tool use, excels at multi-file coding tasks and compile-run-fix loops, and performs strongly on benchmarks like SWE-Bench and Terminal-Bench. It also handles long-horizon planning and recovery in agent evaluations, ranking among the top open models across reasoning domains. With fast inference and low cost, it’s ideal for large-scale agents and developer assistants — and works best when reasoning is preserved across turns.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.