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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. S2 ProFish AudioRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Gemini 4 HydrogenGoogleRemove
s2-pro vs gpt-6.1-terra vs gemini-4-hydrogen
AttributeS2 Pros2-proGPT-6.1 Terragpt-6.1-terraGemini 4 Hydrogengemini-4-hydrogen
Pricing
Input— Not priced per input token$1.00 / 1M$1.50 / 1M
Output— Not priced per output token$5.00 / 1M$7.50 / 1M
Cache Write (5m)Not applicable$1.00 / 1M$1.50 / 1M
Cache Write (1h)Not applicable$1.00 / 1M$1.50 / 1M
Cache ReadNot applicable$1.00 / 1M$1.50 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoNoYes
Catalogue
ProviderFish AudioOpenAIGoogle
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-07-29——
Description
SummaryFish Audio S2 Pro text-to-speech. Billed per UTF-8 byte of input text.GPT-6.1 Terra is OpenAI’s mid-tier model in the GPT-6.1 series, designed to balance capability, speed, and cost efficiency. Positioned below GPT-6.1 Sol, it delivers strong performance across everyday agentic workflows, coding, and professional writing, with particular strengths in screenshot understanding and on-screen content analysis. Optimized for high-volume production workloads, GPT-6.1 Terra is well suited for coding assistants, visual interface analysis, workflow automation, and scalable agentic applications that require reliable performance at an accessible cost.Gemini 4 Hydrogen is Google's high-efficiency multimodal model in the Gemini 4 family, optimized for high-throughput agentic workflows, coding, and multi-model routing. It delivers strong performance at lower cost and latency than Gemini 4 Carbon, supporting text, image, audio, video, and file inputs for versatile multimodal applications. Designed for scalable production workloads and long-horizon execution, Gemini 4 Hydrogen supports up to 1M output tokens through continuation and configurable reasoning effort levels (low, medium, high, and xhigh) to balance reasoning depth, speed, and cost. It is well suited for coding assistants, autonomous agents, complex workflow automation, and large-scale AI systems.