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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. Kimi K3.1 ProMoonShot AIRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Gemma 4 26B A4BGoogleRemove
kimi-k3.1-pro vs gpt-6.1-terra vs gemma-4-26b-a4b-it
AttributeKimi K3.1 Prokimi-k3.1-proGPT-6.1 Terragpt-6.1-terraGemma 4 26B A4Bgemma-4-26b-a4b-it
Pricing
Input$2.00 / 1M$1.00 / 1M$0.13 / 1M
Output$10.00 / 1M$5.00 / 1M$0.40 / 1M
Cache Write (5m)$2.00 / 1M$1.00 / 1M$0.13 / 1M
Cache Write (1h)$2.00 / 1M$1.00 / 1M$0.13 / 1M
Cache Read$2.00 / 1M$1.00 / 1M$0.13 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M262.1K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesNoYes
Catalogue
ProviderMoonShot AIOpenAIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryKimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model, building on Kimi K3 with enhanced post-training for advanced reasoning and autonomous task execution. It features a 1M-token context window and native image and video understanding, enabling complex, long-context multimodal workflows. Optimized for long-horizon coding, 3D creation, financial research, and end-to-end agentic knowledge work, Kimi K3.1 Pro is well suited for autonomous software engineering, multimodal analysis, and complex professional workflows that require sustained reasoning and reliable execution.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.Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind, featuring 25.2B total parameters with only 3.8B activated per token—delivering near 31B-class quality at a fraction of the compute cost. It supports multimodal inputs including text, images, and video (up to 60s at 1fps). The model includes a 256K token context window, native function calling, configurable thinking/reasoning modes, and structured output support. Released under the Apache 2.0 license, it is well suited for efficient, production-ready multimodal and agentic applications.