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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. GPT-6.1 TerraOpenAIRemove
  2. Muse Spark OpenMetaRemove
  3. GPT OSS 20BOpenAIRemove
gpt-6.1-terra vs muse-spark-open vs gpt-oss-20b
AttributeGPT-6.1 Terragpt-6.1-terraMuse Spark Openmuse-spark-openGPT OSS 20Bgpt-oss-20b
Pricing
Input$1.00 / 1M$1.25 / 1M$0.075 / 1M
Output$5.00 / 1M$4.25 / 1M$3.00 / 1M
Cache Write (5m)$1.00 / 1M$1.25 / 1M$0.075 / 1M
Cache Write (1h)$1.00 / 1M$1.25 / 1M$0.075 / 1M
Cache Read$1.00 / 1M$1.25 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M131.1K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAIMetaOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryGPT-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.Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.gpt-oss-20b is an open-weight, 21B-parameter OpenAI model released under Apache 2.0. It uses a Mixture-of-Experts design so only ~3.6B parameters run each step, enabling faster, lower-cost inference on consumer or single-GPU hardware. Trained in the Harmony format, it supports configurable reasoning depth, fine-tuning, function calling, tool use, and structured outputs.