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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. Muse Spark OpenMetaRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Qwen3 MaxAlibabaRemove
muse-spark-open vs gpt-6.1-terra vs qwen3-max
AttributeMuse Spark Openmuse-spark-openGPT-6.1 Terragpt-6.1-terraQwen3 Maxqwen3-max
Pricing
Input$1.25 / 1M$1.00 / 1M$1.20 / 1M
Output$4.25 / 1M$5.00 / 1M$6.00 / 1M
Cache Write (5m)$1.25 / 1M$1.00 / 1M$1.20 / 1M
Cache Write (1h)$1.25 / 1M$1.00 / 1M$1.20 / 1M
Cache Read$1.25 / 1M$1.00 / 1M$1.20 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M256K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesNoYes
Catalogue
ProviderMetaOpenAIAlibaba
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryMuse 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-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.Qwen3-Max is an updated Qwen3 model with significant gains in reasoning, instruction following, multilingual support, and long-tail knowledge. It improves accuracy in math, coding, logic, and science, reduces hallucinations, and delivers higher-quality open-ended and conversational responses in over 100 languages. Optimized for RAG and tool use, it focuses on reliable final answers rather than a dedicated “thinking” mode.