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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. Tongyi DeepResearch 30B A3BAlibabaRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Muse WonderMetaRemove
tongyi-deepresearch-30b-a3b vs gpt-6.1-terra vs muse-wonder
AttributeTongyi DeepResearch 30B A3Btongyi-deepresearch-30b-a3bGPT-6.1 Terragpt-6.1-terraMuse Wondermuse-wonder
Pricing
Input$0.135 / 1M$1.00 / 1M$4.00 / 1M
Output$0.675 / 1M$5.00 / 1M$20.00 / 1M
Cache Write (5m)$0.135 / 1M$1.00 / 1M$4.00 / 1M
Cache Write (1h)$0.135 / 1M$1.00 / 1M$4.00 / 1M
Cache Read$0.135 / 1M$1.00 / 1M$4.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context131.1K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesNoYes
Catalogue
ProviderAlibabaOpenAIMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryTongyi DeepResearch is a 30B-parameter agentic model (3B active per token) built for long-horizon, deep research and information-seeking tasks. It achieves state-of-the-art results on major agentic search and reasoning benchmarks, outperforming prior models in complex multi-step problem solving. Trained with a fully automated synthetic data pipeline and advanced on-policy RL, it supports ReAct workflows and a high-performance “Heavy” mode for test-time scaling, making it well suited for advanced research agents, tool use, and intensive inference workloads.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.Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision.