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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. GPT-6.1 TerraOpenAIRemove
  2. Muse WonderMetaRemove
  3. Gemini 4 ArgonGoogleRemove
  4. Qwen3.6-35B-A3BAlibabaRemove

4 is the maximum. Remove one to add another.

gpt-6.1-terra vs muse-wonder vs gemini-4-argon vs qwen3.6-35b-a3b
AttributeGPT-6.1 Terragpt-6.1-terraMuse Wondermuse-wonderGemini 4 Argongemini-4-argonQwen3.6-35B-A3Bqwen3.6-35b-a3b
Pricing
Input$1.00 / 1M$4.00 / 1M$2.00 / 1M$5.40 / 1M
Output$5.00 / 1M$20.00 / 1M$10.00 / 1M$32.40 / 1M
Cache Write (5m)$1.00 / 1M$4.00 / 1M$2.00 / 1M$5.40 / 1M
Cache Write (1h)$1.00 / 1M$4.00 / 1M$2.00 / 1M$5.40 / 1M
Cache Read$1.00 / 1M$4.00 / 1M$2.00 / 1M$5.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M262.1K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderOpenAIMetaGoogleAlibaba
Categorychatchatchatchat
Charge typePay As You GoPay 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 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.Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.Qwen3.6-35B-A3B is an open-weight Mixture-of-Experts (MoE) multimodal model designed for agentic coding and long-horizon workflows. It features ~35–36B total parameters with ~3B activated per token, enabling strong performance with high inference efficiency. The model supports text and image inputs with a ~260K token context window, and is optimized for repository-level reasoning, multi-step development, and tool-driven workflows. With strong benchmark performance and improved coherence across extended tasks, Qwen3.6-35B-A3B is well suited for developer tools, coding agents, and real-world engineering applications that require both reasoning depth and efficiency.