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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. Gemini 4 ArgonGoogleRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Qwen3.5-35B-A3BAlibabaRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

gemini-4-argon vs gpt-6.1-terra vs qwen3.5-35b-a3b vs muse-wonder
AttributeGemini 4 Argongemini-4-argonGPT-6.1 Terragpt-6.1-terraQwen3.5-35B-A3Bqwen3.5-35b-a3bMuse Wondermuse-wonder
Pricing
Input$2.00 / 1M$1.00 / 1M$0.25 / 1M$4.00 / 1M
Output$10.00 / 1M$5.00 / 1M$2.00 / 1M$20.00 / 1M
Cache Write (5m)$2.00 / 1M$1.00 / 1M$0.25 / 1M$4.00 / 1M
Cache Write (1h)$2.00 / 1M$1.00 / 1M$0.25 / 1M$4.00 / 1M
Cache Read$2.00 / 1M$1.00 / 1M$0.25 / 1M$4.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M262.1K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesNoYesYes
Catalogue
ProviderGoogleOpenAIAlibabaMeta
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released————
Description
SummaryGemini 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.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.Qwen3.5-35B-A3B is a native vision-language model built on a hybrid architecture that combines linear attention mechanisms with a sparse Mixture-of-Experts (MoE) design to enhance inference efficiency. It delivers balanced multimodal performance with overall capabilities comparable to Qwen3.5-27B, making it a practical option for efficient vision-language and agent-based applications.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.