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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. Qwen3 Next 80B A3B ThinkingAlibabaRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Gemini 4 ArgonGoogleRemove
qwen3-next-80b-a3b-thinking vs gpt-6.1-terra vs gemini-4-argon
AttributeQwen3 Next 80B A3B Thinkingqwen3-next-80b-a3b-thinkingGPT-6.1 Terragpt-6.1-terraGemini 4 Argongemini-4-argon
Pricing
Input$0.10 / 1M$1.00 / 1M$2.00 / 1M
Output$0.40 / 1M$5.00 / 1M$10.00 / 1M
Cache Write (5m)$0.10 / 1M$1.00 / 1M$2.00 / 1M
Cache Write (1h)$0.10 / 1M$1.00 / 1M$2.00 / 1M
Cache Read$0.10 / 1M$1.00 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context262.1K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesNoYes
Catalogue
ProviderAlibabaOpenAIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryQwen3-Next-80B-A3B-Thinking is a reasoning-first chat model designed for difficult multi-step tasks such as math proofs, code synthesis and debugging, logical reasoning, and agentic planning. It outputs structured thinking traces by default, emphasizing stability over long chains of thought, efficient inference scaling, and strong instruction following. Suited for agent frameworks, tool use, retrieval-heavy workflows, and benchmarks requiring step-by-step solutions, it supports long detailed outputs and faster generation via throughput-optimized techniques, operating exclusively in thinking mode.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.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.