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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 LunaOpenAIRemove
  2. Qwen3.8 27BAlibabaRemove
  3. Muse WonderMetaRemove
  4. DeepSeek V4.1 Flash 1017DeepSeekRemove

4 is the maximum. Remove one to add another.

gpt-6-luna vs qwen3.8-27b vs muse-wonder vs deepseek-v4.1-flash-1017
AttributeGPT-6 Lunagpt-6-lunaQwen3.8 27Bqwen3.8-27bMuse Wondermuse-wonderDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017
Pricing
Input$0.10 / 1M$0.45 / 1M$4.00 / 1M$0.10 / 1M
Output$0.50 / 1M$3.20 / 1M$20.00 / 1M$0.50 / 1M
Cache Write (5m)$0.10 / 1M$0.45 / 1M$4.00 / 1M$0.10 / 1M
Cache Write (1h)$0.10 / 1M$0.45 / 1M$4.00 / 1M$0.10 / 1M
Cache Read$0.10 / 1M$0.45 / 1M$4.00 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1.1M262K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderOpenAIAlibabaMetaDeepSeek
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released————
Description
SummaryGPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, optimized for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic tasks. It combines low-cost, responsive inference with the GPT-6 family’s improvements in factual reliability and clear, concise communication. At higher reasoning effort, GPT-6 Luna can also handle complex software engineering and computer-use workflows that previously required a Sol-tier model, making it a versatile choice for scalable production applications that need to balance speed, cost, and capability.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.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.DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek’s cost-efficient Mixture-of-Experts (MoE) model, built on its innovative Causal Encoder-Decoder (CED) architecture. Featuring 552B total parameters with 8B activated for input and 16B for output, it combines efficient inference with native multimodal understanding through jointly trained text and visual representations. Optimized for coding, terminal and computer-use agents, and long-horizon workflows, the model introduces compressed KV caching that reduces cache memory usage to approximately one-quarter of the previous Flash generation. DeepSeek reports that it surpasses V4 Pro in performance, inference speed, and task completion time, making it well suited for cost-efficient autonomous agents, complex software engineering, and long-context analysis.