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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. Muse WonderMetaRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. MiniMax M2MiniMaxRemove
  4. DeepSeek V4.1 Flash 1017DeepSeekRemove

4 is the maximum. Remove one to add another.

muse-wonder vs gpt-6.1-terra vs minimax-m2 vs deepseek-v4.1-flash-1017
AttributeMuse Wondermuse-wonderGPT-6.1 Terragpt-6.1-terraMiniMax M2minimax-m2DeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017
Pricing
Input$4.00 / 1M$1.00 / 1M$0.15 / 1M$0.20 / 1M
Output$20.00 / 1M$5.00 / 1M$0.45 / 1M$0.80 / 1M
Cache Write (5m)$4.00 / 1M$1.00 / 1M$0.15 / 1M$0.20 / 1M
Cache Write (1h)$4.00 / 1M$1.00 / 1M$0.15 / 1M$0.20 / 1M
Cache Read$4.00 / 1M$1.00 / 1M$0.15 / 1M$0.20 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M196.6K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesNoYesYes
Catalogue
ProviderMetaOpenAIMiniMaxDeepSeek
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released————
Description
SummaryMuse 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.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.MiniMax-M2 is a compact, high-efficiency model with 10B active (230B total) parameters, optimized for coding and agentic workflows. It delivers near-frontier reasoning and tool use, excels at multi-file coding tasks and compile-run-fix loops, and performs strongly on benchmarks like SWE-Bench and Terminal-Bench. It also handles long-horizon planning and recovery in agent evaluations, ranking among the top open models across reasoning domains. With fast inference and low cost, it’s ideal for large-scale agents and developer assistants — and works best when reasoning is preserved across turns.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.