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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. DeepSeek V4.1 Flash 1017DeepSeekRemove
  2. Muse WonderMetaRemove
  3. GLM 5.3Z.AIRemove
  4. Transcribe 1 ProFish AudioRemove

4 is the maximum. Remove one to add another.

deepseek-v4.1-flash-1017 vs muse-wonder vs glm-5.3 vs transcribe-1-pro
AttributeDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017Muse Wondermuse-wonderGLM 5.3glm-5.3Transcribe 1 Protranscribe-1-pro
Pricing
Input$0.10 / 1M$4.00 / 1M$1.40 / 1M— Not priced per input token
Output$0.50 / 1M$20.00 / 1M$4.40 / 1M— Not priced per output token
Cache Write (5m)$0.10 / 1M$4.00 / 1M$1.40 / 1MNot applicable
Cache Write (1h)$0.10 / 1M$4.00 / 1M$1.40 / 1MNot applicable
Cache Read$0.10 / 1M$4.00 / 1M$1.40 / 1MNot applicable
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1MN/A
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoNo
Function CallingYesYesYesNo
JSON ModeYesYesYesNo
StreamingYesYesYesNo
Catalogue
ProviderDeepSeekMetaZ.AIFish Audio
Categorychatchatchatvoice
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released———2026-09-24
Description
SummaryDeepSeek 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.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.GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.