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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. Qwen3.8 27BAlibabaRemove
  2. Veo 3 FastGoogleRemove
  3. GLM 5.3 FlashZ.AIRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

qwen3.8-27b vs veo3-fast vs glm-5.3-flash vs muse-spark-1.3
AttributeQwen3.8 27Bqwen3.8-27bVeo 3 Fastveo3-fastGLM 5.3 Flashglm-5.3-flashMuse Spark 1.3muse-spark-1.3
Pricing
Input$0.45 / 1M$0.075 / 1M$1.25 / 1M
Output$3.20 / 1M$0.25 / 1M$4.25 / 1M
Cache Write (5m)$0.45 / 1MNot applicable$0.075 / 1M$1.25 / 1M
Cache Write (1h)$0.45 / 1MNot applicable$0.075 / 1M$1.25 / 1M
Cache Read$0.45 / 1MNot applicable$0.075 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Request$1.26 / request
BillingPay Per Request
Context
Max context262KN/A1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderAlibabaGoogleZ.AIMeta
Categorychatvideochatchat
Charge typePay As You GoPay Per RequestPay As You GoPay As You Go
Released
Description
SummaryQwen3.8 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.GLM-5.3-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads.Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.