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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. Gemini 3.5 TranscribeGoogleRemove
  2. Muse Spark 1.3MetaRemove
  3. GLM 5.3Z.AIRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

gemini-3.5-transcribe vs muse-spark-1.3 vs glm-5.3 vs gemini-4-argon
AttributeGemini 3.5 Transcribegemini-3.5-transcribeMuse Spark 1.3muse-spark-1.3GLM 5.3glm-5.3Gemini 4 Argongemini-4-argon
Pricing
Input— Not priced per input token$1.25 / 1M$1.40 / 1M$2.00 / 1M
Output— Not priced per output token$4.25 / 1M$4.40 / 1M$10.00 / 1M
Cache Write (5m)Not applicable$1.25 / 1M$1.40 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$1.25 / 1M$1.40 / 1M$2.00 / 1M
Cache ReadNot applicable$1.25 / 1M$1.40 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context98.3K1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoYes
Function CallingYesYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderGoogleMetaZ.AIGoogle
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-25———
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.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.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.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.