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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 OSS 120BOpenAIRemove
  2. Muse Spark 1.3MetaRemove
  3. GLM 5.3Z.AIRemove
  4. Gemini 3.8 FlashGoogleRemove

4 is the maximum. Remove one to add another.

gpt-oss-120b vs muse-spark-1.3 vs glm-5.3 vs gemini-3.8-flash
AttributeGPT OSS 120Bgpt-oss-120bMuse Spark 1.3muse-spark-1.3GLM 5.3glm-5.3Gemini 3.8 Flashgemini-3.8-flash
Pricing
Input$0.15 / 1M$1.25 / 1M$1.40 / 1M$0.75 / 1M
Output$0.75 / 1M$4.25 / 1M$4.40 / 1M$3.75 / 1M
Cache Write (5m)$0.15 / 1M$1.25 / 1M$1.40 / 1M$0.75 / 1M
Cache Write (1h)$0.15 / 1M$1.25 / 1M$1.40 / 1M$0.75 / 1M
Cache Read$0.15 / 1M$1.25 / 1M$1.40 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context131.1K1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesNoYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderOpenAIMetaZ.AIGoogle
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
Summarygpt-oss-120b is an open-weight 117B-parameter MoE model from OpenAI, built for advanced reasoning and production workloads. Only about 5.1B parameters are active per step, and it’s optimized to run on a single H100 using MXFP4 quantization. It supports adjustable reasoning depth, full chain-of-thought, and native agent features like tool use, function calling, browsing, and structured outputs.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 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.