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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-5.1 CodexOpenAIRemove
  2. Gemini 3.7 FlashGoogleRemove
  3. GLM 5.3Z.AIRemove
gpt-5.1-codex vs gemini-3.7-flash vs glm-5.3
AttributeGPT-5.1 Codexgpt-5.1-codexGemini 3.7 Flashgemini-3.7-flashGLM 5.3glm-5.3
Pricing
Input$0.375 / 1M$0.375 / 1M$1.40 / 1M
Output$3.00 / 1M$1.88 / 1M$4.40 / 1M
Cache Write (5m)$0.375 / 1M$0.375 / 1M$1.40 / 1M
Cache Write (1h)$0.375 / 1M$0.375 / 1M$1.40 / 1M
Cache Read$0.375 / 1M$0.375 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context400K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIGoogleZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-5.1 Codex is a coding-focused version of GPT-5.1 designed for both interactive development and long autonomous engineering tasks. It can build projects, add features, debug, refactor, and review code with higher steerability and cleaner outputs than GPT-5.1. It integrates with developer tools (CLI, IDEs, GitHub, cloud), supports adjustable reasoning effort, handles images/screenshots for UI work, and uses tools for search and environment setup — making it purpose-built for agentic coding workflows.Gemini 3.7 Flash is Google's fast multimodal model designed for agentic workflows, coding, and complex multi-step reasoning. It combines responsive inference with reliable problem-solving capabilities, making it well suited for interactive and production-scale applications. Optimized for speed and dependable multi-step execution, Gemini 3.7 Flash is a strong choice for coding assistants, autonomous agents, and high-throughput workflows that require both low latency and capable reasoning.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.