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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. Fugu UltraSakanaRemove
  2. GPT-6 Astra ProOpenAIRemove
  3. GLM 5.3Z.AIRemove
fugu-ultra vs gpt-6-astra-pro vs glm-5.3
AttributeFugu Ultrafugu-ultraGPT-6 Astra Progpt-6-astra-proGLM 5.3glm-5.3
Pricing
Input$5.00 / 1M$10.00 / 1M$1.40 / 1M
Output$30.00 / 1M$50.00 / 1M$4.40 / 1M
Cache Write (5m)$5.00 / 1M$10.00 / 1M$1.40 / 1M
Cache Write (1h)$5.00 / 1M$10.00 / 1M$1.40 / 1M
Cache Read$5.00 / 1M$10.00 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderSakanaOpenAIZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryFugu Ultra is the high-performance model in Sakana AI's Fugu family, built as a learned multi-agent orchestration system rather than a single monolithic model. It intelligently routes tasks across a pool of underlying models and can recursively invoke itself to solve complex problems more effectively. Optimized for multi-step reasoning, coding, and agentic workflows, Fugu Ultra supports configurable reasoning effort, native tool calling, and built-in web search. Its orchestration-based design makes it well suited for advanced autonomous agents and complex task execution requiring adaptive model coordination.GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost.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.