Sakana Namazu

Architectural Overview & Capability Profile

Sakana Namazu is developed by Sakana as an advanced foundation model engineered for production enterprise applications, reasoning workflows, and autonomous problem-solving. On the comprehensive RankLLMs composite evaluation index, Sakana Namazu registers an overall benchmark rating of 43.2 / 100.

Operating under a Proprietary license structure, Sakana Namazu is served via high-throughput managed API endpoints designed for low-latency production queries.

Benchmark Performance & Real-World Utility

In standardized laboratory and real-world evaluation suites, Sakana Namazu demonstrates calibrated proficiency across key performance vectors:

• SWE-bench Verified Software Engineering (38.5%): Measures multi-file GitHub issue resolution. The model demonstrates practical capability in localizing source code bugs, synthesizing functional patches, and passing rigorous integration test matrices. For evaluation methodology, consult our SWE-bench Pro guide.

• GPQA Diamond Scientific Reasoning (43%): Assesses graduate-level domain problem solving across physics, chemistry, and biology, minimizing hallucinations in multi-step deductive arguments. See our Humanity’s Last Exam benchmark analysis.

• MATH-500 Competition Mathematics (43%): Evaluates formal mathematical problem solving and symbolic derivation, providing foundational logic for algorithmic computing.

• Terminal-Bench Execution (38.5%): Reflects proficiency in interactive command-line environments, shell tool use, and automated DevOps scripting.

Inference Latency & Cost Architecture

• Token Context Window: 262K tokens, allowing large documents, multi-file codebases, and long conversation memories to be processed in a single context window.
• Inference Speed: Reference throughput clocks at approximately 70 tokens/second, enabling responsive streaming in developer tools and interactive interfaces.
• Commercial Pricing: API consumption is billed at $0.95 per 1M input tokens and $4.00 per 1M output tokens, offering predictable scaling for high-volume enterprise traffic.

Strengths, Limitations & Ideal Workflows

Primary Strengths

• High overall benchmark score of 43.2 on frontier evaluation suites
• Robust reasoning performance (43) and coding accuracy (N/A)
• Optimized inference throughput with 256K token context

Known Considerations

• Subject to standard provider rate limits and API quota

Recommended Use Cases

Enterprise production applications, coding assistance, and advanced reasoning.

Related Comparisons & Leaderboard Context

• Compare with alternatives in the Model Comparison Engine
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• Explore top coding models in our Best LLM for Coding Guide