Fugu Ultra vs Claude Fable 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Fugu Ultra by Sakana AI and Claude Fable 5 by Anthropic represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Fugu Ultra holds a notable pricing advantage for token ingestion. On output generation, Fugu Ultra is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 1,000K tokens. In latency-sensitive production environments, Fugu Ultra leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Fugu Ultra
深度推理模型Sakana AI 推出的 Fugu Ultra 人工智能模型，具备 1M 上下文窗口，输入价格为 $1.5/百万 Token，输出价格为 $6/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$1.50 / 1M
$6.00 / 1M
1M tokens
N/A
Claude Fable 5
深度推理模型Anthropic 推出的 Claude Fable 5 人工智能模型，具备 1M 上下文窗口，输入价格为 $10/百万 Token，输出价格为 $50/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$10.00 / 1M
$50.00 / 1M
1M tokens
76.5%
核心差异一览
- Fugu Ultra is 7.8x cheaper for API usage (input + output tokens combined).
- Fugu Ultra is faster at 75 tok/s vs 52 tok/s.
| Feature | Fugu Ultra | Claude Fable 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 72 |
Provider | Sakana AI | Anthropic |
Input Price Cost per million input tokens | $1.50 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.01 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 75 tok/s | 52 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 76.5% |
GPQA Diamond Graduate-level reasoning | 67% | 79.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.15 / 1M | $1.00 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Fugu Ultra · Strengths & Key Advantages
- 由 Sakana AI 研发的高精度 Evolutionary Multi-Model Merged Transformer 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $1.5/1M，输出 $6/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Claude Fable 5 · Strengths & Key Advantages
- 由 Anthropic 研发的高精度 Mythos-Class Dense Reasoning Architecture 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $10/1M，输出 $50/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Fugu Ultra | Claude Fable 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.28 | $10.97 |
Output Tokens / Task Tokens generated per task | 22K | 72K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 37K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Fugu Ultra | Claude Fable 5 |
|---|---|---|
| Official API Model ID (SDK) | fugu-ultra | claude-fable-5-20260601 |
| API Protocol | Sakana AI REST API (OpenAI-Compatible) | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-06 | 2026-06 |
| Knowledge Cutoff | March 2026 | March 2026 |
| Architecture | Evolutionary Multi-Model Merged Transformer | Mythos-Class Dense Reasoning Architecture |
| Parameters | Confidential / Frontier Merged | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.5/1M in) across full 1000K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.15/1M tokens | $1/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Sakana AI Production Safety & Alignment Verified | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Fugu Ultra vs Claude Fable 5: 常见问题解答 (FAQ)
Which is more intelligent, Fugu Ultra or Claude Fable 5?
Fugu Ultra is more intelligent. It scores 86 compared with 72 on the composite Intelligence Index.
Which model is faster?
Fugu Ultra is faster, generating 75 tokens per second compared with 52 tokens per second.
Which model is cheaper to run?
Fugu Ultra is cheaper at $1.01 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Fugu Ultra has lower latency, with a time to first token of 0.54s compared with 1.75s.
综合结论
Verdict & Model Selection Recommendation: Choose Fugu Ultra if your priority is Japanese enterprise intelligence, bilingual translation, and creative scientific exploration. Its performance in demanding tasks justifies its operational token cost. Choose Claude Fable 5 if your workflow prioritizes Literary authorship, complex screenwriting, video game narrative trees, and high-end marketing campaigns. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Fugu Ultra compare to Claude Fable 5 in API token pricing and cost per token?+
Fugu Ultra costs $1.50 per 1M input tokens and $6.00 per 1M output tokens, whereas Claude Fable 5 costs $10.00 input and $50.00 output. Overall, Fugu Ultra is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Fugu Ultra or Claude Fable 5?+
Both Fugu Ultra and Claude Fable 5 offer an identical context window of 1,000K tokens (~750k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Fugu Ultra or Claude Fable 5?+
Fugu Ultra scores at the frontier of coding benchmarks, while Claude Fable 5 scores 76.5%. For complex autonomous debugging and agentic engineering, Claude Fable 5 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Fugu Ultra and Claude Fable 5?+
Fugu Ultra features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Fable 5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Fugu Ultra and Claude Fable 5?+
Fugu Ultra generates approximately 75 tokens per second, while Claude Fable 5 delivers 52 tokens per second. In latency-sensitive production environments, Fugu Ultra provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Fugu Ultra and Claude Fable 5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Fugu Ultra to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Fable 5. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs