Fugu Ultra vs Qwen3.7 Plus
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Fugu Ultra by Sakana AI and Qwen3.7 Plus by Alibaba 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, Qwen3.7 Plus holds a notable pricing advantage for token ingestion. On output generation, Qwen3.7 Plus 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, Qwen3.7 Plus 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
Deep-Reasoning-ModellFugu Ultra von Sakana AI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $1.5/1M Input- und $6/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$1.50 / 1M
$6.00 / 1M
1M tokens
N/A
Qwen3.7 Plus
Deep-Reasoning-ModellQwen3.7 Plus von Alibaba ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $0.8/1M Input- und $3.2/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.80 / 1M
$3.20 / 1M
1M tokens
68%
Wichtigste Unterschiede auf einen Blick
- Qwen3.7 Plus is 1.9x cheaper for API usage (input + output tokens combined).
- Qwen3.7 Plus is faster at 92 tok/s vs 75 tok/s.
| Feature | Fugu Ultra | Qwen3.7 Plus |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 89 |
Provider | Sakana AI | Alibaba |
Input Price Cost per million input tokens | $1.50 / 1M | $0.80 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $3.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.01 / 1M | $0.54 / 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 | 16K |
Throughput Speed Tokens generated per second | 75 tok/s | 92 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 68% |
GPQA Diamond Graduate-level reasoning | 67% | 75% |
Cached Input Price Prompt caching discount per 1M tokens | $0.15 / 1M | $0.08 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkEvaluations ongoing
Geprüfte Benchmark-Ergebnisse im Vergleich
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.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Fugu Ultra · Strengths & Key Advantages
- Fortschrittliche Evolutionary Multi-Model Merged Transformer-Architektur von Sakana AI mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($1.5/1M Input, $6/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Qwen3.7 Plus · Strengths & Key Advantages
- Fortschrittliche High-Value Multimodal Foundation Transformer-Architektur von Alibaba mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.8/1M Input, $3.2/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Fugu Ultra | Qwen3.7 Plus |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.28 | $0.24 |
Output Tokens / Task Tokens generated per task | 22K | 26K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 9K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Fugu Ultra | Qwen3.7 Plus |
|---|---|---|
| Official API Model ID (SDK) | fugu-ultra | qwen3-7-plus-instruct |
| API Protocol | Sakana AI REST API (OpenAI-Compatible) | Alibaba REST API (OpenAI-Compatible) |
| Release Date | 2026-06 | 2026-06 |
| Knowledge Cutoff | March 2026 | April 2026 |
| Architecture | Evolutionary Multi-Model Merged Transformer | High-Value Multimodal Foundation Transformer |
| Parameters | Confidential / Frontier Merged | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.5/1M in) across full 1000K window | Flat rate ($0.8/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.15/1M tokens | Cached input at $0.08/1M tokens |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG 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 | Alibaba Production Safety & Alignment Verified |
| 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 Qwen3.7 Plus: Schnelle Antworten (FAQ)
Which is more intelligent, Fugu Ultra or Qwen3.7 Plus?
Qwen3.7 Plus is more intelligent. It scores 89 compared with 86 on the composite Intelligence Index.
Which model is faster?
Qwen3.7 Plus is faster, generating 92 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Qwen3.7 Plus is cheaper at $0.54 per 1M tokens blended, compared with $1.01 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.7 Plus has lower latency, with a time to first token of 0.44s compared with 0.54s.
Fazit
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 Qwen3.7 Plus if your workflow prioritizes Large-scale multimodal codebases, video document parsing, and enterprise assistants. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does Fugu Ultra compare to Qwen3.7 Plus 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 Qwen3.7 Plus costs $0.80 input and $3.20 output. Overall, Qwen3.7 Plus 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 Qwen3.7 Plus?+
Both Fugu Ultra and Qwen3.7 Plus 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 Qwen3.7 Plus?+
Fugu Ultra scores at the frontier of coding benchmarks, while Qwen3.7 Plus scores 68%. For complex autonomous debugging and agentic engineering, Qwen3.7 Plus provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Fugu Ultra and Qwen3.7 Plus?+
Fugu Ultra features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Qwen3.7 Plus provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Fugu Ultra and Qwen3.7 Plus?+
Fugu Ultra generates approximately 75 tokens per second, while Qwen3.7 Plus delivers 92 tokens per second. In latency-sensitive production environments, Qwen3.7 Plus provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Fugu Ultra and Qwen3.7 Plus to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3.7 Plus to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Fugu Ultra. 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.
Offizielle Quellen: GetInfoUs Independent AI Benchmarks & Official API Specs