Gemini 4 Argon vs Claude Fable 5.1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemini 4 Argon by Google and Claude Fable 5.1 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, Gemini 4 Argon holds a notable pricing advantage for token ingestion. On output generation, Gemini 4 Argon 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, Gemini 4 Argon 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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Gemini 4 Argon
Deep-Reasoning-ModellGemini 4 Argon von Google ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $2/1M Input- und $10/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Claude Fable 5.1
Deep-Reasoning-ModellClaude Fable 5.1 von Anthropic ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $10/1M Input- und $50/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$10.00 / 1M
$50.00 / 1M
1M tokens
83.8%
Wichtigste Unterschiede auf einen Blick
- Gemini 4 Argon is 5.0x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon is faster at 115 tok/s vs 65 tok/s.
- Claude Fable 5.1 leads on SWE-bench coding (83.8% vs 81.9%).
| Feature | Gemini 4 Argon | Claude Fable 5.1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 97 | 82 |
Provider | Anthropic | |
Input Price Cost per million input tokens | $2.00 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.47 / 1M | $7.17 / 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 | 1000K | 128K |
Throughput Speed Tokens generated per second | 115 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 81.9% | 83.8% |
GPQA Diamond Graduate-level reasoning | 81.5% | 81.6% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.25 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte 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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
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.
Gemini 4 Argon · Strengths & Key Advantages
- Fortschrittliche Pathways Multimodal Sparse Mixture-of-Experts-Architektur von Google mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($2/1M Input, $10/1M 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
Claude Fable 5.1 · Strengths & Key Advantages
- Fortschrittliche Frontier Auditable Multimodal Transformer-Architektur von Anthropic mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($10/1M Input, $50/1M 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 | Gemini 4 Argon | Claude Fable 5.1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.45 | $10.97 |
Output Tokens / Task Tokens generated per task | 75K | 72K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 37K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 4 Argon | Claude Fable 5.1 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-4-argon | claude-fable-5-1-20260901 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-09 | 2026-09 |
| Knowledge Cutoff | June 2026 | June 2026 |
| Architecture | Pathways Multimodal Sparse Mixture-of-Experts | Frontier Auditable Multimodal Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 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) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL | $0.25/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | 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 | Google DeepMind Frontier Safety Framework 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, vision, audio, video | text, vision |
| Output Modalities | text | text |
Gemini 4 Argon vs Claude Fable 5.1: Schnelle Antworten (FAQ)
Which is more intelligent, Gemini 4 Argon or Claude Fable 5.1?
Gemini 4 Argon is more intelligent. It scores 97 compared with 82 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Gemini 4 Argon is cheaper at $1.47 per 1M tokens blended, compared with $7.17 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 4 Argon has lower latency, with a time to first token of 0.85s compared with 1.45s.
Fazit
Verdict & Model Selection Recommendation: Choose Gemini 4 Argon if your priority is Whole-repository code generation, full-book synthesis, video understanding, and multi-hour audio processing. Its performance in demanding tasks justifies its operational token cost. Choose Claude Fable 5.1 if your workflow prioritizes High-stakes security auditing, automated vulnerability discovery, and mission-critical enterprise agents. 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 Gemini 4 Argon compare to Claude Fable 5.1 in API token pricing and cost per token?+
Gemini 4 Argon costs $2.00 per 1M input tokens and $10.00 per 1M output tokens, whereas Claude Fable 5.1 costs $10.00 input and $50.00 output. Overall, Gemini 4 Argon 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: Gemini 4 Argon or Claude Fable 5.1?+
Both Gemini 4 Argon and Claude Fable 5.1 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: Gemini 4 Argon or Claude Fable 5.1?+
Gemini 4 Argon scores 81.9% on SWE-bench Verified while Claude Fable 5.1 scores 83.8%. For autonomous software engineering and multi-file debugging, Claude Fable 5.1 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Gemini 4 Argon and Claude Fable 5.1?+
Gemini 4 Argon features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Fable 5.1 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemini 4 Argon and Claude Fable 5.1?+
Gemini 4 Argon generates approximately 115 tokens per second, while Claude Fable 5.1 delivers 65 tokens per second. In latency-sensitive production environments, Gemini 4 Argon provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini 4 Argon and Claude Fable 5.1 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 4 Argon to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Fable 5.1. 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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- Command A Plus vs Gemini 4 Argon: Benchmarks, Pricing & Speed ComparisonVergleich
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