Claude Sonnet 5.5 vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude Sonnet 5.5 by Anthropic and Gemini 4 Argon by Google 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, both models offer identical input token rates. On output generation, costs are matched. 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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Claude Sonnet 5.5
Model Penalaran MendalamClaude Sonnet 5.5 dari Anthropic adalah model AI canggih dengan jendela konteks 1M token serta harga API $2.0/1M token input dan $10.0/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.9%
Gemini 4 Argon
Model Penalaran MendalamGemini 4 Argon dari Google adalah model AI canggih dengan jendela konteks 1M token serta harga API $2/1M token input dan $10/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Perbedaan Utama Sekilas
- Gemini 4 Argon is 1.0x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon is faster at 115 tok/s vs 105 tok/s.
- Claude Sonnet 5.5 leads on SWE-bench coding (82.9% vs 81.9%).
| Feature | Claude Sonnet 5.5 | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 78 | 97 |
Provider | Anthropic | |
Input Price Cost per million input tokens | $2.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.54 / 1M | $1.47 / 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 | 128K | 1000K |
Throughput Speed Tokens generated per second | 105 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 82.9% | 81.9% |
GPQA Diamond Graduate-level reasoning | 79.4% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.20 / 1M | $0.10 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude Sonnet 5.5 · Strengths & Key Advantages
- Arsitektur Adaptive Frontier Multimodal Transformer mutakhir dari Anthropic dengan dukungan konteks 1M token
- Efisiensi biaya API yang kompetitif ($2.0/1M input, $10.0/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
Gemini 4 Argon · Strengths & Key Advantages
- Arsitektur Pathways Multimodal Sparse Mixture-of-Experts mutakhir dari Google dengan dukungan konteks 1M token
- Efisiensi biaya API yang kompetitif ($2/1M input, $10/1M output) dan respons cepat
- Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
- Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
- Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Sonnet 5.5 | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $3.29 | $2.45 |
Output Tokens / Task Tokens generated per task | 72K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 37K | 36K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude Sonnet 5.5 | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | claude-sonnet-5-5-20260901 | models/gemini-4-argon |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-09 | 2026-09 |
| Knowledge Cutoff | June 2026 | June 2026 |
| Architecture | Adaptive Frontier Multimodal Transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 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 | $0.2/1M cached read (90% off); 5-min default & 1-hour extended TTL | Implicit + Explicit Context Caching at $0.1/1M with configurable 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | Google DeepMind Frontier Safety Framework 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, vision | text, vision, audio, video |
| Output Modalities | text | text |
Claude Sonnet 5.5 vs Gemini 4 Argon: Jawaban Cepat (FAQ)
Which is more intelligent, Claude Sonnet 5.5 or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 78 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 105 tokens per second.
Which model is cheaper to run?
Gemini 4 Argon is cheaper at $1.47 per 1M tokens blended, compared with $1.54 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude Sonnet 5.5 has lower latency, with a time to first token of 0.55s compared with 0.85s.
Kesimpulan
Verdict & Model Selection Recommendation: Choose Claude Sonnet 5.5 if your priority is Daily production development, agentic workflows, IDE integrations, and automated code review. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 4 Argon if your workflow prioritizes Whole-repository code generation, full-book synthesis, video understanding, and multi-hour audio processing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Pertanyaan Umum (FAQ)
How does Claude Sonnet 5.5 compare to Gemini 4 Argon in API token pricing and cost per token?+
Both Claude Sonnet 5.5 and Gemini 4 Argon share matched API token pricing at $2.00 per 1M input tokens and $10.00 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Claude Sonnet 5.5 or Gemini 4 Argon?+
Both Claude Sonnet 5.5 and Gemini 4 Argon 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: Claude Sonnet 5.5 or Gemini 4 Argon?+
Claude Sonnet 5.5 scores 82.9% on SWE-bench Verified while Gemini 4 Argon scores 81.9%. For autonomous software engineering and multi-file debugging, Claude Sonnet 5.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Claude Sonnet 5.5 and Gemini 4 Argon?+
Claude Sonnet 5.5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 4 Argon provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Claude Sonnet 5.5 and Gemini 4 Argon?+
Claude Sonnet 5.5 generates approximately 105 tokens per second, while Gemini 4 Argon delivers 115 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 Claude Sonnet 5.5 and Gemini 4 Argon to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Gemini 4 Argon, and route deep architectural or complex reasoning queries to Claude Sonnet 5.5. This balanced routing strategy maximizes system throughput and answer quality.
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.
Sumber resmi: GetInfoUs Independent AI Benchmarks & Official API Specs