Claude Opus 4.6 vs Claude Sonnet 4.6
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude Opus 4.6 dari Anthropic dan Claude Sonnet 4.6 dari Anthropic adalah dua model AI unggulan. Claude Opus 4.6 menawarkan jendela konteks 1M token dengan harga $5.00/$25.00 per 1 juta token, sedangkan Claude Sonnet 4.6 menyediakan konteks 1M token seharga $3.00/$15.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Claude Opus 4.6
Mesin Penalaran MendalamClaude Opus 4.6 dari Anthropic adalah model AI yang dirancang untuk penalaran logis mendalam, matematika tingkat lanjut, dan analisis ilmiah, menghadirkan jendela konteks 1M, kecepatan 55 token/detik, indeks kecerdasan 65/100, serta tarif $
$5.00 / 1M
$25.00 / 1M
1M tokens
78.4%
Claude Sonnet 4.6
Model Unggulan Garis DepanClaude Sonnet 4.6 dari Anthropic adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 1M, kecepatan 92 token/detik, indeks kecerdasan 60/100, serta
$3.00 / 1M
$15.00 / 1M
1M tokens
76.1%
Perbedaan Utama Sekilas
- Claude Sonnet 4.6 is 1.7x cheaper for API usage (input + output tokens combined).
- Claude Sonnet 4.6 is faster at 92 tok/s vs 55 tok/s.
- Claude Opus 4.6 leads on SWE-bench coding (78.4% vs 76.1%).
| Feature | Claude Opus 4.6 | Claude Sonnet 4.6 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 65 | 60 |
Provider | Anthropic | Anthropic |
Input Price Cost per million input tokens | $5.00 / 1M | $3.00 / 1M |
Output Price Cost per million output tokens | $25.00 / 1M | $15.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $3.85 / 1M | $2.31 / 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 | 128K |
Throughput Speed Tokens generated per second | 55 tok/s | 92 tok/s |
SWE-bench Verified Real-world software engineering | 78.4% | 76.1% |
GPQA Diamond Graduate-level reasoning | 76.5% | 74% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.30 / 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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
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 Opus 4.6 · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 78.4% · GPQA Diamond 76.5%)
- Kecepatan generasi tinggi 55 token/detik dengan latensi token pertama (TTFT) hanya 1900 ms
- Skema harga API kompetitif (tarif $5/$25 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya
Claude Sonnet 4.6 · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 76.1% · GPQA Diamond 74%)
- Kecepatan generasi tinggi 92 token/detik dengan latensi token pertama (TTFT) hanya 650 ms
- Skema harga API kompetitif (tarif $3/$15 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Opus 4.6 | Claude Sonnet 4.6 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $5.49 | $3.29 |
Output Tokens / Task Tokens generated per task | 72K | 72K |
Reasoning Tokens / Task Thinking tokens burned per task | 37K | 37K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude Opus 4.6 | Claude Sonnet 4.6 |
|---|---|---|
| Official API Model ID (SDK) | claude-opus-4-6-20260201 | claude-sonnet-4-6-20260201 |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-02 | 2026-02 |
| Knowledge Cutoff | December 2025 | December 2025 |
| Architecture | Megacontext Frontier Reasoning Transformer | High-Throughput Multimodal Agent Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority 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) | 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 | $0.5/1M cached read (90% off); 5-min default & 1-hour extended TTL | $0.3/1M 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | Custom Model Tuning via Amazon Bedrock & Prompt 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) |
| 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 | 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 | text, vision |
| Output Modalities | text | text |
Claude Opus 4.6 vs Claude Sonnet 4.6: Jawaban Cepat (FAQ)
Which is more intelligent, Claude Opus 4.6 or Claude Sonnet 4.6?
Claude Opus 4.6 is more intelligent. It scores 65 compared with 60 on the composite Intelligence Index.
Which model is faster?
Claude Sonnet 4.6 is faster, generating 92 tokens per second compared with 55 tokens per second.
Which model is cheaper to run?
Claude Sonnet 4.6 is cheaper at $2.31 per 1M tokens blended, compared with $3.85 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude Sonnet 4.6 has lower latency, with a time to first token of 0.65s compared with 1.90s.
Kesimpulan
Baik Claude Opus 4.6 (Sangat cocok untuk penalaran logis mendalam, matematika tingkat lanjut, dan analisis ilmiah, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Claude Sonnet 4.6 (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Claude Opus 4.6 unggul dalam penalaran dan pemrograman, Claude Sonnet 4.6 memberikan kecepatan generasi token lebih tinggi, dan Claude Sonnet 4.6 lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Claude Opus 4.6 atau Claude Sonnet 4.6?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Claude Opus 4.6 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Claude Opus 4.6 vs Claude Sonnet 4.6?+
Claude Opus 4.6 mengenakan biaya $5.00 / $25.00 per 1M token input/output, sedangkan Claude Sonnet 4.6 sebesar $3.00 / $15.00. Secara keseluruhan, Claude Sonnet 4.6 lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Claude Opus 4.6 mendukung konteks 1M token dibandingkan 1M token pada Claude Sonnet 4.6, sementara Claude Sonnet 4.6 unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Claude Opus 4.6 vs Grok Build 0.1: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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- Claude Sonnet 4.6 vs Claude Opus 4.5: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Claude Sonnet 4.6 vs Gemini 3.1 Flash-Lite: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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- Aion-3.0 vs Gemini 3.6 Flash: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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: Dokumentasi API resmi Anthropic & Anthropic serta evaluasi benchmark independen (2026).