Kimi K2 Thinking vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Kimi K2 Thinking by Moonshot AI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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Kimi K2 Thinking
Penalaran Bobot TerbukaKimi K2 Thinking dari Moonshot AI adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 262K, kecepatan 55 token/detik, indeks kecerdasan 85/100
$0.00 / 1M
$0.00 / 1M
262.1K tokens
50%
Claude Opus 5.5
Model Unggulan Garis DepanClaude Opus 5.5 dari Anthropic adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 1M, kecepatan 78 token/detik, indeks kecerdasan 94/100, serta ta
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Perbedaan Utama Sekilas
- Kimi K2 Thinking is 1.0x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 262K tokens).
- Claude Opus 5.5 is faster at 78 tok/s vs 55 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 50.0%).
| Feature | Kimi K2 Thinking | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 85 | 94 |
Provider | Moonshot AI | Anthropic |
Input Price Cost per million input tokens | $0.00 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 32K | 128K |
Throughput Speed Tokens generated per second | 55 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 50% | 84.6% |
GPQA Diamond Graduate-level reasoning | 70% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.40 / 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.
Kimi K2 Thinking · Strengths & Key Advantages
- Jendela konteks 262K dengan kapasitas output maksimum hingga 32K token (SWE-bench Verified 50% · GPQA Diamond 70%)
- Kecepatan generasi tinggi 55 token/detik dengan latensi token pertama (TTFT) hanya 950 ms
- Bobot terbuka (1T total (32B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
Claude Opus 5.5 · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
- Kecepatan generasi tinggi 78 token/detik dengan latensi token pertama (TTFT) hanya 1400 ms
- Skema harga API kompetitif (tarif $4/$20 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 | Kimi K2 Thinking | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $3.85 |
Output Tokens / Task Tokens generated per task | 28K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 16K | 42K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Kimi K2 Thinking | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | kimi-k2-thinking | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-11 | 2026-09 |
| Knowledge Cutoff | September 2025 | June 2026 |
| Architecture | Deliberate Reasoning Open Sparse Mixture-of-Experts | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 1T total (32B active MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | $0.4/1M 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 | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 620 GB VRAM (4-bit Quantized) · 2150 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Kimi K2 Thinking vs Claude Opus 5.5: Jawaban Cepat (FAQ)
Which is more intelligent, Kimi K2 Thinking or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 85 on the composite Intelligence Index.
Which model is faster?
Claude Opus 5.5 is faster, generating 78 tokens per second compared with 55 tokens per second.
Which model is cheaper to run?
Kimi K2 Thinking is cheaper at $0.00 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Kimi K2 Thinking has lower latency, with a time to first token of 0.95s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 262.1K tokens.
Kesimpulan
Choose Kimi K2 Thinking for sangat cocok untuk penalaran rantai pemikiran (chain-of-thought) berbobot terbuka yang sangat efisien, sistem rag perusahaan, dan integrasi api skala produksi., or choose Claude Opus 5.5 for sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi ai, sistem rag perusahaan, dan integrasi api skala produksi..
Pertanyaan Umum (FAQ)
Which is cheaper: Kimi K2 Thinking or Claude Opus 5.5?+
Kimi K2 Thinking costs $0/1M input and $0/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: Kimi K2 Thinking or Claude Opus 5.5?+
Kimi K2 Thinking supports 262K tokens whereas Claude Opus 5.5 supports 1M tokens.
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: Moonshot AI & Anthropic Official Documentation