Kimi K2 vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Kimi K2 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
Modello a Pesi ApertiKimi K2 di Moonshot AI è un modello di intelligenza artificiale con finestra di contesto da 131K token e tariffazione API di $0/1M token in input e $0/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
46%
Claude Opus 5.5
Modello di Ragionamento ProfondoClaude Opus 5.5 di Anthropic è un modello di intelligenza artificiale con finestra di contesto da 1M token e tariffazione API di $4.0/1M token in input e $20.0/1M in output, ideale per coding, ragionamento e automazione aziendale.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Differenze Chiave a Colpo d’Occhio
- Kimi K2 is 1.0x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 131K tokens).
- Claude Opus 5.5 is faster at 78 tok/s vs 62 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 46.0%).
| Feature | Kimi K2 | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 83 | 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 | 131.1K tokens~197 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 62 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 46% | 84.6% |
GPQA Diamond Graduate-level reasoning | 66.5% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.40 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Kimi K2 · Strengths & Key Advantages
- Architettura Trillion-Scale Sparse Mixture-of-Experts Transformer avanzata di Moonshot AI con supporto fino a 131K token di contesto
- Ottimo rapporto qualità-prezzo ($0/1M input, $0/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Claude Opus 5.5 · Strengths & Key Advantages
- Architettura Extended Autonomous Hybrid Reasoning Transformer avanzata di Anthropic con supporto fino a 1M token di contesto
- Ottimo rapporto qualità-prezzo ($4.0/1M input, $20.0/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Kimi K2 | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $3.85 |
Output Tokens / Task Tokens generated per task | 22K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 42K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Kimi K2 | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | kimi-k2 | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-07 | 2026-09 |
| Knowledge Cutoff | May 2025 | June 2026 |
| Architecture | Trillion-Scale Sparse Mixture-of-Experts Transformer | 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 vs Claude Opus 5.5: Risposte Rapide (FAQ)
Which is more intelligent, Kimi K2 or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 83 on the composite Intelligence Index.
Which model is faster?
Claude Opus 5.5 is faster, generating 78 tokens per second compared with 62 tokens per second.
Which model is cheaper to run?
Kimi K2 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 has lower latency, with a time to first token of 0.64s 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 131.1K tokens.
Verdetto
Choose Kimi K2 for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione moonshot ai, or choose Claude Opus 5.5 for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione anthropic.
Domande Frequenti (FAQ)
Which is cheaper: Kimi K2 or Claude Opus 5.5?+
Kimi K2 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 or Claude Opus 5.5?+
Kimi K2 supports 131K 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.
Fonti ufficiali: Moonshot AI & Anthropic Official Documentation