Claude Opus 5.5 vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Claude Opus 5.5 by Anthropic against Kimi K3 by Moonshot AI across real-world coding, reasoning, token economics, and latency.
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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%
Kimi K3
Modello a Pesi ApertiKimi K3 di Moonshot AI è un modello di intelligenza artificiale con finestra di contesto da 1.0M 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
1.05M tokens
74.5%
Differenze Chiave a Colpo d’Occhio
- Kimi K3 is 1.0x cheaper for API usage (input + output tokens combined).
- Kimi K3 has a larger context window (1M vs 1M tokens).
- Claude Opus 5.5 is faster at 78 tok/s vs 58 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 74.5%).
| Feature | Claude Opus 5.5 | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 94 | 93 |
Provider | Anthropic | Moonshot AI |
Input Price Cost per million input tokens | $4.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $20.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $3.08 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 128K | 131K |
Throughput Speed Tokens generated per second | 78 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 84.6% | 74.5% |
GPQA Diamond Graduate-level reasoning | 82.3% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.40 / 1M | $0.00 / 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.
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
Kimi K3 · Strengths & Key Advantages
- Architettura Colossus-Scale Multimodal Sparse Mixture-of-Experts avanzata di Moonshot AI con supporto fino a 1.0M 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Opus 5.5 | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $3.85 | $2.00 |
Output Tokens / Task Tokens generated per task | 68K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 42K | 18K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude Opus 5.5 | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | claude-opus-5-5-20260901 | kimi-k3 |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-07 |
| Knowledge Cutoff | June 2026 | March 2026 |
| Architecture | Extended Autonomous Hybrid Reasoning Transformer | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential | 2.8T total (MoE) |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| 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) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Claude Opus 5.5 vs Kimi K3: Risposte Rapide (FAQ)
Which is more intelligent, Claude Opus 5.5 or Kimi K3?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 93 on the composite Intelligence Index.
Which model is faster?
Claude Opus 5.5 is faster, generating 78 tokens per second compared with 58 tokens per second.
Which model is cheaper to run?
Kimi K3 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 K3 has lower latency, with a time to first token of 0.72s compared with 1.40s.
Which model has a larger context window?
Kimi K3 has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Verdetto
Choose Claude Opus 5.5 for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione anthropic, or choose Kimi K3 for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione moonshot ai.
Domande Frequenti (FAQ)
Which is cheaper: Claude Opus 5.5 or Kimi K3?+
Claude Opus 5.5 costs $4/1M input and $20/1M output tokens, while Kimi K3 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Claude Opus 5.5 or Kimi K3?+
Claude Opus 5.5 supports 1M tokens whereas Kimi K3 supports 1.0M 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: Anthropic & Moonshot AI Official Documentation