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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Live comparison: no in-depth editorial review has been published for this pair yet.
Claude Opus 5.5
Titán de inteligencia de fronterade Anthropic pinnacle modelo de frontera diseñado para de largo alcance agentic ingeniería de software, denso multimodal documento síntesis, y autónomo investigación flujos de trabajo.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Kimi K3
Razonador abierto de 2.8TEl gigante modelo MoE abierto de 2.8T de Moonshot AI con ventana de contexto de 1 millón de tokens y capacidades revolucionarias en codificación.
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
Diferencias Clave de un Vistazo
- 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%).
| Característica | 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 |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude Opus 5.5 · Strengths & Key Advantages
- inigualable autónomo multipaso codificación agéntica y refactorización
- Flawless multidocumento razonamiento sobre 1M token contexto con no largo-prompt surcharge
- más alto Artificial análisis inteligencia Index ever measured (58/100), leading SWE-bench Pro un 89.9%
- Precise herramienta ejecución con robust error-recovery bucles
Considerations & Trade-offs
- Premium salida precios ($20.00/M) plus heavy razonamiento-token usage (~119K salida tokens por index tarea) keeps largo agentic se ejecuta expensive
- largo pensamiento times un maximum effort puede push un único hard agentic tarea toward ~$13
Kimi K3 · Strengths & Key Advantages
- Largest de pesos abiertos modelo fundacional en existence (2.8T parámetros)
- 1480 LMSYS Chatbot Arena Elo rivaling propietario cerrado de frontera leaders
- completo 1M token ventana de contexto paired con masivo 131K max salida tokens
Considerations & Trade-offs
- Extreme hardware prerequisites (distributed multinodo clúster implementación)
- genera extensive razonamiento traces que demand careful token económico management
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas Rápidas (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.
Veredicto
Choose Claude Opus 5.5 for grande-escala software development agentes, empresarial codebases, y de misión crítica científico síntesis, or choose Kimi K3 for auditorías de código de contexto largo, análisis de repositorios completos y trabajo agéntico autónomo.
Preguntas frecuentes
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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: Anthropic & Moonshot AI Official Documentation