Claude Sonnet 5.5 vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Claude Sonnet 5.5 by Anthropic against Llama 5 by Meta 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 Sonnet 5.5
Gama media 5.5el mainstream medio-nivel insignia de late 2026, heredando el generacional advances de Opus 5.5 un estándar Sonnet precios para producción engineering swarms.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.9%
Llama 5
Automejora recursivade Meta insignia abierto modelo con nativo agente training, recursive self-improvement, y 5M token contexto.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Diferencias Clave de un Vistazo
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 1M tokens).
- Claude Sonnet 5.5 is faster at 105 tok/s vs 65 tok/s.
- Claude Sonnet 5.5 leads on SWE-bench coding (82.9% vs 74.0%).
| Característica | Claude Sonnet 5.5 | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 78 | 94 |
Provider | Anthropic | Meta |
Input Price Cost per million input tokens | $2.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.54 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 105 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 82.9% | 74% |
GPQA Diamond Graduate-level reasoning | 79.4% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.20 / 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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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 Sonnet 5.5 · Strengths & Key Advantages
- Incredible 82.9% SWE-bench Verified score un cotidiano $3/$15 API rates
- completo 1M token contexto con alto-velocidad 105 tokens/sec generación
- Streamlined multi-agente coordination con bajo latencia
Considerations & Trade-offs
- Slightly behind Opus 5.5 en extremely denso multi-hour científico theorems
- Cached prompt overhead during masivo 1M token updates
Llama 5 · Strengths & Key Advantages
- 5 millones token contexto con nativo multi-audio/video comprensión
- Bucle de entrenamiento de automejora recursiva
- Completely de pesos abiertos con empresarial comercial license
Considerations & Trade-offs
- complejo MoE servicio requirements
- Los cambios del tokenizador de 4B requieren actualizar entornos de ajuste heredados
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Sonnet 5.5 | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $3.29 | $0.00 |
Output Tokens / Task Tokens generated per task | 72K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 37K | 20K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Claude Sonnet 5.5 | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | claude-sonnet-5-5-20260901 | meta-llama/Llama-5 |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-04 |
| Knowledge Cutoff | June 2026 | January 2026 |
| Architecture | Adaptive Frontier Multimodal Transformer | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | Confidential | 600B+ total (~40-80B active) |
| 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 | $0.2/1M 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| 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) · 372 GB VRAM (4-bit Quantized) · 1290 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, video, audio |
| Output Modalities | text | text |
Claude Sonnet 5.5 vs Llama 5: Respuestas Rápidas (FAQ)
Which is more intelligent, Claude Sonnet 5.5 or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 78 on the composite Intelligence Index.
Which model is faster?
Claude Sonnet 5.5 is faster, generating 105 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $1.54 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude Sonnet 5.5 has lower latency, with a time to first token of 0.55s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 1M tokens.
Veredicto
Choose Claude Sonnet 5.5 for daily producción development, flujos de trabajo agénticos, ide integrations, y automatizado code revisión, or choose Llama 5 for autónomo de código abierto multi-agente systems, profundo software refactorización, y de frontera investigación.
Preguntas frecuentes
Which is cheaper: Claude Sonnet 5.5 or Llama 5?+
Claude Sonnet 5.5 costs $2/1M input and $10/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Claude Sonnet 5.5 or Llama 5?+
Claude Sonnet 5.5 supports 1M tokens whereas Llama 5 supports 5M 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 & Meta Official Documentation