Llama 3.1 Nemotron 70B Instruct vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 3.1 Nemotron 70B Instruct by NVIDIA against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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Llama 3.1 Nemotron 70B Instruct
70B alineadode NVIDIA customized 70B alignment potencia trained con HelpSteer2 un rival comercial flagships en conversacional calidad y codificación.
$0.35 / 1M
$0.70 / 1M
128K tokens
41.2%
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%
Diferencias Clave de un Vistazo
- Llama 3.1 Nemotron 70B Instruct is 18.9x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 128K tokens).
- Llama 3.1 Nemotron 70B Instruct is faster at 80 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 41.2%).
| Característica | Llama 3.1 Nemotron 70B Instruct | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 47 | 94 |
Provider | NVIDIA | Anthropic |
Input Price Cost per million input tokens | $0.35 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.70 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.21 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 80 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 41.2% | 84.6% |
GPQA Diamond Graduate-level reasoning | 58.5% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.40 / 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.
Llama 3.1 Nemotron 70B Instruct · Strengths & Key Advantages
- Trained con HelpSteer2 reward modelos para excepcional conversacional matiz
- Rivals larger 100B+ modelos en instrucción-following y codificación clarity
- optimizado para empresarial NVIDIA NIM microservice implementación
Considerations & Trade-offs
- requiere dual-GPU empresarial hardware para local unquantized ejecución
- carece de nativo visual imagen comprensión
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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.1 Nemotron 70B Instruct | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.28 | $3.85 |
Output Tokens / Task Tokens generated per task | 32K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 42K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Llama 3.1 Nemotron 70B Instruct | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | llama-3-1-nemotron-70b | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-04 | 2026-09 |
| Knowledge Cutoff | December 2024 | June 2026 |
| Architecture | RLHF Aligned Llama 3.1 Derivative | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 70B | 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) | Flat rate ($0.35/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.1/1M tokens | $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) · 43 GB VRAM (4-bit Quantized) · 151 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 |
Llama 3.1 Nemotron 70B Instruct vs Claude Opus 5.5: Respuestas Rápidas (FAQ)
Which is more intelligent, Llama 3.1 Nemotron 70B Instruct or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 47 on the composite Intelligence Index.
Which model is faster?
Llama 3.1 Nemotron 70B Instruct is faster, generating 80 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
Llama 3.1 Nemotron 70B Instruct is cheaper at $0.21 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3.1 Nemotron 70B Instruct has lower latency, with a time to first token of 0.45s 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 128K tokens.
Veredicto
Choose Llama 3.1 Nemotron 70B Instruct for empresarial cliente soporte, synthetic datos generación, y local agente orchestration, or choose Claude Opus 5.5 for grande-escala software development agentes, empresarial codebases, y de misión crítica científico síntesis.
Preguntas frecuentes
Which is cheaper: Llama 3.1 Nemotron 70B Instruct or Claude Opus 5.5?+
Llama 3.1 Nemotron 70B Instruct costs $0.35/1M input and $0.7/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: Llama 3.1 Nemotron 70B Instruct or Claude Opus 5.5?+
Llama 3.1 Nemotron 70B Instruct supports 128K 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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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: NVIDIA & Anthropic Official Documentation