Llama 3.1 Nemotron 70B Instruct vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 3.1 Nemotron 70B Instruct by NVIDIA against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
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Llama 3.1 Nemotron 70B Instruct
Modelo Base de Pesos AbiertosLlama 3.1 Nemotron 70B Instruct de NVIDIA es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 128K, velocidad de 80 tokens/s, índice de inteligen
$0.35 / 1M
$0.70 / 1M
128K tokens
41.2%
GPT-6 Astra
Modelo de Voz y Audio NativoGPT-6 Astra de OpenAI es un modelo de IA diseñado para síntesis de voz natural, transcripción acústica y procesamiento de audio en tiempo real, con ventana de contexto de 272K, velocidad de 100 tokens/s, índice de inteligencia de 96/100 y t
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Diferencias Clave de un Vistazo
- Llama 3.1 Nemotron 70B Instruct is 47.1x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 128K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 80 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 41.2%).
| Característica | Llama 3.1 Nemotron 70B Instruct | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 47 | 96 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $0.35 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $0.70 / 1M | $50.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.21 / 1M | $7.70 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 80 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 41.2% | 82.4% |
GPQA Diamond Graduate-level reasoning | 58.5% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $1.00 / 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.
Llama 3.1 Nemotron 70B Instruct · Strengths & Key Advantages
- Ventana de contexto de 128K con hasta 8K tokens de salida máxima (SWE-bench Verified 41.2% · GPQA Diamond 58.5%)
- Rendimiento ágil de 80 tokens/segundo con latencia inicial (TTFT) de solo 450 ms
- Pesos abiertos (70B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
GPT-6 Astra · Strengths & Key Advantages
- Ventana de contexto de 272K con hasta 128K tokens de salida máxima (SWE-bench Verified 82.4% · GPQA Diamond 80.9%)
- Rendimiento ágil de 100 tokens/segundo con latencia inicial (TTFT) de solo 950 ms
- Economía de API competitiva ($10/$50 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas
Considerations & Trade-offs
- Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
- En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.1 Nemotron 70B Instruct | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.28 | $2.15 |
Output Tokens / Task Tokens generated per task | 32K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Llama 3.1 Nemotron 70B Instruct | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | llama-3-1-nemotron-70b | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-04 | 2026-09 |
| Knowledge Cutoff | December 2024 | June 2026 |
| Architecture | RLHF Aligned Llama 3.1 Derivative | Omni-Adaptive Autoregressive with Dynamic CoT |
| 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 | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.35/1M in) across full 128K window | Flat rate ($10/1M in) across full 272K window |
| Prompt Cache Policy & TTL | Cached input at $0.1/1M tokens | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| 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 tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) |
| 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) | OpenAI Preparedness Framework Audited & System Card Published |
| 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, audio |
| Output Modalities | text | text, audio |
Llama 3.1 Nemotron 70B Instruct vs GPT-6 Astra: Respuestas Rápidas (FAQ)
Which is more intelligent, Llama 3.1 Nemotron 70B Instruct or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 47 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 80 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 $7.70 (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 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 128K tokens.
Veredicto
Choose Llama 3.1 Nemotron 70B Instruct for ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas rag empresariales y automatización mediante api., or choose GPT-6 Astra for ideal para síntesis de voz natural, transcripción acústica y procesamiento de audio en tiempo real, sistemas rag empresariales y automatización mediante api..
Preguntas frecuentes
Which is cheaper: Llama 3.1 Nemotron 70B Instruct or GPT-6 Astra?+
Llama 3.1 Nemotron 70B Instruct costs $0.35/1M input and $0.7/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.
Which model has a larger context window: Llama 3.1 Nemotron 70B Instruct or GPT-6 Astra?+
Llama 3.1 Nemotron 70B Instruct supports 128K tokens whereas GPT-6 Astra supports 272K 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 & OpenAI Official Documentation