Llama 3.1 405B vs GPT-4o
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 3.1 405B by Meta and GPT-4o by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Llama 3.1 405B holds a notable pricing advantage for token ingestion. On output generation, Llama 3.1 405B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, GPT-4o leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Llama 3.1 405B
Modelo de Pesos AbertosO Llama 3.1 405B da Meta é um modelo de inteligência artificial com janela de contexto de 128K tokens e preço de API de $0.0/1M na entrada e $0.0/1M na saída, projetado para programação, raciocínio e automação empresarial.
$0.00 / 1M
$0.00 / 1M
128K tokens
41.5%
GPT-4o
Modelo Flagship de FronteiraO GPT-4o da OpenAI é um modelo de inteligência artificial com janela de contexto de 128K tokens e preço de API de $2.5/1M na entrada e $10/1M na saída, projetado para programação, raciocínio e automação empresarial.
$2.50 / 1M
$10.00 / 1M
128K tokens
38.8%
Principais Diferenças em Resumo
- Llama 3.1 405B is 13.0x cheaper for API usage (input + output tokens combined).
- GPT-4o is faster at 105 tok/s vs 50 tok/s.
- Llama 3.1 405B leads on SWE-bench coding (41.5% vs 38.8%).
| Feature | Llama 3.1 405B | GPT-4o |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 41 |
Provider | Meta | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $2.50 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.53 / 1M | $2.38 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 50 tok/s | 105 tok/s |
SWE-bench Verified Real-world software engineering | 41.5% | 38.8% |
GPQA Diamond Graduate-level reasoning | 53% | 53.6% |
Cached Input Price Prompt caching discount per 1M tokens | $0.75 / 1M | $1.25 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações 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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 3.1 405B · Strengths & Key Advantages
- Arquitetura World’s Largest Open Foundation Transformer avançada da Meta com contexto de 128K tokens
- Excelente relação custo-benefício ($0.0/1M entrada, $0.0/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
GPT-4o · Strengths & Key Advantages
- Arquitetura Omni-Multimodal Dense avançada da OpenAI com contexto de 128K tokens
- Excelente relação custo-benefício ($2.5/1M entrada, $10/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.1 405B | GPT-4o |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.68 |
Output Tokens / Task Tokens generated per task | 24K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.1 405B | GPT-4o |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.1-405B | gpt-4o |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-07 | 2024-05 |
| Knowledge Cutoff | December 2023 | October 2023 |
| Architecture | World’s Largest Open Foundation Transformer | Omni-Multimodal Dense |
| Parameters | 405B | 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) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($2.5/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $1.25/1M (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) · 251 GB VRAM (4-bit Quantized) · 871 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 405B vs GPT-4o: Respostas Rápidas (FAQ)
Which is more intelligent, Llama 3.1 405B or GPT-4o?
Llama 3.1 405B is more intelligent. It scores 48 compared with 41 on the composite Intelligence Index.
Which model is faster?
GPT-4o is faster, generating 105 tokens per second compared with 50 tokens per second.
Which model is cheaper to run?
Llama 3.1 405B is cheaper at $0.53 per 1M tokens blended, compared with $2.38 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4o has lower latency, with a time to first token of 0.41s compared with 0.85s.
Veredito
Verdict & Model Selection Recommendation: Choose Llama 3.1 405B if your priority is Synthetic data generation, smaller model distillation, national sovereign AI, and air-gapped defense labs. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4o if your workflow prioritizes Omni-modal voice applications, conversational agents, and structured enterprise APIs. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Perguntas Frequentes (FAQ)
How does Llama 3.1 405B compare to GPT-4o in API token pricing and cost per token?+
Llama 3.1 405B costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas GPT-4o costs $2.50 input and $10.00 output. Overall, Llama 3.1 405B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Llama 3.1 405B or GPT-4o?+
Both Llama 3.1 405B and GPT-4o offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Llama 3.1 405B or GPT-4o?+
Llama 3.1 405B scores 41.5% on SWE-bench Verified while GPT-4o scores 38.8%. For autonomous software engineering and multi-file debugging, Llama 3.1 405B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 3.1 405B and GPT-4o?+
Llama 3.1 405B is optimized for low-latency direct generation. In contrast, GPT-4o focuses on standard fast inference.
How does inference speed (tokens per second) compare between Llama 3.1 405B and GPT-4o?+
Llama 3.1 405B generates approximately 50 tokens per second, while GPT-4o delivers 105 tokens per second. In latency-sensitive production environments, GPT-4o provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 3.1 405B and GPT-4o to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3.1 405B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-4o. This tiered routing pattern cuts overall API bills by up to 70%.
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.
Fontes oficiais: GetInfoUs Independent AI Benchmarks & Official API Specs