o3 vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare o3 by OpenAI against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
Troque e Compare Qualquer Modelo
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Live comparison: no in-depth editorial review has been published for this pair yet.
o3
Motor de Raciocínio ProfundoO o3 da OpenAI é um modelo de IA projetado para raciocínio lógico profundo, matemática avançada e análise científica passo a passo, oferecendo janela de contexto de 200K, velocidade de 75 tokens/s, índice de inteligência de 56/100 e tarifa
$2.00 / 1M
$8.00 / 1M
200K tokens
69.1%
Llama 5
Modelo Base de Pesos AbertosO Llama 5 da Meta é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 5M, velocidade de 65 tokens/s, índice de inteligência de 94/100 e tarifa de ace
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Principais Diferenças em Resumo
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 200K tokens).
- o3 is faster at 75 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 69.1%).
| Feature | o3 | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 56 | 94 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $2.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $8.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.55 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 100K | 128K |
Throughput Speed Tokens generated per second | 75 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 69.1% | 74% |
GPQA Diamond Graduate-level reasoning | 76.5% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.00 / 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.
o3 · Strengths & Key Advantages
- Janela de contexto de 200K com limite máximo de saída de 100K tokens (SWE-bench Verified 69.1% · GPQA Diamond 76.5%)
- Taxa de transferência rápida de 75 tokens/segundo com latência inicial (TTFT) de 2800 ms
- Economia de API competitiva ($2/$8 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas
Considerations & Trade-offs
- Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Llama 5 · Strengths & Key Advantages
- Janela de contexto de 5M com limite máximo de saída de 128K tokens (SWE-bench Verified 74% · GPQA Diamond 84%)
- Taxa de transferência rápida de 65 tokens/segundo com latência inicial (TTFT) de 850 ms
- Pesos abertos (600B+ total (~40-80B )) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o3 | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.35 | $0.00 |
Output Tokens / Task Tokens generated per task | 48K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 26K | 20K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | o3 | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | o3 | meta-llama/Llama-5 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-04 | 2026-04 |
| Knowledge Cutoff | June 2024 | January 2026 |
| Architecture | Reasoning transformer with adjustable thinking effort | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | Confidential | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 200K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.5/1M (no extra cache-write fee) | 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 tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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 | OpenAI Preparedness Framework Audited & System Card Published | 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 |
o3 vs Llama 5: Respostas Rápidas (FAQ)
Which is more intelligent, o3 or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 56 on the composite Intelligence Index.
Which model is faster?
o3 is faster, generating 75 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 $1.55 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 5 has lower latency, with a time to first token of 0.85s compared with 2.80s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 200K tokens.
Veredito
Choose o3 for indicado para raciocínio lógico profundo, matemática avançada e análise científica passo a passo, arquiteturas rag corporativas e integração escalável via api., or choose Llama 5 for indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas rag corporativas e integração escalável via api..
Perguntas Frequentes (FAQ)
Which is cheaper: o3 or Llama 5?+
o3 costs $2/1M input and $8/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: o3 or Llama 5?+
o3 supports 200K 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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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: OpenAI & Meta Official Documentation