o3-mini vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare o3-mini by OpenAI against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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o3-mini
Motore di Ragionamento Profondoo3-mini di OpenAI è un modello di IA progettato per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, con finestra di contesto da 200K, velocità di 85 token/s, indice di intelligenza di 51/100 e tariffa di $1.
$1.10 / 1M
$4.40 / 1M
200K tokens
49%
Llama 5
Modello Base a Pesi ApertiLlama 5 di Meta è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 5M, velocità di 65 token/s, indice di intelligenza di 94/100 e tariffa di
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Differenze Chiave a Colpo d’Occhio
- 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-mini is faster at 85 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 49.0%).
| Feature | o3-mini | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 51 | 94 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $1.10 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $4.40 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.05 / 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 | 85 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 49% | 74% |
GPQA Diamond Graduate-level reasoning | 79.7% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.55 / 1M | $0.00 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o3-mini · Strengths & Key Advantages
- Finestra di contesto da 200K con capacità massima di output fino a 100K token (SWE-bench Verified 49% · GPQA Diamond 79.7%)
- Throughput elevato di 85 token/secondo con latenza al primo token (TTFT) di soli 1400 ms
- Economia API competitiva ($1.1/$4.4 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati
Considerations & Trade-offs
- Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Llama 5 · Strengths & Key Advantages
- Finestra di contesto da 5M con capacità massima di output fino a 128K token (SWE-bench Verified 74% · GPQA Diamond 84%)
- Throughput elevato di 65 token/secondo con latenza al primo token (TTFT) di soli 850 ms
- Pesi aperti (600B+ total (~40-80B )) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o3-mini | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.30 | $0.00 |
Output Tokens / Task Tokens generated per task | 45K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 25K | 20K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | o3-mini | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | o3-mini | meta-llama/Llama-5 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-01 | 2026-04 |
| Knowledge Cutoff | October 2023 | January 2026 |
| Architecture | Chain-of-Thought Reasoning Dense | 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 ($1.1/1M in) across full 200K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.55/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | 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) | 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 | text, vision, video, audio |
| Output Modalities | text | text |
o3-mini vs Llama 5: Risposte Rapide (FAQ)
Which is more intelligent, o3-mini or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 51 on the composite Intelligence Index.
Which model is faster?
o3-mini is faster, generating 85 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 $1.05 (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 1.40s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 200K tokens.
Verdetto
Choose o3-mini for ideale per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, sistemi rag aziendali e integrazioni api scalabili., or choose Llama 5 for ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi rag aziendali e integrazioni api scalabili..
Domande Frequenti (FAQ)
Which is cheaper: o3-mini or Llama 5?+
o3-mini costs $1.1/1M input and $4.4/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: o3-mini or Llama 5?+
o3-mini 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.
Fonti ufficiali: OpenAI & Meta Official Documentation