o4-mini-deep-research vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare o4-mini-deep-research by OpenAI against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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Live comparison: no in-depth editorial review has been published for this pair yet.
o4-mini-deep-research
Moteur de Raisonnement Profondo4-mini-deep-research de OpenAI est un modèle d’IA conçu pour le raisonnement logique profond, les mathématiques formelles et l’analyse scientifique, offrant une fenêtre de contexte de 200K, une vitesse de 70 tokens/s, un indice d’intellige
$2.00 / 1M
$8.00 / 1M
200K tokens
48.5%
Llama 5
Modèle de Base à Poids OuvertsLlama 5 de Meta est un modèle d’IA conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, offrant une fenêtre de contexte de 5M, une vitesse de 65 tokens/s, un indice d’intelligence de 94/100
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Différences Clés en un Coup d’Œil
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 200K tokens).
- o4-mini-deep-research is faster at 70 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 48.5%).
| Caractéristique | o4-mini-deep-research | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 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 | 70 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 48.5% | 74% |
GPQA Diamond Graduate-level reasoning | 68% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.00 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o4-mini-deep-research · Strengths & Key Advantages
- Fenêtre de contexte de 200K et capacité de sortie maximale de 100K tokens (SWE-bench Verified 48.5% · GPQA Diamond 68%)
- Débit rapide de 70 tokens/seconde avec une latence au premier token (TTFT) de 2400 ms
- Tarification API optimisée ($2/$8 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
- Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
- Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée
Llama 5 · Strengths & Key Advantages
- Fenêtre de contexte de 5M et capacité de sortie maximale de 128K tokens (SWE-bench Verified 74% · GPQA Diamond 84%)
- Débit rapide de 65 tokens/seconde avec une latence au premier token (TTFT) de 850 ms
- Poids ouverts (600B+ total (~40-80B )) permettant un déploiement sur site ou cloud privé via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Nécessite des serveurs GPU dotés de suffisamment de mémoire VRAM pour l’inférence locale à pleine précision
- Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o4-mini-deep-research | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.10 | $0.00 |
Output Tokens / Task Tokens generated per task | 75K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 30K | 20K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | o4-mini-deep-research | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | o4-mini-deep-research | meta-llama/Llama-5 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-06 | 2026-04 |
| Knowledge Cutoff | June 2024 | January 2026 |
| Architecture | Compact agentic reasoning transformer | 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 |
o4-mini-deep-research vs Llama 5: Réponses Rapides (FAQ)
Which is more intelligent, o4-mini-deep-research or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 48 on the composite Intelligence Index.
Which model is faster?
o4-mini-deep-research is faster, generating 70 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 2.40s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 200K tokens.
Verdict
Choose o4-mini-deep-research for conçu pour le raisonnement logique profond, les mathématiques formelles et l’analyse scientifique, l’intégration rag et les pipelines de production évolutifs., or choose Llama 5 for conçu pour l’auto-hébergement sur infrastructure privée, le fine-tuning et la souveraineté des données, l’intégration rag et les pipelines de production évolutifs..
Questions fréquentes
Which is cheaper: o4-mini-deep-research or Llama 5?+
o4-mini-deep-research 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: o4-mini-deep-research or Llama 5?+
o4-mini-deep-research 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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: OpenAI & Meta Official Documentation