GPT-5.4 mini vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-5.4 mini 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.
GPT-5.4 mini
Petit GPT puissantun solide petit GPT pour codage sous-agents et élevé-volume travail.
$0.50 / 1M
$4.00 / 1M
400K tokens
68.5%
Llama 5
Auto-amélioration récursivede Meta phare ouvert modèle intégrant natif agent training, recursive self-improvement, et 5M token contexte.
$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 400K tokens).
- GPT-5.4 mini is faster at 175 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 68.5%).
| Caractéristique | GPT-5.4 mini | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 59 | 94 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $0.50 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $4.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.53 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 175 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 68.5% | 74% |
GPQA Diamond Graduate-level reasoning | 72.5% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 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.
GPT-5.4 mini · Strengths & Key Advantages
- Excellent prix-à-capacité pour sous-agents
- rapide enough pour parallel outil boucles
- fiable structuré sortie
- excellent débit pour parallel sous-agent fleets
Considerations & Trade-offs
- Weaker que GPT-5.4 sur hard raisonnement
- Shorter contexte que le 5.4 phare
Llama 5 · Strengths & Key Advantages
- 5 millions token contexte avec natif multi-audio/vidéo compréhension
- Boucle d'entraînement à auto-amélioration récursive
- Completely à poids ouverts avec d'entreprise commercial license
Considerations & Trade-offs
- complexe MoE hébergement requirements
- Les modifications du tokeniseur 4B nécessitent la mise à jour des environnements d'ajustement existants
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.4 mini | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.62 | $0.00 |
Output Tokens / Task Tokens generated per task | 52K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 27K | 20K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | GPT-5.4 mini | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-4-mini | meta-llama/Llama-5 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-03 | 2026-04 |
| Knowledge Cutoff | August 2025 | January 2026 |
| Architecture | Compact agent-ready reasoning model | 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 ($0.5/1M in) across full 400K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.05/1M (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 |
GPT-5.4 mini vs Llama 5: Réponses Rapides (FAQ)
Which is more intelligent, GPT-5.4 mini or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 59 on the composite Intelligence Index.
Which model is faster?
GPT-5.4 mini is faster, generating 175 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 $0.53 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5.4 mini has lower latency, with a time to first token of 0.76s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 400K tokens.
Verdict
Choose GPT-5.4 mini for sous-agents, parallel outil calls et élevé-volume codage tâches, or choose Llama 5 for autonome open source multi-agent systems, profond logiciel refactorisation, et de pointe recherche.
Questions fréquentes
Which is cheaper: GPT-5.4 mini or Llama 5?+
GPT-5.4 mini costs $0.5/1M input and $4/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT-5.4 mini or Llama 5?+
GPT-5.4 mini supports 400K 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