de Microsoft 5.6B compact ouvert modèle supporting simultaneous texte, vision, et audio compréhension pour local périphérie intelligence.
Technical Specifications
- Provider
- Microsoft
- Category
- open-source
- Release Date
- 2025-02
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- phi-4-multimodal
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Free across full context window (only KV-cache VRAM scales)
- Prompt Cache Write & TTL Policy
- Automatic prefix KV-caching in vLLM / SGLang (0 cost)
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 3 GB VRAM (4-bit Quantized) · 12 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Phi-4 Multimodal Cost, Context, Hardware & API Integration
Open-weights or free-tier model: $0 API license cost when self-hosted.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Phi-4 Multimodal:
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 71%
- HumanEval
- 82%
- Chatbot Arena Elo
- 1250pts
Strengths
- Tri-modal traitement (audio, image, texte) dans un featherweight 5.6B paramètre modèle
- fonctionne smoothly sur mobile phones, embedded devices, et léger laptops
- étendu 128K fenêtre de contexte avec à poids ouverts freedom
Limitations & Trade-offs
- inférieur précision sur complexe abstract raisonnement que grand phare modèles
- audio sortie est texte-seulement (nécessite companion parole synthesizer)
Editorial Assessment
un compact marvel bringing tri-modal perception à personal périphérie matériel.
Best for: périphérie multimodal devices, offline smart cameras, et léger personal assistants
Compare Phi-4 Multimodal
Frequently Asked Questions about Phi-4 Multimodal
How much does the Phi-4 Multimodal API cost per 1M tokens?+
Phi-4 Multimodal by Microsoft is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.
Should I use a $20/month subscription or the Phi-4 Multimodal pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Phi-4 Multimodal's context window?+
Phi-4 Multimodal features a 128K tokens context window, which equals approximately 96,000 English words or ~256 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Phi-4 Multimodal locally on my own GPU (Open Weights)?+
Yes, Phi-4 Multimodal (5.6B) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Microsoft train on data sent to the Phi-4 Multimodal API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Open-Weights Research & Commercial License. Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Phi-4 Multimodal perform on coding and reasoning benchmarks?+
Phi-4 Multimodal achieves a composite Intelligence Index of 75/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 71% on MMLU-Pro, and 1250 pts on Chatbot Arena Elo.
What is Phi-4 Multimodal best used for?+
Phi-4 Multimodal is best suited for: périphérie multimodal devices, offline smart cameras, et léger personal assistants. Assessment: un compact marvel bringing tri-modal perception à personal périphérie matériel.
Microsoft Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Microsoft's flagship reasoning tier:
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Phi-4 Multimodal:
L'unité textuelle de base traitée par un modèle linguistique, représentant généralement 3 à 4 caractères ou environ 0,75 mot. Par exemple, une phrase courante est découpée en plusieurs jetons.
Une suite de tests standardisés permettant d'évaluer et de comparer objectivement les performances des modèles d'IA sur des tâches ciblées (code avec SWE-bench, mathématiques avec AIME, etc.).
Le nombre maximal de jetons qu'un modèle peut traiter simultanément (entrée et sortie comprises). Une fenêtre de 1 000 000 jetons permet d'analyser environ 750 000 mots en une seule requête.
La capacité d'un LLM à déterminer quand une action externe est requise et à générer des arguments structurés pour invoquer des API, calculatrices ou bases de données avant de poursuivre.
Un standard ouvert créé par Anthropic permettant aux agents d'IA de se connecter de façon uniforme aux systèmes de fichiers locaux, aux environnements de développement et aux API tierces.
Un réseau neuronal multimodal conçu pour analyser, raisonner et générer conjointement des données visuelles (images, schémas, interfaces) et du langage naturel au sein d'une même structure.
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.