Phi-4 vs DeepSeek-R1 Distill Llama 70B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Phi-4 di Microsoft e DeepSeek-R1 Distill Llama 70B di DeepSeek sono due modelli di IA di riferimento. Phi-4 offre una finestra di contesto di 16.4K token a $0.00/$0.00 per milione di token, mentre DeepSeek-R1 Distill Llama 70B mette a disposizione 131.1K token di contesto a $0.35/$1.05 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
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Phi-4
Modello Base a Pesi ApertiPhi-4 di Microsoft 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 16K, velocità di 135 token/s, indice di intelligenza di 80/100 e tarif
$0.00 / 1M
$0.00 / 1M
16.4K tokens
N/A
DeepSeek-R1 Distill Llama 70B
Ragionamento a Pesi ApertiDeepSeek-R1 Distill Llama 70B di DeepSeek è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 131K, velocità di 48 token/s, indice di intelligenza di 4
$0.35 / 1M
$1.05 / 1M
131.1K tokens
57.5%
Differenze Chiave a Colpo d’Occhio
- Phi-4 is 1.0x cheaper for API usage (input + output tokens combined).
- DeepSeek-R1 Distill Llama 70B has a larger context window (131K vs 16K tokens).
- Phi-4 is faster at 135 tok/s vs 48 tok/s.
| Feature | Phi-4 | DeepSeek-R1 Distill Llama 70B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 80 | 48 |
Provider | Microsoft | DeepSeek |
Input Price Cost per million input tokens | $0.00 / 1M | $0.35 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $1.05 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.20 / 1M |
Context Window Maximum input + output tokens | 16.4K tokens~25 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 135 tok/s | 48 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 57.5% |
GPQA Diamond Graduate-level reasoning | 55% | 70% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.04 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkEvaluations ongoing
Punteggi 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.
Phi-4 · Strengths & Key Advantages
- Finestra di contesto da 16K con capacità massima di output fino a 4K token (GPQA Diamond 55% · MMLU-Pro 78.5%)
- Throughput elevato di 135 token/secondo con latenza al primo token (TTFT) di soli 270 ms
- Pesi aperti (14B) 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
DeepSeek-R1 Distill Llama 70B · Strengths & Key Advantages
- Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 57.5% · GPQA Diamond 70%)
- Throughput elevato di 48 token/secondo con latenza al primo token (TTFT) di soli 880 ms
- Pesi aperti (70B) 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 | Phi-4 | DeepSeek-R1 Distill Llama 70B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.42 |
Output Tokens / Task Tokens generated per task | 12K | 27K |
Reasoning Tokens / Task Thinking tokens burned per task | 3K | 21K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Phi-4 | DeepSeek-R1 Distill Llama 70B |
|---|---|---|
| Official API Model ID (SDK) | phi-4 | deepseek-reasoner |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-12 | 2025-01 |
| Knowledge Cutoff | June 2024 | July 2024 |
| Architecture | Synthetic-Data Dense Reasoning Transformer | Dense Distilled Reasoning Transformer |
| Parameters | 14B | 70B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.35/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.035/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 9 GB VRAM (4-bit Quantized) · 30 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
Phi-4 vs DeepSeek-R1 Distill Llama 70B: Risposte Rapide (FAQ)
Which is more intelligent, Phi-4 or DeepSeek-R1 Distill Llama 70B?
Phi-4 is more intelligent. It scores 80 compared with 48 on the composite Intelligence Index.
Which model is faster?
Phi-4 is faster, generating 135 tokens per second compared with 48 tokens per second.
Which model is cheaper to run?
Phi-4 is cheaper at $0.00 per 1M tokens blended, compared with $0.20 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Phi-4 has lower latency, with a time to first token of 0.27s compared with 0.88s.
Which model has a larger context window?
DeepSeek-R1 Distill Llama 70B has a larger context window, supporting 131.1K tokens compared with 16.4K tokens.
Verdetto
Sia Phi-4 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia DeepSeek-R1 Distill Llama 70B (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Phi-4 guida nei benchmark di ragionamento e programmazione, Phi-4 garantisce una generazione più rapida e Phi-4 offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, Phi-4 o DeepSeek-R1 Distill Llama 70B?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Phi-4. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra Phi-4 e DeepSeek-R1 Distill Llama 70B?+
Phi-4 costa $0.00 / $0.00 per milione di token di input/output, mentre DeepSeek-R1 Distill Llama 70B costa $0.35 / $1.05. Nel costo combinato, Phi-4 risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Phi-4 supporta 16.4K token di contesto contro 131.1K token di DeepSeek-R1 Distill Llama 70B, mentre Phi-4 offre una velocità di generazione superiore.
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: Documentazione API ufficiale di Microsoft e DeepSeek, schede di sistema e benchmark indipendenti (2026).