Head-to-Head Model Comparison

Phi-4 vs DeepSeek-R1 Distill Llama 70B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

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Phi-4Profile

DeepSeek-R1 Distill Llama 70BProfile

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Popular Matchups:

Microsoft

Phi-4

Açık Ağırlıklı Temel Model

Microsoft tarafından geliştirilen Phi-4; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 16K bağlam penceresi, 135 token/sn hız, 80/100 zeka indeksi ve ücretsiz açık ağırlık eriş

Intelligence Index80 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

16.4K tokens

SWE-bench Verified:

N/A

DeepSeek

DeepSeek-R1 Distill Llama 70B

Açık Ağırlıklı Akıl Yürütme

DeepSeek tarafından geliştirilen DeepSeek-R1 Distill Llama 70B; yüksek verimli açık ağırlıklı düşünce zinciri (Chain-of-Thought) akıl yürütme için tasarlanmış olup 131K bağlam penceresi, 48 token/sn hız, 48/100 zeka indeksi ve 1M token başı

Intelligence Index48 / 100

Input Token:

$0.35 / 1M

Output Token:

$1.05 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

57.5%

Bir Bakışta Temel Farklar

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.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

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

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Phi-4

1.0x cheaper monthly runtime

Speed

Phi-4

+181% faster generation

Context

DeepSeek-R1 Distill Llama 70B

131.1K tokens window

Doğrulanmış Benchmark Puanları

Official evaluation metrics verified by independent third-party research labs.

Phi-4DeepSeek-R1 Distill Llama 70B

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs57.5%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

55%vs70%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

78.5%vs78.6%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1305ptsvs1330pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs11.2%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs34.2%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs39.8%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs60.5%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs70%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs70.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

135 tok/s vs48 tok/s

Gecikme Süresi ve Yanıt Hızı

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Phi-40.27s

DeepSeek-R1 Distill Llama 70B0.88s

Est. End-to-End Response (500 tokens)TTFT + generation time

Phi-43.97s

DeepSeek-R1 Distill Llama 70B11.30s

Gerçek Zamanlı Token Maliyet Simülatörü

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Phi-4 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

DeepSeek-R1 Distill Llama 70B Monthly API Bill

$1.52 / month

$0.35/1M in · $1.05/1M out

Phi-4 saves approximately $1.52 / month (100%) under this workload.1.0x Savings

Phi-4 · Strengths & Key Advantages

16K bağlam penceresi ve yanıt başına maksimum 4K çıktı tokenı kapasitesi (GPQA Diamond 55% · MMLU-Pro 78.5%)
Sadece 270 ms ilk token gecikmesi (TTFT) ile saniyede 135 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (14B)

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

DeepSeek-R1 Distill Llama 70B · Strengths & Key Advantages

131K bağlam penceresi ve yanıt başına maksimum 8K çıktı tokenı kapasitesi (SWE-bench Verified 57.5% · GPQA Diamond 70%)
Sadece 880 ms ilk token gecikmesi (TTFT) ile saniyede 48 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (70B)

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

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

Teknik Özellikler

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: Hızlı Yanıtlar (SSS)

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.

Sonuç

Hem Phi-4 (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de DeepSeek-R1 Distill Llama 70B (yüksek verimli açık ağırlıklı düşünce zinciri (Chain-of-Thought) akıl yürütme, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: Phi-4 karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Phi-4 daha yüksek üretim hızı sağlar; Phi-4 ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Phi-4 mi yoksa DeepSeek-R1 Distill Llama 70B mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Phi-4 önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Phi-4 ve DeepSeek-R1 Distill Llama 70B API fiyatları nasıl karşılaştırılır?+

Phi-4 1M giriş/çıkış tokeni başına $0.00 / $0.00 ücretlendirilirken, DeepSeek-R1 Distill Llama 70B $0.35 / $1.05 seviyesindedir. Karma maliyette Phi-4 daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Phi-4 16.4K token bağlam penceresi sunarken DeepSeek-R1 Distill Llama 70B 131.1K token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Phi-4 öndedir.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Phi-4 vs DeepSeek-R1 Distill Llama 70B

İstediğiniz Modelleri Seçin ve Karşılaştırın

Phi-4

DeepSeek-R1 Distill Llama 70B

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

Phi-4 · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek-R1 Distill Llama 70B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

Phi-4 vs DeepSeek-R1 Distill Llama 70B: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

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