Head-to-Head Model Comparison

Phi-4 vs Claude Opus 5.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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MicrosoftAnthropicopen-sourceflagship

Compare Phi-4 by Microsoft against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.

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Microsoft

Phi-4

Açık Ağırlıklı Model

Microsoft tarafından geliştirilen Phi-4, 16K token bağlam penceresi ve 1M girdi başına $0, 1M çıktı başına $0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.

Intelligence Index80 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

16.4K tokens

SWE-bench Verified:

N/A

Anthropic

Claude Opus 5.5

Derin Akıl Yürütme Modeli

Anthropic tarafından geliştirilen Claude Opus 5.5, 1M token bağlam penceresi ve 1M girdi başına $4.0, 1M çıktı başına $20.0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.

Intelligence Index94 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

84.6%

Bir Bakışta Temel Farklar

  • Phi-4 is 1.0x cheaper for API usage (input + output tokens combined).
  • Claude Opus 5.5 has a larger context window (1M vs 16K tokens).
  • Phi-4 is faster at 135 tok/s vs 78 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeaturePhi-4Claude Opus 5.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
8094
Provider
MicrosoftAnthropic
Input Price
Cost per million input tokens
$0.00 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
16.4K tokens~25 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
4K128K
Throughput Speed
Tokens generated per second
135 tok/s78 tok/s
SWE-bench Verified
Real-world software engineering
N/A84.6%
GPQA Diamond
Graduate-level reasoning
55%82.3%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.40 / 1M

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Phi-4
1.0x cheaper monthly runtime
Speed
Phi-4
+73% faster generation
Context
Claude Opus 5.5
1M tokens window

Doğrulanmış Benchmark Puanları

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

Phi-4Claude Opus 5.5
SWE-bench Verified (Autonomous Coding)

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

N/Avs84.6%
GPQA Diamond (Doctorate-Level Science & Math)

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

55%vs82.3%
MMLU-Pro (Multi-task Language Understanding)

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

78.5%vs91.5%
LMSYS Chatbot Arena Elo

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

1305ptsvs1507pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs19.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs66.4%
SciCode (Scientific Computing)

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

N/Avs58.7%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs83.1%
AIME (Advanced Math Competition)

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

N/Avs95.4%
Long-Context Reasoning

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

N/Avs92.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

135 tok/s vs78 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
Claude Opus 5.51.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
Phi-43.97s
Claude Opus 5.57.81s

Gerçek Zamanlı Token Maliyet Simülatörü

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Phi-4 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Claude Opus 5.5 Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
Phi-4 saves approximately $24.60 / month (100%) under this workload.1.0x Savings

Phi-4 · Strengths & Key Advantages

  • Microsoft imzalı gelişmiş Synthetic-Data Dense Reasoning Transformer mimarisi ve 16K token bağlam kapasitesi
  • Dengeli API maliyeti ($0/1M girdi, $0/1M çıktı) ve yüksek yanıt hızı
  • Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
  • Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
  • Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir

Claude Opus 5.5 · Strengths & Key Advantages

  • Anthropic imzalı gelişmiş Extended Autonomous Hybrid Reasoning Transformer mimarisi ve 1M token bağlam kapasitesi
  • Dengeli API maliyeti ($4.0/1M girdi, $20.0/1M çıktı) ve yüksek yanıt hızı
  • Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
  • Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
  • Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricPhi-4Claude Opus 5.5
Cost per Task
Estimated USD to finish one task
$0.00$3.85
Output Tokens / Task
Tokens generated per task
12K68K
Reasoning Tokens / Task
Thinking tokens burned per task
3K42K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

FeaturePhi-4Claude Opus 5.5
Official API Model ID (SDK)phi-4claude-opus-5-5-20260901
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIAnthropic Messages API + AWS Bedrock + Vertex AI
Release Date2024-122026-09
Knowledge CutoffJune 2024June 2026
ArchitectureSynthetic-Data Dense Reasoning TransformerExtended Autonomous Hybrid Reasoning Transformer
Parameters14BConfidential
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Free across full context window (only KV-cache VRAM scales)Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)$0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Custom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 9 GB VRAM (4-bit Quantized) · 30 GB VRAM (Native BF16)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 ControlSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext, vision
Output Modalitiestexttext

Phi-4 vs Claude Opus 5.5: Hızlı Yanıtlar (SSS)

Which is more intelligent, Phi-4 or Claude Opus 5.5?

Claude Opus 5.5 is more intelligent. It scores 94 compared with 80 on the composite Intelligence Index.

Which model is faster?

Phi-4 is faster, generating 135 tokens per second compared with 78 tokens per second.

Which model is cheaper to run?

Phi-4 is cheaper at $0.00 per 1M tokens blended, compared with $3.08 (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 1.40s.

Which model has a larger context window?

Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 16.4K tokens.

Sonuç

Choose Phi-4 for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve microsoft tabanlı üretim sistemleri, or choose Claude Opus 5.5 for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve anthropic tabanlı üretim sistemleri.

Sıkça Sorulan Sorular (SSS)

Which is cheaper: Phi-4 or Claude Opus 5.5?+

Phi-4 costs $0/1M input and $0/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.

Which model has a larger context window: Phi-4 or Claude Opus 5.5?+

Phi-4 supports 16K tokens whereas Claude Opus 5.5 supports 1M tokens.

AI Terminology & Guide
Explore Glossary →

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

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.

Resmi kaynaklar: Microsoft & Anthropic Official Documentation