Head-to-Head Model Comparison

o3 vs DeepSeek-R1-0528

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
o3 vs DeepSeek-R1-0528: Benchmarks, Pricing & Speed Comparison

o3 dari OpenAI dan DeepSeek-R1-0528 dari DeepSeek adalah dua model AI unggulan. o3 menawarkan jendela konteks 200K token dengan harga $2.00/$8.00 per 1 juta token, sedangkan DeepSeek-R1-0528 menyediakan konteks 128K token seharga $0.55/$2.19 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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OpenAI

o3

Mesin Penalaran Mendalam

o3 dari OpenAI adalah model AI yang dirancang untuk penalaran logis mendalam, matematika tingkat lanjut, dan analisis ilmiah, menghadirkan jendela konteks 200K, kecepatan 75 token/detik, indeks kecerdasan 56/100, serta tarif $2/$8 per 1M to

Intelligence Index56 / 100
Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

69.1%

DeepSeek

DeepSeek-R1-0528

Penalaran Bobot Terbuka

DeepSeek-R1-0528 dari DeepSeek adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 128K, kecepatan 48 token/detik, indeks kecerdasan 52/100, s

Intelligence Index52 / 100
Input Token:

$0.55 / 1M

Output Token:

$2.19 / 1M

Context Window:

128K tokens

SWE-bench Verified:

51.8%

Perbedaan Utama Sekilas

  • DeepSeek-R1-0528 is 3.6x cheaper for API usage (input + output tokens combined).
  • o3 has a larger context window (200K vs 128K tokens).
  • o3 is faster at 75 tok/s vs 48 tok/s.
  • o3 leads on SWE-bench coding (69.1% vs 51.8%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
Featureo3DeepSeek-R1-0528
Intelligence Index
Composite reasoning & knowledge score (0-100)
5652
Provider
OpenAIDeepSeek
Input Price
Cost per million input tokens
$2.00 / 1M$0.55 / 1M
Output Price
Cost per million output tokens
$8.00 / 1M$2.19 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.55 / 1M$0.43 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
100K8K
Throughput Speed
Tokens generated per second
75 tok/s48 tok/s
SWE-bench Verified
Real-world software engineering
69.1%51.8%
GPQA Diamond
Graduate-level reasoning
76.5%74.2%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.14 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework
Coding
o3
+17.3% SWE-bench lead
Cost
DeepSeek-R1-0528
3.6x cheaper monthly runtime
Speed
o3
+56% faster generation
Context
o3
200K tokens window

Skor Benchmark Terverifikasi

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

o3DeepSeek-R1-0528
SWE-bench Verified (Autonomous Coding)

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

69.1%vs51.8%
GPQA Diamond (Doctorate-Level Science & Math)

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

76.5%vs74.2%
MMLU-Pro (Multi-task Language Understanding)

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

87%vs85.6%
LMSYS Chatbot Arena Elo

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

1345ptsvs1355pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

12.9%vs12%
Terminal-Bench (Agentic Coding & Terminal Use)

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

42%vs34%
SciCode (Scientific Computing)

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

42.5%vs31.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

70.5%vs54%
AIME (Advanced Math Competition)

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

91.6%vs85%
Long-Context Reasoning

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

78.5%vs78%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs48 tok/s

Latensi & Waktu Respons

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
o32.80s
DeepSeek-R1-05280.90s
Est. End-to-End Response (500 tokens)TTFT + generation time
o39.47s
DeepSeek-R1-052811.32s

Simulator Biaya Token Interaktif

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)
o3 Monthly API Bill
$10.95 / month
$2/1M in · $8/1M out
DeepSeek-R1-0528 Monthly API Bill
$3.01 / month
$0.55/1M in · $2.19/1M out
DeepSeek-R1-0528 saves approximately $7.94 / month (73%) under this workload.3.6x Savings

o3 · Strengths & Key Advantages

  • Jendela konteks 200K dengan kapasitas output maksimum hingga 100K token (SWE-bench Verified 69.1% · GPQA Diamond 76.5%)
  • Kecepatan generasi tinggi 75 token/detik dengan latensi token pertama (TTFT) hanya 2800 ms
  • Skema harga API kompetitif (tarif $2/$8 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
  • Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
  • Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya

DeepSeek-R1-0528 · Strengths & Key Advantages

  • Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 51.8% · GPQA Diamond 74.2%)
  • Kecepatan generasi tinggi 48 token/detik dengan latensi token pertama (TTFT) hanya 900 ms
  • Bobot terbuka (671B (37B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
  • Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

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

Metrico3DeepSeek-R1-0528
Cost per Task
Estimated USD to finish one task
$1.35$0.62
Output Tokens / Task
Tokens generated per task
48K45K
Reasoning Tokens / Task
Thinking tokens burned per task
26K25K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Featureo3DeepSeek-R1-0528
Official API Model ID (SDK)o3deepseek-reasoner
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-042025-05
Knowledge CutoffJune 2024March 2025
ArchitectureReasoning transformer with adjustable thinking effortOpen-Weights Reasoning MoE
ParametersConfidential671B (37B active MoE)
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($2/1M in) across full 200K windowFlat rate ($0.55/1M in) across full 128K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0.5/1M (no extra cache-write fee)Automatic prefix caching at $0.14/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
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 & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMCloud-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) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpen-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 Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

o3 vs DeepSeek-R1-0528: Jawaban Cepat (FAQ)

Which is more intelligent, o3 or DeepSeek-R1-0528?

o3 is more intelligent. It scores 56 compared with 52 on the composite Intelligence Index.

Which model is faster?

o3 is faster, generating 75 tokens per second compared with 48 tokens per second.

Which model is cheaper to run?

DeepSeek-R1-0528 is cheaper at $0.43 per 1M tokens blended, compared with $1.55 (7:2:1 cache:input:output ratio).

Which model has lower latency?

DeepSeek-R1-0528 has lower latency, with a time to first token of 0.90s compared with 2.80s.

Which model has a larger context window?

o3 has a larger context window, supporting 200K tokens compared with 128K tokens.

Kesimpulan

Baik o3 (Sangat cocok untuk penalaran logis mendalam, matematika tingkat lanjut, dan analisis ilmiah, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun DeepSeek-R1-0528 (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: o3 unggul dalam penalaran dan pemrograman, o3 memberikan kecepatan generasi token lebih tinggi, dan DeepSeek-R1-0528 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, o3 atau DeepSeek-R1-0528?+

Dalam indeks kecerdasan gabungan dan benchmark coding, o3 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API o3 vs DeepSeek-R1-0528?+

o3 mengenakan biaya $2.00 / $8.00 per 1M token input/output, sedangkan DeepSeek-R1-0528 sebesar $0.55 / $2.19. Secara keseluruhan, DeepSeek-R1-0528 lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

o3 mendukung konteks 200K token dibandingkan 128K token pada DeepSeek-R1-0528, sementara o3 unggul dalam kecepatan output token per detik.

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.

Sumber resmi: Dokumentasi API resmi OpenAI & DeepSeek serta evaluasi benchmark independen (2026).