o3 vs DeepSeek-R1-0528
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
o3 by OpenAI and DeepSeek-R1-0528 by DeepSeek represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, DeepSeek-R1-0528 holds a notable pricing advantage for token ingestion. On output generation, DeepSeek-R1-0528 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, o3 provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, o3 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
Changez et Comparez les Modèles
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o3
Raisonneur réfléchile o-series phare reasoner — solide sur difficult maths, codage et multi-étapes analyse.
$2.00 / 1M
$8.00 / 1M
200K tokens
69.1%
DeepSeek-R1-0528
Mise à jour de raisonneur open sourceMay 2025 checkpoint de DeepSeek-R1 offrant refined reinforcement learning, stabilized chaîne de pensée, et improved mathématiques raisonnement.
$0.55 / 1M
$2.19 / 1M
128K tokens
51.8%
Différences Clés en un Coup d’Œil
- 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%).
| Caractéristique | o3 | DeepSeek-R1-0528 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 56 | 52 |
Provider | OpenAI | DeepSeek |
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 text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 100K | 8K |
Throughput Speed Tokens generated per second | 75 tok/s | 48 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 |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o3 · Strengths & Key Advantages
- puissant deliberate raisonnement avec vision support
- Significantly cheaper que o1-pro pour comparable précision
- Adjustable raisonnement effort à trade coût against qualité
- solide à outil-augmented codage agentique boucles
Considerations & Trade-offs
- Outperformed par GPT-5 sur raisonnement et codage
- pensée tokens inflate le effective coût par tâche
DeepSeek-R1-0528 · Strengths & Key Advantages
- à poids ouverts raisonnement champion avec transparent et inspectable chaîne de pensée
- Highly compétitif GPQA Diamond (74.2%) et AIME (85.0%) competition mathématiques scores
- Inexpensive API tarification et complet sur-premise déploiement rights
Considerations & Trade-offs
- Verbose token génération resulting dans supérieur latence par question
- Strict 128K fenêtre de contexte et texte-seulement modality
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o3 | DeepSeek-R1-0528 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.35 | $0.62 |
Output Tokens / Task Tokens generated per task | 48K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 26K | 25K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | o3 | DeepSeek-R1-0528 |
|---|---|---|
| Official API Model ID (SDK) | o3 | deepseek-reasoner |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-04 | 2025-05 |
| Knowledge Cutoff | June 2024 | March 2025 |
| Architecture | Reasoning transformer with adjustable thinking effort | Open-Weights Reasoning MoE |
| Parameters | Confidential | 671B (37B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | Unlimited (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 window | Flat rate ($0.55/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic 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 Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-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 Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-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 Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
o3 vs DeepSeek-R1-0528: Réponses Rapides (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.
Verdict
Verdict & Model Selection Recommendation: Choose o3 if your priority is Hard maths, science and agentic coding where accuracy beats latency. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek-R1-0528 if your workflow prioritizes Mathematical theorem proving, rigorous algorithm design, and privacy-compliant research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does o3 compare to DeepSeek-R1-0528 in API token pricing and cost per token?+
o3 costs $2.00 per 1M input tokens and $8.00 per 1M output tokens, whereas DeepSeek-R1-0528 costs $0.55 input and $2.19 output. Overall, DeepSeek-R1-0528 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: o3 or DeepSeek-R1-0528?+
o3 supports a context window of 200K tokens (~150k words), while DeepSeek-R1-0528 supports 128K tokens (~96k words). Therefore, o3 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: o3 or DeepSeek-R1-0528?+
o3 scores 69.1% on SWE-bench Verified while DeepSeek-R1-0528 scores 51.8%. For autonomous software engineering and multi-file debugging, o3 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between o3 and DeepSeek-R1-0528?+
o3 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, DeepSeek-R1-0528 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between o3 and DeepSeek-R1-0528?+
o3 generates approximately 75 tokens per second, while DeepSeek-R1-0528 delivers 48 tokens per second. In latency-sensitive production environments, o3 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining o3 and DeepSeek-R1-0528 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek-R1-0528 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to o3. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs