o4-mini-deep-research vs o3-deep-research
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
o4-mini-deep-research da OpenAI e o3-deep-research da OpenAI são dois modelos de IA de destaque. O o4-mini-deep-research oferece uma janela de contexto de 200K tokens a $2.00/$8.00 por milhão de tokens, enquanto o o3-deep-research oferece 200K tokens de contexto a $10.00/$40.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
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o4-mini-deep-research
Motor de Raciocínio ProfundoO o4-mini-deep-research da OpenAI é um modelo de IA projetado para raciocínio lógico profundo, matemática avançada e análise científica passo a passo, oferecendo janela de contexto de 200K, velocidade de 70 tokens/s, índice de inteligência
$2.00 / 1M
$8.00 / 1M
200K tokens
48.5%
o3-deep-research
Motor de Raciocínio ProfundoO o3-deep-research da OpenAI é um modelo de IA projetado para raciocínio lógico profundo, matemática avançada e análise científica passo a passo, oferecendo janela de contexto de 200K, velocidade de 45 tokens/s, índice de inteligência de 55
$10.00 / 1M
$40.00 / 1M
200K tokens
55%
Principais Diferenças em Resumo
- o4-mini-deep-research is 5.0x cheaper for API usage (input + output tokens combined).
- o4-mini-deep-research is faster at 70 tok/s vs 45 tok/s.
- o3-deep-research leads on SWE-bench coding (55.0% vs 48.5%).
| Feature | o4-mini-deep-research | o3-deep-research |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 55 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $2.00 / 1M | $10.00 / 1M |
Output Price Cost per million output tokens | $8.00 / 1M | $40.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.55 / 1M | $7.75 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 100K | 100K |
Throughput Speed Tokens generated per second | 70 tok/s | 45 tok/s |
SWE-bench Verified Real-world software engineering | 48.5% | 55% |
GPQA Diamond Graduate-level reasoning | 68% | 74.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $2.50 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o4-mini-deep-research · Strengths & Key Advantages
- Janela de contexto de 200K com limite máximo de saída de 100K tokens (SWE-bench Verified 48.5% · GPQA Diamond 68%)
- Taxa de transferência rápida de 70 tokens/segundo com latência inicial (TTFT) de 2400 ms
- Economia de API competitiva ($2/$8 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas
Considerations & Trade-offs
- Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
o3-deep-research · Strengths & Key Advantages
- Janela de contexto de 200K com limite máximo de saída de 100K tokens (SWE-bench Verified 55% · GPQA Diamond 74.5%)
- Taxa de transferência rápida de 45 tokens/segundo com latência inicial (TTFT) de 3400 ms
- Economia de API competitiva ($10/$40 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas
Considerations & Trade-offs
- Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
- Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o4-mini-deep-research | o3-deep-research |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.10 | $7.28 |
Output Tokens / Task Tokens generated per task | 75K | 90K |
Reasoning Tokens / Task Thinking tokens burned per task | 30K | 42K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | o4-mini-deep-research | o3-deep-research |
|---|---|---|
| Official API Model ID (SDK) | o4-mini-deep-research | o3-deep-research |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-06 | 2025-06 |
| Knowledge Cutoff | June 2024 | June 2024 |
| Architecture | Compact agentic reasoning transformer | Agentic reasoning transformer with web and file tools |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 200K window | Flat rate ($10/1M in) across full 200K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.5/1M (no extra cache-write fee) | Automatic prefix caching at $2.5/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
o4-mini-deep-research vs o3-deep-research: Respostas Rápidas (FAQ)
Which is more intelligent, o4-mini-deep-research or o3-deep-research?
o3-deep-research is more intelligent. It scores 55 compared with 48 on the composite Intelligence Index.
Which model is faster?
o4-mini-deep-research is faster, generating 70 tokens per second compared with 45 tokens per second.
Which model is cheaper to run?
o4-mini-deep-research is cheaper at $1.55 per 1M tokens blended, compared with $7.75 (7:2:1 cache:input:output ratio).
Which model has lower latency?
o4-mini-deep-research has lower latency, with a time to first token of 2.40s compared with 3.40s.
Veredito
Tanto o4-mini-deep-research (Indicado para raciocínio lógico profundo, matemática avançada e análise científica passo a passo, arquiteturas RAG corporativas e integração escalável via API.) quanto o3-deep-research (Indicado para raciocínio lógico profundo, matemática avançada e análise científica passo a passo, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: o3-deep-research lidera em inteligência geral e programação, o4-mini-deep-research oferece geração mais rápida e o4-mini-deep-research apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, o4-mini-deep-research ou o3-deep-research?+
Nas avaliações de inteligência e código, o3-deep-research leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, o4-mini-deep-research ou o3-deep-research?+
O o4-mini-deep-research custa $2.00 / $8.00 por 1M de tokens (entrada/saída) e o o3-deep-research custa $10.00 / $40.00. No custo combinado, o4-mini-deep-research é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O o4-mini-deep-research suporta 200K tokens de contexto contra 200K tokens do o3-deep-research, enquanto o4-mini-deep-research entrega maior velocidade de geração.
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.
Fontes oficiais: Documentação oficial de API da OpenAI e OpenAI e benchmarks independentes (2026).