- Instruction Following
MiniMax M2-her is a dialogue-first large language model from MiniMax, optimized for immersive roleplay, character-driven chat, and expressive multi-turn conversations.
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o4 Mini Deep Research
- Instruction Following
o4 Mini Deep Research is an OpenAI API model optimized for multi-step, web-grounded research tasks, offering a balance of depth, speed, and cost. It is designed to autonomously plan searches, gather evidence, and synthesize sourced answers within a large context window.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is o4 Mini Deep Research?
o4 Mini Deep Research is an OpenAI large language model variant tailored for deep, tool-using research workflows over the web. It is mainly used for complex multi-step investigations where the model must plan queries, call search tools iteratively, and aggregate information into supported, citation-like summaries. It is also used in applications that need affordable, faster research-grade reasoning compared to heavier deep research models, such as automated literature reviews, competitive analysis, and robust fact-checking. It belongs to OpenAI’s o4/o3 deep-research model family, complementing o3 Deep Research with a smaller, cost-efficient alternative.
Model capabilities
5 Core Capabilities
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Deep Research
Conducts multi-step, in-depth research by browsing the web, synthesizing information, and producing cited, well-structured answers to complex queries.
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Conversational Assistance
Engages in interactive dialogues, clarifies requirements, and iteratively refines answers based on user feedback and follow-up questions.
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Document Analysis
Reads and analyzes long texts or documents, extracting key points, comparing sources, and summarizing information relevant to user goals.
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Web-Aware Reasoning
Combines prior knowledge with live web data to reason about current events, evolving topics, and niche domains more accurately.
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Cross-Language Use
Understands content in multiple languages and can leverage foreign-language sources when researching, while responding to the user in English.
Use cases
6 Most Valuable Use Cases
- Market landscape research
- Scientific literature reviews
- Legal and policy analysis
- Competitive product comparisons
- Technical tool evaluations
- Long-form report drafting
Transparent pricing
Cost Comparison
LLM API offers the lowest prices and best performance for o4 Mini Deep Research–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~80 tps | ~99.99% | ~$0.18 | ~$0.72 | ~256K tokens |
| OpenAI | Global | ~180ms | ~60 tps | ~99.9% | ~$0.25 | ~$1.00 | ~200K tokens |
| Azure OpenAI | US East | ~210ms | ~55 tps | ~99.9% | ~$0.27 | ~$1.05 | ~200K tokens |
| AWS Bedrock (OpenAI-compatible) | US West | ~220ms | ~50 tps | ~99.9% | ~$0.28 | ~$1.10 | ~128K tokens |
| GCP Vertex AI (OpenAI proxy) | EU West | ~230ms | ~45 tps | ~99.9% | ~$0.30 | ~$1.20 | ~128K tokens |
Performance benchmarks
Technical Specifications
| Metric | o4 Mini Deep Research | Claude 3.7 Sonnet | Gemini 2.0 Pro |
|---|---|---|---|
| Avg Latency | ~2200ms | ~2000ms | ~2100ms |
| Context Window | 200K | 200K | 1M |
| Input Price ($/1M) | $2.50 | $3.00 | $1.50 |
| Output Price ($/1M) | $10.00 | $15.00 | $7.50 |
| Max Output Tokens | 8K | 8K | 8K |
| Throughput | 40 tps | 35 tps | 38 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 62B
- Prompt tokens processed (30 days)
- 48B
- Completion tokens generated (30 days)
- 7.5M
- API requests served (30 days)
- 1.1M
- Unique developer & team workspaces (30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Dynamically route each request across providers and models based on latency, cost, or quality policies—without changing your integration or redeploying code.
One endpoint, every model. -
Cost-Aware Orchestration
Enforce per-project and per-team budgets, auto-select cheaper equivalents, and see real-time spend across providers from a single control plane.
Max control, minimal spend. -
Resilient Fallback Flows
Configure automatic retries and provider fallbacks when models fail, time out, or degrade—keeping your AI features reliable in production.
Never fail on one model. -
End-to-End Observability
Inspect every request, token, and latency metric across providers; trace failures, compare models, and debug prompts from a single, queryable timeline.
See every token, everywhere. -
Task-Level Abstractions
Define reusable tasks—chat, extraction, tools, reranking—once and run them on any underlying model, standardizing behavior and simplifying experimentation.
Program tasks, not models. -
High-Throughput Batch API
Submit massive, parallel workloads to any provider with automatic chunking, rate-limit handling, and progress tracking built in for data and evaluation pipelines.
Scale to millions of calls.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need deep, multi-step web research synthesized into a concise, sourced report.
- You need to investigate unfamiliar domains and autonomously gather and compare online evidence.
- You need structured research outputs, like outlines, briefs, or literature reviews from web data.
- Your use case involves answering complex, open-ended questions that require corroborating multiple sources.
- You need the model to proactively browse, fact-check, and resolve contradictions across sources.
- Your use case involves scouting tools, libraries, or vendors and summarizing trade-offs.
- You need ongoing research assistance that periodically re-checks the web for new developments.
Avoid if...
- You need ultra-low-latency responses for chatbots or interactive UI with instant feedback.
- Your workload requires strict cost control and does not benefit from live web research.
- You need fully offline inference on edge devices without any external web access.
- Your workload requires deterministic, reproducible outputs without variability from changing web content.
- You need simple classification or rote Q&A that cheaper non-research models handle well.
- Your workload requires processing highly sensitive data that must never leave a closed environment.
- You need high-throughput batch processing of short prompts where browsing overhead dominates.
FAQ
Frequently Asked Questions
-
What is o4 Mini Deep Research?
o4 Mini Deep Research is an OpenAI model exposed via LLM.API, optimized for low-cost, higher-depth reasoning and research-style responses.
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What is o4 Mini Deep Research best suited for?
It is best for multi-step reasoning, exploratory research assistance, synthesizing information, and generating structured, well-argued answers rather than short chat-style replies.
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How is o4 Mini Deep Research priced on LLM.API?
Pricing is metered per token and may differ from OpenAI’s native rates; check LLM.API’s pricing page for current input and output token costs.
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What context window does o4 Mini Deep Research support?
LLM.API exposes the context window configured for this model by the provider; see the model’s details in LLM.API for the current token limit.
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How fast is o4 Mini Deep Research in terms of latency and throughput?
Latency depends on request size and LLM.API routing, but it generally trades some speed for deeper reasoning compared to smaller chat-optimized models.
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Which input and output modalities does o4 Mini Deep Research support via LLM.API?
Through LLM.API it supports standard text input and text output; check the model card to confirm any additional modality support.
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How do I call o4 Mini Deep Research through LLM.API?
Set the model field to "o4 Mini Deep Research" in your LLM.API request, keep your existing API key, and send standard chat or completion payloads.
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How does o4 Mini Deep Research compare to other OpenAI reasoning or research models?
Compared to larger frontier models it targets lower cost with strong reasoning, but may underperform them on the hardest, open-ended reasoning benchmarks.
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Are there any notable limitations of o4 Mini Deep Research?
It can still hallucinate, may be slower than lightweight chat models, and might miss highly domain-specific details without good prompting and references.
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Can I use tools or retrieval with o4 Mini Deep Research via LLM.API?
Tool and retrieval support depends on LLM.API’s orchestration layer; consult the platform’s docs for whether tool-calling is enabled for this model.
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