OpenAI o3-mini
Reasoning quality at fast-tier prices — the practical o-series default.
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OpenAI o3-mini Overview at a Glance
OpenAI o3-mini is a large language model from OpenAI, first released on 31 January 2025. It is proprietary (closed-weights) and sits in the reasoning, openai, agents, enterprise, and code categories of our catalog. Reasoning quality at fast-tier prices — the practical o-series default. This page covers OpenAI o3-mini pricing, benchmarks, API limits, speed, modalities, best use cases, and how it compares with similar models — so you can decide whether it belongs in your stack in 2026.
As a language model, OpenAI o3-mini is evaluated on reasoning quality, coding ability, latency, context window size, and dollars-per-million-tokens. The context window is 200k tokens (about 150k words), which determines how much prompt, document, and conversation history you can send in one request. At a typical 3:1 input-to-output mix, the blended API price is about $1.93 per 1M tokens. On our intelligence index it ranks #10 of 22 language models we track with a score of 70/100 (strong production-tier).
Teams usually shortlist OpenAI o3-mini when they need a dependable OpenAI option for production chat, agents, retrieval-augmented generation, or coding copilots. Common fits include coding agents, stem tutoring, and step-by-step problem solving. Reviewers consistently call out affordable reasoning, tool use, and reliable on math/code as standout strengths. Trade-offs to weigh include no vision and slower than non-reasoning models. The sections below break down pricing tables, benchmark charts, token limits, input/output modalities, and head-to-head comparisons so long-tail queries — from “OpenAI o3-mini API pricing” to “OpenAI o3-mini vs GPT-5.5” — are answered on this page.
If you are migrating from an older OpenAI model or switching labs entirely, treat this page as a decision brief: skim the overview stats, confirm API pricing fits your volume, check whether the context window covers your longest documents, then validate quality on a golden set of prompts. Benchmarks and charts help shortlist; your own evals decide. We refresh catalog numbers periodically (last update 2026-06) so figures stay useful through the year.
- Context window
- 200k tokens
- Max output
- 100k tokens
- Input price
- $1.10 / 1M tokens
- Output price
- $4.40 / 1M tokens
- Time to first token
- 6.5s
- Input modalities
- text
- Output modalities
- text
- License
- Proprietary
- Provider
- OpenAI
- Affordable reasoning
- Tool use
- Reliable on math/code
- No vision
- Slower than non-reasoning models
- Coding agents
- STEM tutoring
- Step-by-step problem solving
OpenAI o3-mini Pricing
OpenAI o3-mini uses token-based API pricing from OpenAI. You pay $1.10 per million input tokens and $4.40 per million output tokens. For planning budgets we quote a blended rate of $1.93 per 1M tokens at a 3:1 input-to-output ratio — the same convention used across our catalog so models are comparable. Output tokens usually dominate cost for chatty or agentic workloads, so watch generation length and system-prompt size.
When estimating production spend, multiply expected monthly tokens by the blended rate, then add a buffer for retries, tool-calling loops, and RAG context. OpenAI o3-mini is proprietary, so hosted API pricing (or a cloud marketplace listing) is the primary cost lever; negotiate committed-use discounts once traffic is predictable.
Also compare OpenAI o3-mini against cheaper siblings from OpenAI for router patterns: send easy traffic to a mini/flash tier and reserve OpenAI o3-mini for hard reasoning. That hybrid design often cuts billable tokens 30–70% without users noticing quality drops on simple turns.
- Input price
- $1.10 / 1M tokens
- Output price
- $4.40 / 1M tokens
- Blended (3:1)
- $1.93 / 1M tokens
OpenAI o3-mini Benchmarks
Public benchmark scores help compare OpenAI o3-mini with other LLMs on knowledge, graduate-level science, competition math, and coding. Reported figures in our catalog include MMLU 86, MMLU Pro 74, GPQA 71.5, MATH 87.3, and HumanEval 88. These are not a substitute for evals on your own prompts, but they are useful for shortlisting.
Our intelligence index (70/100) normalizes those benchmarks into a single strong production-tier score so you can scan the leaderboard quickly. The performance chart below shows each benchmark against the current catalog leader.
OpenAI o3-mini API Pricing
API pricing for OpenAI o3-mini is what you pay when calling OpenAI’s developer endpoint (or a marketplace such as Azure, Bedrock, or Vertex when available). Unlike consumer chat apps with flat subscriptions, API bills scale with tokens processed. Cache prompt prefixes where the provider supports it, batch non-interactive jobs, and prefer smaller sibling models for classification or routing when full OpenAI o3-mini quality is unnecessary.
To convert catalog numbers into a monthly forecast: estimate average input tokens per request (system prompt + user message + retrieved context), average output tokens, and request volume. Cost ≈ requests × ((inputTokens/1e6) × inputPrice + (outputTokens/1e6) × outputPrice). Our LLM pricing calculator can stress-test scenarios if you need a second opinion against peers.
Always verify live rates on the official docs — our figures are refreshed periodically (last catalog update: 2026-06) and providers change list prices. Official reference: https://platform.openai.com/docs/models/o3-mini.
OpenAI o3-mini Context Window
OpenAI o3-mini offers a 200k-token context window — roughly about 150k words of English text. Everything in a single API call counts against that budget: system instructions, chat history, retrieved documents, tool schemas, and the model’s reply. Exceeding the window truncates or errors depending on the provider.
Large windows help with long PDFs, multi-file code reviews, and multi-hour agent traces, but bigger contexts also cost more tokens and can add latency. Prefer retrieval that stuffs only relevant chunks, summarize old turns, and reserve headroom for up to 100k output tokens.
OpenAI o3-mini Input / Output Modalities
OpenAI o3-mini accepts text as input and produces text as output. Knowing the modality matrix matters when you design pipelines — for example, vision-capable language models can take screenshots or PDFs as images, while pure text models need an OCR or captioning step first.
If you need bidirectional voice, native video understanding, or tool-use with multimodal arguments, confirm support in OpenAI’s API schema rather than assuming parity with the consumer chat app. Modality support also affects pricing: image or audio inputs may be tokenized differently than plain text.
Document which of OpenAI o3-mini’s listed modalities you will actually send in production. Turning on unused multimodal features can change tokenizers, rate limits, and safety filters unexpectedly.
- Inputs
- text
- Outputs
- text
OpenAI o3-mini Token Limits
Token limits define how much OpenAI o3-mini can read and write per request. Total context is capped at 200k tokens. Maximum completion length is 100k tokens — even if context remains, the model stops generating beyond that ceiling unless you continue in a follow-up call. Providers may also enforce organization-level rate limits (RPM/TPM) separate from these per-request caps.
Practical tip: set max_tokens intentionally. Leaving it unbounded wastes budget on verbose answers; setting it too low truncates JSON or code. For structured outputs, prefer schemas/tool calls and keep completions tight.
- Context window
- 200k tokens
- Max output
- 100k tokens
OpenAI o3-mini Speed
Speed for OpenAI o3-mini is measured two ways: time-to-first-token (how quickly streaming starts) and steady-state tokens per second. Catalog median throughput is about 60 tok/s. Typical TTFT is 6.5 s. Reasoning-heavy modes that think before answering will look slower on tok/s even when quality is higher.
Interactive chat wants low TTFT; batch extraction can tolerate higher latency for cheaper regions or providers. If OpenAI o3-mini is too slow for your UX, evaluate a “mini/flash/haiku” sibling from the same lab before switching ecosystems.
- Throughput
- 60 tokens/sec
- Time to first token
- 6.5 s
- Speed percentile
- Faster than ~18% of tracked LLMs
OpenAI o3-mini Performance Charts
The charts on this page visualize OpenAI o3-mini against catalog peers. Benchmark bars show academic scores versus the current leader; the similar-models comparison table plots intelligence, speed, and blended price so you can see trade-offs at a glance. Use them to answer “is OpenAI o3-mini fast enough?” and “is the quality jump worth the premium?” without opening a spreadsheet.
Benchmark performance vs catalog leaders
Intelligence index vs similar models
Comparison with Similar Models
Choosing an AI model is rarely absolute — it is relative to the next-best option. OpenAI o3-mini is most often weighed against GPT-5.5, OpenAI o1, and Claude 4 Opus. Compare intelligence (or generation quality), latency, price, license, and modality support. A slightly weaker but much cheaper model can win for high-volume workloads; a pricier frontier model wins when a single mistake is expensive.
Use the links and table below for structured OpenAI o3-mini vs alternatives research. We also maintain dedicated head-to-head pages for popular matchups when available. If you are standardizing on OpenAI, check sibling models from the same lab before leaving the ecosystem.
A practical bake-off: pick 20–50 real prompts, score accuracy/style, measure p50/p95 latency, and compute cost at projected volume for OpenAI o3-mini and two peers. Ship the winner behind a feature flag so you can reverse the decision without a rewrite.
| Model | Provider | Intelligence | Speed | Price |
|---|---|---|---|---|
| OpenAI o3-mini | OpenAI | 70 | 60 t/s | $1.93/1M |
| GPT-5.5 | OpenAI | 82 | 95 t/s | $7.50/1M |
| OpenAI o1 | OpenAI | 76 | 32 t/s | $26.25/1M |
| Claude 4 Opus | Anthropic | 81 | 50 t/s | $16.00/1M |
OpenAI o3-mini vs popular alternatives
More from OpenAI
OpenAI o3-mini Best Use Cases
Best use cases for OpenAI o3-mini follow from its strengths, price point, and modality support. Match the model to the job: frontier reasoning for hard planning, fast/cheap tiers for classification, image/video/speech specialists for media pipelines.
Based on catalog notes, OpenAI o3-mini is a particularly strong fit for coding agents, stem tutoring, and step-by-step problem solving. Validate with a short bake-off on your real prompts before a full cutover.
Anti-patterns: do not use a frontier-priced model like a generic classifier if a smaller model scores within a point on your eval; do not stuff entire corpora into context when retrieval would be cheaper; and do not skip structured outputs if you plan to parse OpenAI o3-mini responses in code.
- Coding agents
- STEM tutoring
- Step-by-step problem solving
OpenAI o3-mini Pros & Cons
Every model trades quality, speed, cost, and openness. Here is a concise pros and cons list for OpenAI o3-mini drawn from our catalog strengths and weaknesses — pair it with your own evals before committing.
Read pros as “reasons to shortlist” and cons as “risks to mitigate,” not as deal-breakers in isolation. A listed weakness (for example higher price or smaller context) may be irrelevant if your workload is bursty, short-context, or already standardized on OpenAI.
After scanning this list, jump to the comparison table and FAQ for decision support, then lock a trial window with success metrics before replacing a production model with OpenAI o3-mini.
- Affordable reasoning
- Tool use
- Reliable on math/code
- No vision
- Slower than non-reasoning models
OpenAI o3-mini — frequently asked questions
Need help choosing between models?
Compare every option in one sortable table — intelligence, speed and price on a single page.