How much does O4 Mini cost on Renas AI?

O4 Mini costs 0.06 credits per word generated. With a 10,000-credit Spark plan, that's roughly 167,000 words per month — about 230 medium-length blog posts or 60 long technical reports. Credits are shared across all your Renas tools.

What's the difference between O4 Mini and GPT-5 Mini?

Both are reasoning models, but O4 Mini is the older o-series design (April 2025) while GPT-5 Mini is part of the newer GPT-5 family (August 2025). GPT-5 Mini has a larger context window (400K vs 200K), is 6x cheaper, and scores higher on Artificial Analysis Intelligence Index. O4 Mini may still be preferred when you specifically want the o-series reasoning trace style.

Is O4 Mini still recommended in 2026?

OpenAI now recommends GPT-5 Mini or GPT-5.2 for most use cases. O4 Mini remains useful for workloads that specifically benefit from the o-series reasoning style — heavy math, scientific olympiad-style problems, and code that needs explicit step-by-step traces. For general use, the newer GPT-5 family is the better default.

What is the context window of O4 Mini?

200,000 tokens — about half of the GPT-5 family's 400K but still large enough for full codebases, long research papers, and multi-document synthesis. Max output token count was not publicly disclosed by OpenAI.

Does O4 Mini support vision input?

Yes. O4 Mini accepts text + image input and produces text output. Vision is particularly useful for technical analysis — interpreting charts, diagrams, scientific figures, or UI screenshots alongside reasoning.

When should I pick O4 Mini over GPT-5.2?

Pick GPT-5.2 by default. Reach for O4 Mini only when (a) you specifically want the o-series reasoning trace style, (b) cost matters and the 200K context is enough for your input, or (c) you're benchmarking against existing o-series workflows. For new projects, the GPT-5 family is generally the better starting point.

Can I use O4 Mini for commercial work?

Yes. All output generated through Renas AI is yours to use commercially — for client deliverables, marketing materials, code in production, and published content. The Renas AI Terms of Service grant you full commercial rights to anything you generate within your active subscription.

Reasoning Specialistby OpenAI

O4 Mini

Name: O4 Mini
Author: OpenAI

OpenAI's compact reasoning model with extended thinking, native vision input, and a 200K context window. Built for problems that need step-by-step logic — math, code, and structured analysis.

Try O4 Mini View Pricing

Model Specs

Released: Apr 2025
Context window: 200K tokens
Capabilities: reasoningextended-thinkingmultimodalfunction-calling
Modalities: textvision

About this model

O4 Mini is OpenAI's compact reasoning model, released on April 16, 2025 alongside o3. It's part of OpenAI's o-series — models that use extended thinking (chain-of-thought spent at inference time) to solve hard problems that non-reasoning models often miss. Where GPT-5 Mini optimizes for everyday speed and broad utility, O4 Mini optimizes for harder reasoning at a smaller-than-flagship size.

The model has a 200K-token context window — smaller than the GPT-5 family's 400K but still large enough for full codebases, lengthy documents, and complex multi-document synthesis. It accepts text and image input, produces text output, and ranks well on math benchmarks (AIME), competitive programming (HumanEval-class), and graduate-level science questions (GPQA).

On Renas AI, O4 Mini costs 0.06 credits per word — close to the flagship tier. The pricing reflects the compute cost of extended thinking. Reach for O4 Mini when you have a hard problem and want a structured, traceable reasoning trace; reach for GPT-5 Mini for the same problem at lower cost when you don't need explicit step-by-step output.

Key Strengths

Extended thinking for hard problems

O4 Mini spends extra inference time on chain-of-thought reasoning before producing the final answer. On math olympiad problems, complex code, and multi-step logic, this reliably improves accuracy over non-reasoning models.

200K context with native vision

Large enough for entire books, codebases, or document archives. Vision input is included at no extra credit — useful for analyzing diagrams, charts, or screenshots alongside text.

Strong on math and code

OpenAI positions o-series models as their best for technical reasoning. AIME math competitions, competitive coding, and scientific Q&A are the model's natural sweet spot.

Audit-friendly reasoning trace

When the model thinks step by step, you can read the reasoning and catch errors. This makes O4 Mini useful in domains where you need to verify how the model reached its answer (research, legal analysis, scientific work).

Function calling and structured output

Production-grade JSON mode and tool use, same as the rest of the GPT family. Suitable for agents that need to reason then act.

Available across Renas AI

Use O4 Mini in Chat, the AI Editor, and content generation flows. Same credit balance, no separate setup.

How it compares

O4 Mini sits between the budget GPT-5 Mini and the flagship GPT-5.2. Each makes a different trade-off between cost, reasoning depth, and knowledge recency.

vs. Model	Verdict	Outcome
GPT-5.2	GPT-5.2 is OpenAI's current flagship — newer (Dec 2025 vs April 2025), stronger benchmark scores (GPQA Diamond 93.2, AIME 100), and a 400K context vs O4 Mini's 200K. GPT-5.2 also costs slightly more (0.07 vs 0.06 credits per word) but is the better default for almost any reasoning task today. Pick O4 Mini only if you specifically prefer the o-series reasoning trace style.	Other wins
GPT-5 Mini	GPT-5 Mini is 6x cheaper (0.01 vs 0.06 credits per word) and has a larger 400K context. For everyday tasks the quality gap is small. Use O4 Mini when you specifically need the explicit reasoning trace, or when math/code-heavy problems are the bulk of your workload.	Depends
Claude Sonnet 4.5	Claude Sonnet 4.5 is Renas's chat default and matches O4 Mini on price (0.07 vs 0.06 credits per word). Anthropic models are often preferred for long-form writing voice and software-engineering benchmarks (SWE-bench). O4 Mini is preferred for math olympiad and scientific reasoning. Test both on your specific workload.	Depends

Pros

Extended thinking improves accuracy on hard reasoning problems
Audit-friendly reasoning trace — you can verify how the model arrived at the answer
200K context with native vision input
Strong on math (AIME), code, and graduate-level science (GPQA)
Production-grade function calling and JSON mode

Things to consider

Smaller context window than GPT-5 family (200K vs 400K)
Older release date (April 2025) — newer GPT-5 family generally outperforms it
Knowledge cutoff not publicly disclosed by OpenAI
Extended thinking adds latency — overkill for simple writing tasks
AA Intelligence Index 33 vs 41 (GPT-5 Mini) and 51 (GPT-5.2) — newer OpenAI models score higher

Best use cases

Math and quantitative reasoning

Olympiad problems, statistical analysis, financial modeling, scientific calculations. Extended thinking shines on problems where the answer depends on multiple correct intermediate steps.

Code generation and debugging

Hard bug fixes that require reasoning about state, complex algorithm implementation, and architectural decisions. Pair with the full-codebase paste pattern in the AI Editor.

Scientific and academic research

Reading papers, generating literature summaries, designing experiments, analyzing data sets. The reasoning trace helps you spot misinterpretations before they propagate.

Strategic and analytical decisions

Multi-variable trade-off analyses, scenario planning, decision frameworks. The model's explicit reasoning makes it easier to verify the logic chain.

Multimodal technical analysis

Reviewing UI screenshots for accessibility, interpreting scientific diagrams, extracting structured data from charts. Vision + reasoning together work well on technical content.

Tool-using agents

Agents that need to think before acting — query a database, reason about results, decide on next steps. Function calling stays consistent across long agentic sessions.

How to use it on Renas AI

Step 1

Pick the surface that fits the task

O4 Mini is available in Renas AI Chat for conversational reasoning, the AI Editor for inline document analysis, and the AI Text Generator for one-shot reasoning tasks. Pick the one that matches the workflow.

Step 2

Select O4 Mini from the model picker

Use the model selector in any text tool. Choose O4 Mini specifically when the task is hard reasoning — math, complex code, multi-step analysis. For everyday writing, GPT-5 Mini is cheaper and nearly as capable; for the hardest tasks, GPT-5.2 has better benchmark scores.

Step 3

Provide the full problem and constraints

Reasoning models work best with the entire problem stated upfront — known constraints, success criteria, edge cases. The 200K context handles full codebases, long papers, and complex problem sets in a single message.

Step 4

Read the reasoning trace, then decide

O4 Mini often shows its work. Skim the reasoning before accepting the final answer — it's a fast way to spot wrong assumptions or missed cases. For automated pipelines, you can suppress the trace via system prompt.