OpenAI released o3 and o4-mini together on April 16, 2026 — and the surprising result is that the cheaper model wins on most benchmarks. Here's the full breakdown of what each model does, where they differ, and which one you should actually use.

The Short Answer

o4-mini beats o3 on math and coding benchmarks, costs roughly 10x less, and runs faster. For the vast majority of users and developers, o4-mini is the right choice. o3 still has a narrow advantage on extended, multi-step reasoning tasks — but only in scenarios where raw benchmark ceiling actually matters.

Benchmark Comparison

o4-mini AIME
92.7% accuracy (vs o3's 87.3%)
o4-mini SWE-bench
68.1% (vs o3's 61.0%)
o3 GPQA
83.3% (vs o4-mini's 81.4% — o3's only win)
o4-mini price
$1.10 / 1M input tokens, $4.40 / 1M output
o3 price
$10.00 / 1M input tokens, $40.00 / 1M output

Those numbers are not a typo. o4-mini scores 5.4 points higher on AIME (advanced math competition problems) and 7 points higher on SWE-bench (real-world software engineering tasks) than o3 — at roughly one-tenth the API cost.

The only benchmark where o3 edges ahead is GPQA (PhD-level science questions), where o3 scores 83.3% versus o4-mini's 81.4% — a 2-point margin that matters only in highly specialized scientific research contexts.

What Changed Between o3 and o4-mini?

OpenAI made three significant architectural improvements in o4-mini that explain the benchmark jump:

1. Tool use during reasoning Unlike o3, which reasons first and then calls tools, o4-mini can use Python, search, and vision while it's thinking. It can write a snippet of code, run it, see the result, and incorporate that into its reasoning chain — all before delivering an answer. This is why SWE-bench scores jumped so dramatically.

2. Improved chain-of-thought efficiency OpenAI says o4-mini generates more compact reasoning chains — fewer tokens of thinking per correct answer. That's partly why it's cheaper: it reaches the right answer with less internal deliberation.

3. Vision integrated into reasoning Both o3 and o4-mini are multimodal, but o4-mini can "look" at images mid-thought rather than as a separate preprocessing step. This makes it significantly better at diagram interpretation, visual math problems, and code screenshot analysis.

o4-mini is the first OpenAI reasoning model where the smaller, cheaper version genuinely outperforms its larger sibling on most benchmarks. OpenAI has confirmed this is intentional — not a fluke.

When to Use o4-mini

o4-mini is the right default for virtually every use case:

  • Coding and debugging — Higher SWE-bench score means better real-world coding tasks. Use it in Cursor, Windsurf, or via API for agentic coding workflows
  • Math and STEM — 92.7% AIME accuracy is elite-level. o4-mini is exceptional for quantitative problems, proofs, and data analysis
  • Production applications — 10x cost reduction makes it viable for high-volume API usage where o3 would be cost-prohibitive
  • Speed-sensitive tasks — o4-mini returns answers significantly faster than o3 under equivalent compute budgets
  • Research and summarization — Strong enough for academic research synthesis without paying o3 rates

When o3 Is Worth the Premium

The case for o3 is narrow but real:

  • Extended multi-step scientific reasoning — On GPQA, o3's 2-point edge compounds when problems require sustained, highly precise reasoning chains over many steps
  • Mission-critical analysis — If you need every possible decimal point of accuracy and cost isn't a constraint (e.g., drug discovery, financial modeling), o3's slight GPQA advantage could matter
  • Comparing against a known baseline — If your existing workflow was tuned around o3's specific output style and reasoning patterns, switching requires re-evaluation even if o4-mini is technically stronger
Pros
  • ✓Outperforms o3 on math and coding benchmarks
  • ✓10x cheaper API pricing
  • ✓Faster response times
  • ✓Tool use integrated into reasoning chain
  • ✓Better for production-scale deployments
Cons
  • ✗Slightly below o3 on GPQA (PhD science)
  • ✗"Mini" name causes underestimation — teams may default to o3 assuming it's better
  • ✗Extended reasoning on the most complex multi-step tasks still slightly favors o3

ChatGPT Access: Which Model Do You Get?

If you're a ChatGPT user rather than a developer, here's what this means for you:

  • Free tier — Access to GPT-4o. No o3 or o4-mini in the free tier (yet)
  • ChatGPT Plus ($20/month) — Access to o4-mini for reasoning tasks. o3 available with usage limits
  • ChatGPT Pro ($200/month) — Unlimited o3 access, plus o3-pro for the most demanding tasks
  • API — Direct access to both models; o4-mini is $1.10/$4.40 per million tokens, o3 is $10/$40

For most Plus subscribers, o4-mini should be your default reasoning model. You get better results on typical coding and analytical tasks, and it burns through your monthly compute allocation more slowly.

How to Choose in Practice

Key Facts
  • Default to o4-mini for everything — math, coding, research, writing
  • Use o3 only if you need maximum PhD-level scientific reasoning accuracy
  • At 10x the cost difference, o3 must be meaningfully better for your specific task to justify it
  • Both models support Python, browsing, and vision — there's no capability gap, only performance nuance
  • o4-mini's tool-use-during-reasoning makes it measurably better at multi-step coding tasks

The Bigger Picture

The o4-mini vs o3 comparison is a sign of how fast reasoning model efficiency is improving. Six months ago, "o3" was synonymous with the best reasoning available. Today, a model called "mini" surpasses it on the benchmarks that matter most to developers.

OpenAI's bet is clearly on making powerful reasoning cheap enough that developers build with it at scale, rather than rationing expensive o3 calls. o4-mini is the clearest embodiment of that strategy yet.

If you're deciding between the two: start with o4-mini. Switch to o3 only when you have a concrete reason tied to specific performance gaps — not because of the name.