OpenAI o1 vs o3: Benchmarks, Pricing, and 2026 Status
OpenAI o3 replaced OpenAI o1 as the company’s flagship reasoning model, scoring higher on every public benchmark while costing about 87% less per output token after a June 2025 price cut. OpenAI o1 launched in December 2024 as the first model in the reasoning series. OpenAI o3 followed in April 2025 with tool use inside the reasoning process, native image analysis, and stronger math and coding scores.
The practical picture in 2026 changed again. OpenAI retired o1 from ChatGPT in February 2026 and scheduled o3 to leave ChatGPT on August 26, 2026, while keeping o3 available through the API. Both models now sit behind the GPT-5 series in OpenAI’s public lineup.
This comparison covers the benchmark gap, current API pricing, the reasoning-quality trade-offs users report, and which model still fits new projects. It uses published OpenAI data, third-party benchmark aggregators, and developer feedback from Reddit, LinkedIn, and the OpenAI forum. Full methodology notes live on the AI Comparison homepage.
OpenAI o1 vs o3 at a Glance
OpenAI o3 is the newer, cheaper, and more capable model for math, coding, and multi-step reasoning, while o1 now matters mainly for teams with locked-in deployments that reference it by name. The table below summarizes the core differences before the detailed sections.
| Attribute | OpenAI o1 | OpenAI o3 |
|---|---|---|
| Release date | December 2024 | April 2025 |
| Reasoning method | Private chain-of-thought | Chain-of-thought plus tool use in the loop |
| Image reasoning | Basic image input | Native visual reasoning (crop, zoom, analyze) |
| Tools during reasoning | None | Web search, Python, image generation |
| API price (input / output per 1M tokens) | $15.00 / $60.00 | $2.00 / $8.00 |
| AIME 2024 math score | 83.3% | 96.7% |
| SWE-bench Verified (coding) | 48.9% | 71.7% |
| ChatGPT status in 2026 | Retired February 2026 | Left ChatGPT August 2026; API access continues |
What Is OpenAI o1?
OpenAI o1 is the first model in OpenAI’s reasoning series, released in December 2024, that spends extra compute on a hidden chain-of-thought before answering. It was built on the GPT-4 architecture with reinforcement learning that rewards step-by-step problem solving. o1 targets tasks where a single-pass model like GPT-4o tends to slip, such as competition math, formal logic, and multi-constraint coding. The same tiered pattern shows up in the GPT-4o vs GPT-4o Mini split.
o1 shipped in three tiers. The full o1 model handled the hardest prompts, o1-mini offered a cheaper option for math and code, and o1-pro used additional compute for the most demanding queries on the ChatGPT Pro plan.
o1 does not browse the web, run code, or view images as part of its reasoning. It produces a private reasoning trace, then a final answer, which keeps it predictable but limits it on tasks that need live data or file inspection. Many users still describe o1’s writing as more direct and less padded than later models.
What Is OpenAI o3?
OpenAI o3 is the second-generation reasoning model, released in April 2025, that adds autonomous tool use and image reasoning to the o1 approach and uses more reinforcement-learning compute. o3 can decide, on its own, to run a web search, execute Python, or crop and inspect an image while it works through a problem. OpenAI describes this as the first reasoning model that acts on tools inside the chain-of-thought rather than after it.
o3 also improved cost efficiency. It launched at $10 input and $40 output per million tokens, then OpenAI cut that by 80% in June 2025 to $2 and $8, with a $0.50 cached-input rate. A higher-effort variant, o3-pro, arrived the same month at $20 input and $80 output per million tokens.
o3 makes roughly 20% fewer major errors than o1 on hard real-world tasks in OpenAI’s testing, with the largest gains in programming, data analysis, and research. It reasons with images directly, scoring 82.9% on the MMMU visual benchmark against o1’s 77.6%.
Feature Comparison: o1 vs o3
o3 leads o1 on tool use, image reasoning, cost, and adjustable reasoning effort, while o1 offers a simpler, more repeatable response pattern. The 5 feature areas below separate the two models in daily use.
Tool use during reasoning
o3 runs web search, Python, and image tools while it reasons, so it can pull a current figure or test a code snippet before answering. o1 cannot, which means o1 answers only from its training data and the prompt.
Image and visual reasoning
o3 treats images as reasoning objects. It can zoom into a chart, read a diagram, or rotate a floor plan mid-solution, and it beats o1 on MathVista (86.8% versus 71.8%) and CharXiv (78.6% versus 55.1%). o1 accepts images but does not manipulate them during reasoning.
Reasoning effort control
o3 exposes low, medium, and high reasoning-effort settings in the API, letting developers trade latency for depth. o1 has fixed reasoning behavior, so cost and speed are less controllable.
Context and output
Both models handle 128,000 tokens of context in the API and up to about 100,000 tokens of output. In practice, several users report o3 truncating long code responses more often than o1, which matters for large refactors.
Speed
Third-party aggregators measured o3 at roughly 150 output tokens per second against about 100 for o1. o3 sometimes shows longer time-to-first-token because it burns more thinking tokens by default at higher effort.
Performance Comparison: Benchmark Results
o3 outperforms o1 on every public benchmark OpenAI and third parties have published, often by double-digit margins. The table below shows the headline scores.
| Benchmark | What it measures | OpenAI o1 | OpenAI o3 |
|---|---|---|---|
| AIME 2024 | Competition math | 83.3% | 96.7% |
| GPQA Diamond | PhD-level science | 78.0% | 87.7% |
| SWE-bench Verified | Real GitHub bug fixes | 48.9% | 71.7% |
| Codeforces | Competitive programming Elo | 1891 | 2727 |
| MMMU | College-level visual reasoning | 77.6% | 82.9% |
| ARC-AGI (high compute) | Abstract pattern reasoning | ~32% | 87.5% |
The SWE-bench gap is the most practical signal. o3 solves about 71.7% of verified real-world repository issues against 48.9% for o1, which is the closest benchmark to fixing an actual bug in an actual codebase. The ARC-AGI result drew the most attention, because o3 was the first model to pass the 85% human-baseline threshold on that test, though at high compute cost.
Benchmark scores do not capture reliability. OpenAI’s own system card notes that o3 hallucinates on some factual benchmarks at a higher rate than o1, which matches user reports discussed below.
Pricing: o1 vs o3 API and ChatGPT Access
o3 is far cheaper than o1 in the API, at $2 input and $8 output per million tokens versus $15 and $60 for o1. After the June 2025 cut, o3 costs about 13% of o1’s output price while scoring higher everywhere.
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Cached input |
|---|---|---|---|
| OpenAI o1 | $15.00 | $60.00 | $7.50 |
| OpenAI o3 | $2.00 | $8.00 | $0.50 |
| OpenAI o3-pro | $20.00 | $80.00 | Not offered |
| OpenAI o3-mini | $1.10 | $4.40 | $0.55 |
For a medium-complexity request, published estimates put o3 near $0.36 per call at medium reasoning effort against about $0.45 for o1. o3-mini drops that to a few cents for math and code that do not need the full model.
ChatGPT access is a separate question. OpenAI removed o1 from ChatGPT in February 2026 and set o3 to leave ChatGPT on August 26, 2026, folding reasoning into a single GPT-5 series model with a reasoning-effort slider. Developers who still need o3 by name use the API.
Pros and Cons
o3 wins on capability and price, but o1 still has a narrow edge in output consistency and, for some users, writing tone. The lists below weigh both models across 4 practical dimensions each. A wider field sits in the Best AI Models roundup.
OpenAI o3 advantages:
- Higher scores on math, science, coding, and visual reasoning benchmarks
- Autonomous web search, Python, and image tools inside the reasoning loop
- About 87% lower output-token cost than o1 after the June 2025 price cut
- Adjustable reasoning effort for latency and budget control
OpenAI o3 drawbacks:
- Higher measured hallucination rate than o1 on some factual tests
- Reports of truncated long code output and skipped instructions
- Scheduled removal from ChatGPT in August 2026
- Higher default latency at high reasoning effort
OpenAI o1 advantages:
- More predictable, repeatable response structure
- Writing that several users describe as more direct and less padded
- Still referenced in some enterprise contracts and evaluation suites
- Lower hallucination rate than o3 on select factual benchmarks
OpenAI o1 drawbacks:
- No tool use, browsing, or code execution during reasoning
- Roughly 4 to 7 times more expensive per token than o3
- Lower scores across every published benchmark
- Retired from ChatGPT in February 2026
User Reviews and Community Feedback
Developer feedback splits along a clear line: o3 is the better problem solver, but a vocal group finds o1 more reliable and pleasant for writing and brainstorming. These themes come from Reddit, the OpenAI developer forum, and LinkedIn testing threads.
On r/ChatGPTPro, a widely discussed thread asked whether o1 was still better than o3-mini-high for brainstorming and report writing on the $20 plan, and answers were divided. On the OpenAI forum, one popular post argued that “o3 feels like a massive downgrade” and lacks “that rawness of o1” for long-form drafting. The current-generation equivalent of that brainstorming question is covered in GPT-5 vs GPT-5 Mini.
Coding feedback favors o3. A CodeAnt review of 100 live pull requests concluded that “o1 might catch a misspelled variable name, but o3-mini catches the deeper issues.” LinkedIn testers summarized the lineup as “o1 – big brain energy, o3-mini – small but mighty,” noting o3-mini beat o1 on several coding tasks while running faster.
The most common o3 complaint is reliability. An r/OpenAI thread titled “What the hell is wrong with O3” collected 160-plus comments about hallucination, forgotten context, and ignored instructions, with several users saying they route factual work back to o1 or a Gemini model.
Use Cases: When to Choose o1 or o3
Pick o3 for any new build that needs reasoning, and keep o1 only where a contract, evaluation harness, or integration names it directly. The 6 scenarios below map each model to a job.
Choose OpenAI o3 for:
- Coding agents that fix real repository issues and run tests
- Quantitative analysis, data cleaning, and research synthesis
- Multi-step planning that includes charts, diagrams, or screenshots
- Cost-sensitive reasoning workloads that ran on o1 before
Choose OpenAI o1 for:
- Production systems whose evaluation suites are pinned to o1 output
- Procurement or compliance settings that specify o1 by name
For general chat, summarization, and drafting, a GPT-5 series model now covers what most users previously asked o1 or o3 to do inside ChatGPT.
Final Recommendation
Choose OpenAI o3 for almost every reasoning task, and treat OpenAI o1 as a legacy option you migrate away from. o3 is cheaper, scores higher, and adds tools and image reasoning that o1 never had. These 3 conditions each point to o3.
Choose OpenAI o3 if:
- You are starting a new project in 2026 and need strong math or coding reasoning
- Token cost matters and you want reasoning-effort control
- Your workflow benefits from live search, Python, or image analysis
Choose OpenAI o1 if:
- An existing contract, benchmark, or integration requires o1 specifically
- You have measured o1 as more reliable for your exact factual workload
- Migration cost outweighs the token savings from switching
Alternatives to o1 and o3
If neither model fits, the strongest reasoning alternatives in 2026 are the GPT-5 series, Claude, Gemini, and DeepSeek R1. Each targets a different priority across cost, transparency, and ecosystem. The open-weight side of that choice is detailed in Grok vs DeepSeek.
The GPT-5 series is OpenAI’s current default and replaces the standalone reasoning picker with a single model and a reasoning-effort control. Claude from Anthropic is the common choice for long-context analysis and agentic coding.
Gemini from Google is competitive on math and pairs with Google Search and Workspace. DeepSeek R1 is the open-weight option, priced far below o1 for teams that want to self-host or audit the model.
For a broader view of OpenAI’s model range and how the reasoning series fits GPT-4o and GPT-5, the OpenAI Models Guide walks through each family.
Frequently Asked Questions
What is the difference between OpenAI o1 and o3?
o3 is the newer reasoning model and adds autonomous tool use, native image reasoning, and adjustable reasoning effort, while scoring higher than o1 on every published benchmark. o1 answers only from its training data and the prompt, with no browsing or code execution.
Is OpenAI o3 better than o1 for coding?
Yes, o3 is better for coding. It solves about 71.7% of SWE-bench Verified issues against 48.9% for o1 and reaches a 2727 Codeforces Elo against o1’s 1891.
How much cheaper is o3 than o1?
o3 costs $2 input and $8 output per million tokens after the June 2025 price cut, against $15 and $60 for o1. That makes o3 roughly 87% cheaper on output tokens.
Is OpenAI retiring o3?
OpenAI scheduled o3 to leave ChatGPT on August 26, 2026, and folded reasoning into the GPT-5 series with a reasoning-effort slider. API access to o3 continues after that date.
Does o1 still exist in 2026?
o1 was retired from ChatGPT in February 2026. It may remain reachable through the API for existing integrations, but OpenAI positions the GPT-5 series as the replacement.
Which model hallucinates less, o1 or o3?
OpenAI’s system card and user reports indicate o3 hallucinates more than o1 on some factual benchmarks, despite o3’s higher reasoning scores. Teams with strict factual requirements should test both on their own data.
Final Verdict
OpenAI o3 is the clear choice over o1: it beats o1 on every benchmark, costs a fraction as much per token, and adds tools and image reasoning that o1 lacks. The only reason to stay on o1 is a contract, evaluation suite, or integration that names it directly, and even then the migration path points to o3 or the GPT-5 series.
For new work in 2026, use o3 through the API for dedicated reasoning, and use a GPT-5 series model in ChatGPT for everything else. o1’s role is now historical, as the model that proved reasoning-first training worked before o3 made it cheaper and stronger.
