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OpenAI o1 was a landmark reasoning-model generation, but it is no longer the default choice for a new OpenAI project. Introduced as a model family trained with reinforcement learning to use additional computation before producing an answer, o1 improved performance on difficult mathematics, coding, science, and multi-step analysis tasks. It also demonstrated that better benchmark reasoning does not automatically solve factuality, planning, verification, latency, or cost.
As of the dossier’s August 16, 2026 snapshot, OpenAI lists o1, o1-mini, o1-preview, and o1-pro as deprecated. Existing applications may still have compatibility reasons to retain a pinned o1 snapshot, but new deployments should normally evaluate currently supported GPT-5-family or other successor models first.
What OpenAI o1 introduced
OpenAI o1 was designed to spend more inference-time computation on difficult problems before returning its final answer. OpenAI described the family as being trained with reinforcement learning to improve reasoning strategies, recognize mistakes, and follow policies more reliably. That description does not mean o1 thinks like a person, exposes a formal proof, or guarantees that every conclusion has been verified.
The practical change was a trade-off: more computation could improve performance on hard, multi-step tasks, but it generally brought higher latency and cost. For a simple rewrite, classification, or short extraction, extended reasoning could be unnecessary overhead.
#1 Best Overall
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The o1 family was not one static model
- o1-preview: The early public preview of OpenAI’s reasoning approach.
- o1: The production successor, including the API snapshot
o1-2024-12-17. - o1-mini: A faster, cheaper model aimed particularly at coding and technical reasoning.
- o1-pro: A higher-compute version intended to produce more consistently strong answers on harder problems.
The production o1 release added function calling, Structured Outputs, developer messages, vision input, and a reasoning_effort parameter. OpenAI said it used approximately 60% fewer reasoning tokens than o1-preview for a given request. These capabilities should not be assumed to apply to every family member: the documented o1-mini model, for example, lists no function calling or Structured Outputs and does not support image input.
OpenAI’s system card explains the family’s reasoning and safety design, while the production o1 announcement documents the API features and launch results.
Where o1 was genuinely strong
o1 was most useful when a task had interacting constraints, required several dependent steps, or benefited from checking intermediate work. Typical examples included mathematical derivations, complex code review, scientific analysis, difficult debugging, and structured decision support.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsOpenAI’s published results for the production snapshot o1-2024-12-17 were:
| Evaluation | o1-2024-12-17 |
|---|---|
| GPQA Diamond | 75.7 |
| MMLU, pass@1 | 91.8 |
| SWE-bench Verified | 48.9 |
| LiveBench Coding | 76.6 |
| MATH, pass@1 | 96.4 |
| AIME 2024, pass@1 | 79.2 |
| MGSM, pass@1 | 89.3 |
| MMMU | 77.3 |
| MathVista | 71.0 |
| SimpleQA | 42.6 |
| TAU-bench retail | 73.5 |
These are vendor-reported benchmark results, not a universal measurement of intelligence or production reliability. Strong mathematics scores show that o1 handled selected mathematical evaluations well. SWE-bench is relevant to software-engineering tasks, but a benchmark score does not mean the model can autonomously ship safe, production-quality code.
The relatively low SimpleQA result is particularly important. Better reasoning did not eliminate factual errors. A model can reason coherently from a false premise, invent a supporting detail, or provide a confident answer despite lacking the required information.
Benchmark results also depend on prompting, tools, scaffolding, number of attempts, dataset construction, and possible contamination. Scores from different evaluation setups should not be treated as directly comparable without checking those conditions.
Rank #2
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What expert and system-card evaluations showed
OpenAI reported one biology-expert comparison in which a pre-mitigation version of o1 beat the selected individual-expert baseline on accuracy, understanding, and ease of execution. But the same system card reported that all evaluated models underperformed the consensus and median expert baselines on ProtocolQA Open-Ended.
That is not a contradiction. A model may beat one answer or a selected baseline while still failing to match the best consensus of multiple experts. “Comparable to experts” must always be tied to the task design, baseline, scoring method, and version tested; it is not evidence that the model reliably replaces specialists.
The system card also identified a problem with automated agent evaluations. Some models passed an autograder even though manual inspection found that major task requirements were incomplete—for example, the model used an easier model than the task requested. OpenAI did not count those cases as genuine passes. This illustrates why real deployments need human inspection, task-specific tests, and checks against the actual requested outcome.
Where o1 failed or required safeguards
Reasoning did not solve hallucinations
o1 could produce a polished chain of explanations while still being factually wrong. Its documented knowledge cutoff was October 1, 2023, so later facts require browsing, retrieval, or user-supplied documents. For consequential work:
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- Require citations where appropriate and verify them.
- Recalculate numerical results independently.
- Use tests for code and a human reviewer for high-impact decisions.
Medical, legal, financial, security, and safety-related answers should never be accepted merely because the response sounds methodical.
Planning and agentic completion were imperfect
o1’s ability to reason through a task did not guarantee that it completed every external action correctly. Automated success signals can miss skipped requirements, incorrect tool choices, or partial completion. Agents need explicit checklists, tool-result validation, retries, audit logs, and a final state check.
Extra thinking could be wasteful
o1 was a poor economic or latency choice for routine summarization, simple transformations, high-volume support, and basic routing. A faster model may deliver sufficient accuracy at much lower cost. The right question is not “Is o1 more capable?” but “Does its additional capability reduce errors enough to justify its added cost and delay?”
Rank #3
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
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Safety results were mixed by test
OpenAI’s system card reported approximate jailbreak success rates of 6% for harmful text, 5% for harmful image-text input, and 5% for malicious-code-generation submissions in the evaluated setup. The comparison GPT-4o rates were approximately 3.5%, 4%, and 6%. These figures do not support a simple claim that o1 was either safer or less safe overall: results varied by modality, attack method, mitigation stage, and test design.
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o1, o1-mini, and o1-pro compared
| Model | Best understood as | Documented details | Practical fit |
|---|---|---|---|
| o1 | Production reasoning model | o1-2024-12-17; vision, function calling, Structured Outputs, developer messages, and reasoning effort were added to production o1 |
Existing integrations and difficult, testable workloads |
| o1-mini | Lower-cost technical reasoning model | 128,000-token context; 65,536-token maximum output; no image input, function calling, or Structured Outputs in the cited documentation | Historical coding and technical workloads where its limitations are acceptable |
| o1-pro | Higher-compute o1 variant | 200,000-token context; 100,000-token maximum output; Responses API only in the cited documentation | Very difficult work where consistency justifies substantial cost |
The retrieved API documentation listed prices of $15 per million input tokens and $60 per million output tokens for o1; $1.10 input and $4.40 output for o1-mini; and $150 input and $600 output for o1-pro. These are API token prices, not ChatGPT subscription prices, and they are volatile. Verify current pricing before making a purchase or migration decision.
Is OpenAI o1 still available?
The precise answer depends on the product surface. As of the dossier’s August 16, 2026 snapshot, OpenAI’s model directory marked o1, o1-mini, o1-preview, and o1-pro as deprecated. OpenAI still retained reference pages for o1 and its variants, so “completely deleted” would be too broad.
For the API, check the current model directory, account access, endpoint support, and active deprecation notices. A pinned snapshot may remain useful for reproducibility or regression avoidance, but deprecation means continued service should not be assumed.
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ChatGPT access and API access are separate questions. A model’s retirement in ChatGPT does not automatically establish that the corresponding API model has changed, and an API reference page does not guarantee that every ChatGPT plan or region exposes the model. OpenAI’s release notes distinguish these surfaces.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How o1 compares with newer OpenAI reasoning models
OpenAI positioned o3 as a more powerful reasoning model across coding, mathematics, science, visual perception, and other complex tasks, and described o4-mini as a faster, cost-efficient reasoning model with strong throughput and tool-use performance. The current model directory later described o3 as succeeded by GPT-5 and o4-mini as succeeded by GPT-5 mini.
There is no responsible single table ranking o1 against current GPT-5-family models unless the tests are recent and apples-to-apples. Model versions, tools, prompts, context, evaluation sets, and pricing change. The safe practical conclusion is that current OpenAI documentation presents newer GPT-5-family models as the active generation. New projects should benchmark the currently supported candidates for their own workload rather than assume that o1’s launch-era scores still make it optimal.
For alternatives outside OpenAI, Anthropic Claude and Google Gemini are credible competing families, but neither is a drop-in replacement. API syntax, pricing, context limits, tool integrations, safety behavior, and output quality differ. Self-hosted reasoning models offer more deployment control but transfer hardware, serving, security, monitoring, and maintenance responsibilities to the operator.
Developer guidance: when to use reasoning at all
Choose a reasoning model when:
- The task has multiple interacting constraints.
- A wrong answer is expensive enough to justify extra latency and cost.
- The model must inspect nontrivial code or perform multi-step analysis.
- The result can be independently tested, retrieved, or reviewed.
Prefer a faster model when:
- The task is routine extraction, summarization, routing, or transformation.
- Request volume and response time dominate quality.
- A simpler model already meets the measured accuracy target.
- The model lacks the current information needed to answer reliably.
A practical routing architecture
- Send routine requests to a fast, lower-cost supported model.
- Route difficult or high-value cases to a current reasoning model.
- Give the model precise constraints and the source material it needs.
- Use schemas or Structured Outputs where the selected model supports them.
- Validate with retrieval, unit tests, deterministic checks, a second model, or human review.
- Log model version, prompt version, latency, token use, refusals, tool calls, and failure type.
- Re-run a regression set before migrating away from a pinned o1 snapshot.
Do not treat hidden reasoning as an audit trail. The final answer can be clear while the underlying process contains silent omissions. For production systems, observable inputs, tool results, tests, and decision logs are more useful than assuming that a longer internal deliberation guarantees correctness.
When preserving o1 still makes sense
Keeping a pinned o1 snapshot can be reasonable when an existing application depends on its exact behavior, a reproducible research result must be maintained, or evaluation shows a workload-specific advantage. Even then, isolate the dependency, record the model identifier, maintain a tested fallback, and plan a migration test. A deprecated model can become unavailable or change operational terms even if documentation remains online.
Final assessment
o1 was important because it made extended test-time reasoning commercially visible and showed that spending more computation on hard problems could materially improve selected mathematics, coding, science, and analysis evaluations. Its limitations are equally important: factuality remained imperfect, agentic benchmarks could overstate task completion, safety outcomes varied by test, and added reasoning increased latency and cost.
In 2026, o1 is best understood as a historically significant transition point rather than an automatic recommendation. Use it mainly for controlled legacy compatibility, reproducibility, or a demonstrated workload-specific advantage. For a new application, evaluate current supported reasoning models first and choose based on measured accuracy, verification requirements, latency, throughput, and total cost.
Quick Recap
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