OpenAI’s o1 Preview and o1 Mini are two advanced language models sharing key similarities yet differing in cost, output capacity, and certain performance metrics, making each suited for distinct applications.
Both models support a large input context window of 128,000 tokens and have the same knowledge cut-off date of October 2023. OpenAI released these models simultaneously on September 12, 2024. Neither is open source, and both are accessible through OpenAI’s APIs and Azure OpenAI Service. The o1 Preview model is available exclusively from OpenAI. Notably, o1 Mini is not older than o1 Preview.
The primary difference lies in their maximum output tokens per request. The o1 Preview generates up to 32,800 tokens, while the o1 Mini supports much longer outputs of up to 65,500 tokens per request, potentially benefiting applications that require extended content generation or detailed responses.
Pricing reveals a significant gap between the two models, heavily favoring o1 Mini for cost efficiency. According to summary pricing, o1 Preview costs $15 per million input tokens and $60 per million output tokens. In contrast, o1 Mini’s input cost is reported at $3 or possibly as low as $1.10 per million tokens, and output ranging from $12 down to $4.40 per million tokens depending on reports. Despite the variations, o1 Mini is approximately 13.6 times cheaper than o1 Preview, making it appealing for large-scale or budget-conscious deployments.
| Price Type | o1 Preview | o1 Mini |
|---|---|---|
| Input Token Cost | $15.00 / million tokens | $1.10 – $3.00 / million tokens |
| Output Token Cost | $60.00 / million tokens | $4.40 – $12.00 / million tokens |
Performance benchmarks spotlight distinct strengths. On the MMLU (Massive Multitask Language Understanding) benchmark, which measures broad knowledge and reasoning, o1 Preview achieves a superior 90.8% pass@1, compared to 85.2% zero-shot chain-of-thought performance by o1 Mini. Conversely, o1 Mini attains a higher score on the MATH benchmark, achieving 90% zero-shot chain-of-thought accuracy, better than o1 Preview’s 85.5% pass@1.
Both models share identical HumanEval results, scoring 92.4%, indicating equal capability in code generation and problem-solving tasks. The o1 Preview also supports the MMMU (Massive Multitask Multimodal Understanding) benchmark with a 78.2% pass@1 score, a metric not available for o1 Mini, suggesting more robust multimodal or multitask abilities for o1 Preview. Neither model currently has available data on the GPQA (Graduate-level Physics Questions Assessment) benchmark.
- Key Performance Scores:
- MMLU: o1 Preview 90.8%, o1 Mini 85.2%
- MATH: o1 Preview 85.5%, o1 Mini 90%
- HumanEval: Both 92.4%
- MMMU: o1 Preview 78.2%, o1 Mini not available
Both models provide a stiff competition, with o1 Preview excelling in multitasking and multimodal understanding, making it suitable for applications demanding broad, diverse knowledge and reasoning. The o1 Mini, with its enhanced output capacity and lower operational cost, better fits extended text generation tasks, especially those emphasizing mathematical reasoning.
When choosing between o1 Preview and o1 Mini, consider the following:
- Cost Efficiency: o1 Mini is more cost-effective, suitable for projects with budget constraints or high token consumption.
- Output Length: o1 Mini supports nearly double the maximum output tokens, ideal for lengthy generations.
- Task Type: Use o1 Preview for broad multitask challenges and multimodal understanding; select o1 Mini for advanced math problem-solving and affordable code generation.
- API Access: Both are available via OpenAI and Azure OpenAI Service, but o1 Preview is exclusively provided by OpenAI’s API.
- Open Source Status: Neither model is open source, limiting customization from source code.
In summary, o1 Preview and o1 Mini share foundational features but diverge in output length, pricing, and benchmark performance, positioning them for different use cases.
- Both released on September 12, 2024, supporting 128K token context windows.
- o1 Mini generates up to 65.5K tokens, nearly doubling o1 Preview’s 32.8K.
- o1 Mini costs approximately 13.6 times less per token than o1 Preview.
- o1 Preview excels in multitask language and multimodal benchmarks; o1 Mini leads in mathematical reasoning.
- Both perform equally well at coding tasks.
o1-preview vs o1-mini
When comparing o1 Preview and o1 Mini, the right pick hinges on balancing token limits, budget constraints, and performance benchmarks to match your AI workload demands.
The o1 Preview model offers an input context window of 128K tokens and can generate up to 32.8K tokens in a single request. It was released on September 12, 2024, and its knowledge base is current through October 2023. OpenAI and Azure OpenAI Service serve as API providers, and the model is proprietary.
By contrast, the o1 Mini also supports a 128K-token context window but extends its maximum output to 65.5K tokens per call. It shares the same release date of September 12, 2024, and knowledge cutoff of October 2023. Like its sibling, it’s closed source and available via OpenAI and Azure.
Input vs Output Capacity
- Context window: 128K tokens (both models)
- Max output: 32.8K tokens (Preview) vs 65.5K tokens (Mini)
- Use case impact: Mini handles longer narratives without mid-stream truncation
Open Source & API
- Both models are proprietary (not open source)
- Both draw knowledge up to October 2023
- Shared API providers: OpenAI and Azure OpenAI Service
- Age difference: zero months (released same day)
Pricing Breakdown
Token costs shape your operating expenses. The o1 Preview lists at $15 per million input tokens and $60 per million output tokens in summary. The o1 Mini, by contrast, charges $3 per million input and $12 per million output in summary. Detailed rate tables even show the Mini as low as $1.10 per million input tokens and $4.40 per million output tokens, underscoring a roughly 13.6× cost advantage.
| Price Type | o1 Preview | o1 Mini |
|---|---|---|
| Input | $15.00 per million tokens | $1.10 per million tokens |
| Output | $60.00 per million tokens | $4.40 per million tokens |
Performance Benchmarks
Benchmark data highlights where each model shines. On the MMLU benchmark, o1 Preview hits a 90.8% pass@1 rate, while o1 Mini scores 85.2% in zero-shot chain-of-thought mode. HumanEval coding tasks show both tied at 92.4%. Math challenges favor the Mini at 90% (0-shot CoT) versus Preview’s 85.5%. The preview model also posts a 78.2% pass@1 on MMMU, with no comparable data for Mini. GPQA physics assessments remain unreported for both.
| Benchmark | o1 Preview | o1 Mini |
|---|---|---|
| MMLU | 90.8% pass@1 | 85.2% 0-shot CoT |
| HumanEval | 92.4% | 92.4% |
| MATH | 85.5% pass@1 | 90% 0-shot CoT |
| MMMU | 78.2% pass@1 | Not available |
| GPQA | No data | No data |
These benchmarks reveal that o1 Preview leads in general multitask and multimodal understanding, while o1 Mini edges ahead on pure mathematical reasoning. Both deliver equal prowess in code generation, making the choice hinge on your primary workload and budget.
Real-World Use Cases
Startups building legal research assistants find both models can ingest lengthy court documents thanks to the 128K context window. The Mini’s extra 65.5K output tokens let it draft multi-section briefs without cutoffs, while the Preview’s superior MMLU score helps parse nuanced legal arguments. Cost-sensitive teams scaling thousands of requests per day favor the mini’s 13.6× lower token rates.
Meanwhile, research groups analyzing scientific papers or multimodal datasets lean on the Preview’s documented strength in MMMU tests. Its combined accuracy on text and image prompts trumps models without proven multimodal backing. Yet for pure text pipelines—chatbots, content generation, summarization—o1 Mini often suffices at a fraction of the spend.
Decision Guide
- Need long outputs and tight budgets? Go with o1 Mini for its 65.5K-token limit and lower token fees.
- Require top-tier multitask and multimodal performance? Choose o1 Preview for its standout MMLU and MMMU scores.
- Balance coding tasks and cost? Both models tie on HumanEval, but mini saves you up to 90% on token costs.
- Both models share the same release date, knowledge cutoff, and API providers—so licensing and availability match.
Additional Insights
Neither model is open source, which means you’ll rely on commercial API terms from OpenAI or Azure. Both freeze knowledge at October 2023, so expect no awareness of developments after that date. Both launched in lockstep on September 12, 2024, so you’re working with equally fresh architectures. Exclusive support revolves around OpenAI’s and Azure’s cloud offerings, with enterprise SLAs ensuring uptime and performance.
Key Takeaways
- Input context window: 128K tokens for both.
- Output capacity: 32.8K tokens (Preview) vs 65.5K tokens (Mini).
- Pricing: $15/$60 (Preview) vs $3/$12 or $1.10/$4.40 (Mini).
- Benchmark strengths: Preview leads on MMLU & MMMU; Mini on MATH; tie on HumanEval.
- Release date, knowledge cutoff, and API providers are identical.
- Neither model is open source—plan licensing accordingly.
In the battle of o1 Preview versus o1 Mini, there’s no one-size-fits-all answer. Your choice depends on whether you prioritize higher output ceilings and cost savings or slightly stronger multitask and multimodal accuracy. Armed with these details, you can confidently align your LLM selection with project goals, budget limits, and performance targets.
What is the key difference in maximum output tokens between o1 Preview and o1 Mini?
o1 Preview can generate up to 32.8K tokens per request. o1 Mini supports up to 65.5K tokens, allowing for longer outputs in a single request.
How do the pricing structures of o1 Preview and o1 Mini compare?
o1 Preview costs $15 per million input tokens and $60 per million output tokens. o1 Mini is significantly cheaper, with input costs reported around $3 or $1.10, and output costs between $12 and $4.40 per million tokens.
Which model performs better on multitask language understanding benchmarks?
o1 Preview performs better on MMLU with 90.8% pass@1 accuracy, outperforming o1 Mini’s 85.2% on the same benchmark.
Are there differences in coding and problem-solving performance?
Both models score equally on HumanEval benchmarks, achieving 92.4%, showing similar abilities in code generation and problem-solving.
Can you use these models from multiple API providers?
Both are available through OpenAI and Azure OpenAI Service. However, o1 Preview is noted as exclusive to OpenAI’s own API.
Which model is better for mathematical problem solving?
o1 Mini scores higher on the MATH benchmark with 90% accuracy, compared to o1 Preview’s 85.5%, making it a stronger choice for math tasks.
