Model Comparison

GPT-3.5 Turbo vs o1

o1 significantly outperforms across most benchmarks. GPT-3.5 Turbo is 35.0x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

7 benchmarks

GPT-3.5 Turbo outperforms in 0 benchmarks, while o1 is better at 7 benchmarks (GPQA, HumanEval, MATH, MathVista, MGSM, MMLU, MMMU).

o1 significantly outperforms across most benchmarks.

Tue Apr 07 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

GPT-3.5 Turbo costs less

For input processing, GPT-3.5 Turbo ($0.50/1M tokens) is 30.0x cheaper than o1 ($15.00/1M tokens).

For output processing, GPT-3.5 Turbo ($1.50/1M tokens) is 40.0x cheaper than o1 ($60.00/1M tokens).

In conclusion, o1 is more expensive than GPT-3.5 Turbo.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Apr 07 2026 • llm-stats.com
OpenAI
GPT-3.5 Turbo
Input tokens$0.50
Output tokens$1.50
Best providerAzure
OpenAI
o1
Input tokens$15.00
Output tokens$60.00
Best providerAzure
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

o1 accepts 200,000 input tokens compared to GPT-3.5 Turbo's 16,385 tokens. o1 can generate longer responses up to 100,000 tokens, while GPT-3.5 Turbo is limited to 4,096 tokens.

OpenAI
GPT-3.5 Turbo
Input16,385 tokens
Output4,096 tokens
OpenAI
o1
Input200,000 tokens
Output100,000 tokens
Tue Apr 07 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

GPT-3.5 Turbo

Proprietary

Closed source

o1

Proprietary

Closed source

Release Timeline

When each model was launched

GPT-3.5 Turbo was released on 2023-03-21, while o1 was released on 2024-12-17.

o1 is 21 months newer than GPT-3.5 Turbo.

GPT-3.5 Turbo

Mar 21, 2023

3.0 years ago

o1

Dec 17, 2024

1.3 years ago

1.7yr newer

Knowledge Cutoff

When training data ends

GPT-3.5 Turbo has a documented knowledge cutoff of 2021-09-30, while o1's cutoff date is not specified.

We can confirm GPT-3.5 Turbo's training data extends to 2021-09-30, but cannot make a direct comparison without o1's cutoff date.

GPT-3.5 Turbo

Sep 2021

o1

—

Provider Availability

GPT-3.5 Turbo is available from Azure, OpenAI. o1 is available from Azure, OpenAI.

GPT-3.5 Turbo

azure logo
Azure
Input Price:Input: $0.50/1MOutput Price:Output: $1.50/1M
openai logo
OpenAI
Input Price:Input: $0.50/1MOutput Price:Output: $1.50/1M

o1

azure logo
Azure
Input Price:Input: $15.00/1MOutput Price:Output: $60.00/1M
openai logo
OpenAI
Input Price:Input: $15.00/1MOutput Price:Output: $60.00/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

OpenAI
Less expensive input tokens
Less expensive output tokens
OpenAI
Larger context window (200,000 tokens)
Higher GPQA score (78.0% vs 30.8%)
Higher HumanEval score (88.1% vs 68.0%)
Higher MATH score (96.4% vs 43.1%)
Higher MathVista score (71.8% vs 0.0%)
Higher MGSM score (89.3% vs 56.3%)
Higher MMLU score (91.8% vs 69.8%)
Higher MMMU score (77.6% vs 0.0%)

Detailed Comparison

AI Model Comparison Table
Feature
OpenAI
GPT-3.5 Turbo
OpenAI
o1

FAQ

Common questions about GPT-3.5 Turbo vs o1

o1 significantly outperforms across most benchmarks. GPT-3.5 Turbo is made by OpenAI and o1 is made by OpenAI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
GPT-3.5 Turbo scores DROP: 70.2%, MMLU: 69.8%, HumanEval: 68.0%, MGSM: 56.3%, MATH: 43.1%. o1 scores GSM8k: 97.1%, MATH: 96.4%, GPQA Physics: 92.8%, MMLU: 91.8%, MGSM: 89.3%.
GPT-3.5 Turbo is 30.0x cheaper for input tokens. GPT-3.5 Turbo costs $0.50/M input and $1.50/M output via azure. o1 costs $15.00/M input and $60.00/M output via azure.
GPT-3.5 Turbo supports 16K tokens and o1 supports 200K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (16K vs 200K), input pricing ($0.50 vs $15.00/M). See the full comparison above for benchmark-by-benchmark results.