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o3-mini vs Parse

Comparing o3-mini and Parse across benchmarks, pricing, and capabilities.

OpenAI · Cohere · Updated for 2026

Which is better?

o3-mini and Parse trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

o3-mini also accepts a larger context window (200,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose o3-mini

  • you process long inputs — it offers a 200,000 token context window

Choose Parse

  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Benchmark wins
Input price
$1.10 / M
— / M
Output price
$4.40 / M
— / M
Context window
200,000
8,192

Individual benchmarks

25 reported for o3-mini · 1 for Parse

No common benchmarks found

o3-mini and Parsedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

o3-mini accepts 200,000 input tokens compared to Parse's 8,192 tokens. Only o3-mini specifies output context (100,000 tokens).

OpenAI
o3-mini
Input200,000 tokens
Output100,000 tokens
Cohere
Parse
Input8,192 tokens
Output- tokens
Mon Aug 31 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Parse supports multimodal inputs, whereas o3-mini does not.

Parse can handle both text and other forms of data like images, making it suitable for multimodal applications.

o3-mini

Text
Images
Audio
Video

Parse

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

o3-mini

Proprietary

Closed source

Parse

Proprietary

Closed source

Release Timeline

When each model was launched

o3-mini was released on 2025-01-30, while Parse was released on 2026-08-27.

Parse is 19 months newer than o3-mini.

o3-mini

Jan 30, 2025

1.6 years ago

Parse

Aug 27, 2026

3 days ago

1.6yr newer

Knowledge Cutoff

When training data ends

o3-mini has a documented knowledge cutoff of 2023-09-30, while Parse's cutoff date is not specified.

We can confirm o3-mini's training data extends to 2023-09-30, but cannot make a direct comparison without Parse's cutoff date.

o3-mini

Sep 2023

Parse

Provider Availability

o3-mini is available from Azure, OpenAI. Parse is available from Azure, Cohere.

o3-mini

azure logo
Azure
Input Price:Input: $1.10/1MOutput Price:Output: $4.40/1M
openai logo
OpenAI
Input Price:Input: $1.10/1MOutput Price:Output: $4.40/1M

Parse

azure logo
Azure
cohere logo
Cohere
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against o3-mini and Parse side-by-side, then vote on the output you prefer.

o3-mini
✓ Preferred
Parse
Open in Playground

FAQ

Common questions about o3-mini vs Parse.

Which is better, o3-mini or Parse?

o3-mini (OpenAI) and Parse (Cohere) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does o3-mini compare to Parse in benchmarks?

o3-mini scores COLLIE: 98.7%, MATH: 97.9%, IFEval: 93.9%, MGSM: 92.0%, AIME 2024: 87.3%. Parse scores ParseBench: 79.2%.

What are the context window sizes for o3-mini and Parse?

o3-mini supports 200K tokens and Parse supports 8K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between o3-mini and Parse?

Key differences include context window (200K vs 8K), multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.

Who makes o3-mini and Parse?

o3-mini is developed by OpenAI and Parse is developed by Cohere.