Llama 3.2 11B Instruct vs o4-mini
o4-mini leads the LLM Stats Score 27.5 to -1.3. Llama 3.2 11B Instruct is 38.5x cheaper per token.
Meta · OpenAI · Updated for 2026
Which is better?
o4-mini leads the overall LLM Stats Score 27.5 to -1.3, ranking #143 overall.
In the 3 individual benchmarks reported for both models, o4-mini wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, Llama 3.2 11B Instruct is roughly 38.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
o4-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 Llama 3.2 11B Instruct
- cost matters — it's about 38.5x cheaper per token
- you need open weights you can self-host or fine-tune
Choose o4-mini
- overall performance matters — it scores 27.5 and ranks #143 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 200,000 token context window
- you want the most recent training data — it shipped Apr 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for Llama 3.2 11B Instruct · 14 for o4-mini
Llama 3.2 11B Instruct outperforms in 0 benchmarks, while o4-mini is better at 3 benchmarks (GPQA, MathVista, MMMU).
o4-mini significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Llama 3.2 11B Instruct ($0.05/1M tokens) is 22.0x cheaper than o4-mini ($1.10/1M tokens).
For output processing, Llama 3.2 11B Instruct ($0.05/1M tokens) is 88.0x cheaper than o4-mini ($4.40/1M tokens).
In conclusion, o4-mini is more expensive than Llama 3.2 11B Instruct.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
o4-mini accepts 200,000 input tokens compared to Llama 3.2 11B Instruct's 128,000 tokens. Llama 3.2 11B Instruct can generate longer responses up to 128,000 tokens, while o4-mini is limited to 100,000 tokens.
Input capabilities
Documented input modalities across available providers
Both Llama 3.2 11B Instruct and o4-mini support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Llama 3.2 11B Instruct
o4-mini
License
Usage and distribution terms
Llama 3.2 11B Instruct is licensed under Llama 3.2 Community License, while o4-mini uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Llama 3.2 Community License
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Llama 3.2 11B Instruct was released on 2024-09-25, while o4-mini was released on 2025-04-16.
o4-mini is 7 months newer than Llama 3.2 11B Instruct.
Sep 25, 2024
2.0 years ago
Apr 16, 2025
1.4 years ago
6mo newerKnowledge Cutoff
When training data ends
Llama 3.2 11B Instruct has a knowledge cutoff of 2023-12-31, while o4-mini has a cutoff of 2024-05-31.
o4-mini has more recent training data (up to 2024-05-31), making it potentially better informed about events through that date compared to Llama 3.2 11B Instruct (2023-12-31).
Dec 2023
May 2024
5 mo newerProvider Availability
Llama 3.2 11B Instruct is available from DeepInfra, Sambanova, Bedrock, Groq, Together, Fireworks. o4-mini is available from OpenAI.
Llama 3.2 11B Instruct
o4-mini
Outputs Comparison
Judge for yourself.
Run your own prompts against Llama 3.2 11B Instruct and o4-mini side-by-side, then vote on the output you prefer.
FAQ
Common questions about Llama 3.2 11B Instruct vs o4-mini.