Model Comparison
o1 vs Llama 4 ScoutWhich is better in 2026?
o1 significantly outperforms across most benchmarks. Llama 4 Scout is 194.4x cheaper per token.
Verdict: o1 vs Llama 4 Scout — which is better?
o1 (by OpenAI) and Llama 4 Scout (by Meta) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
o1 outperforms in 5 benchmarks (GPQA, MATH, MathVista, MMLU, MMMU), while Llama 4 Scout is better at 1 benchmark (MGSM). o1 significantly outperforms across most benchmarks.
On price, Llama 4 Scout is roughly 194.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Llama 4 Scout also accepts a larger context window (10,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose o1 if…
- you want the strongest raw capability — it leads on 5 of 6 shared benchmarks
Choose Llama 4 Scout if…
- cost matters — it's about 194.4x cheaper per token
- you process long inputs — it offers a 10,000,000 token context window
- you want the most recent training data — it shipped Apr 2025
- you need open weights you can self-host or fine-tune
Performance Benchmarks
Comparative analysis across standard metrics
o1 outperforms in 5 benchmarks (GPQA, MATH, MathVista, MMLU, MMMU), while Llama 4 Scout is better at 1 benchmark (MGSM).
o1 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, o1 ($15.00/1M tokens) is 187.5x more expensive than Llama 4 Scout ($0.08/1M tokens).
For output processing, o1 ($60.00/1M tokens) is 200.0x more expensive than Llama 4 Scout ($0.30/1M tokens).
In conclusion, o1 is more expensive than Llama 4 Scout.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Llama 4 Scout accepts 10,000,000 input tokens compared to o1's 200,000 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while o1 is limited to 100,000 tokens.
Input Capabilities
Supported data types and modalities
Llama 4 Scout supports multimodal inputs, whereas o1 does not.
Llama 4 Scout can handle both text and other forms of data like images, making it suitable for multimodal applications.
o1
Llama 4 Scout
License
Usage and distribution terms
o1 is licensed under a proprietary license, while Llama 4 Scout uses Llama 4 Community License Agreement.
License differences may affect how you can use these models in commercial or open-source projects.
Proprietary
Closed source
Llama 4 Community License Agreement
Open weights
Release Timeline
When each model was launched
o1 was released on 2024-12-17, while Llama 4 Scout was released on 2025-04-05.
Llama 4 Scout is 4 months newer than o1.
Dec 17, 2024
1.5 years ago
Apr 5, 2025
1.2 years ago
3mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
o1 is available from Azure, OpenAI. Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together.
o1
Llama 4 Scout
Outputs Comparison
Key Takeaways
o1
View detailsOpenAI
Detailed Comparison
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FAQ
Common questions about o1 vs Llama 4 Scout.