Llama 4 Scout vs Mercury 3
Mercury 3 leads the LLM Stats Score 36.1 to 7.3. Llama 4 Scout is 1.2x cheaper per token.
Meta · Inception · Updated for 2026
Which is better?
Mercury 3 leads the overall LLM Stats Score 36.1 to 7.3, ranking #98 overall.
In the 1 individual benchmarks reported for both models, Mercury 3 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Llama 4 Scout is roughly 1.2x 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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Llama 4 Scout
- cost matters — it's about 1.2x cheaper per token
- you process long inputs — it offers a 10,000,000 token context window
- you need open weights you can self-host or fine-tune
Choose Mercury 3
- overall performance matters — it scores 36.1 and ranks #98 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
12 reported for Llama 4 Scout · 12 for Mercury 3
Llama 4 Scout outperforms in 0 benchmarks, while Mercury 3 is better at 1 benchmark (GPQA).
Mercury 3 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 4 Scout ($0.08/1M tokens) is 1.3x cheaper than Mercury 3 ($0.10/1M tokens).
For output processing, Llama 4 Scout ($0.30/1M tokens) is 1.2x cheaper than Mercury 3 ($0.35/1M tokens).
In conclusion, Mercury 3 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 Mercury 3's 1,000,000 tokens. Llama 4 Scout can generate longer responses up to 10,000,000 tokens, while Mercury 3 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Llama 4 Scout supports multimodal inputs, whereas Mercury 3 does not.
Llama 4 Scout can handle both text and other forms of data like images, making it suitable for multimodal applications.
Llama 4 Scout
Mercury 3
License
Usage and distribution terms
Llama 4 Scout is licensed under Llama 4 Community License Agreement, while Mercury 3 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Llama 4 Community License Agreement
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Llama 4 Scout was released on 2025-04-05, while Mercury 3 was released on 2026-10-19.
Mercury 3 is 19 months newer than Llama 4 Scout.
Apr 5, 2025
1.5 years ago
Oct 19, 2026
0 days ago
1.5yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Llama 4 Scout is available from DeepInfra, Lambda, Novita, Groq, Fireworks, Together. Mercury 3 is available from Inception.
Llama 4 Scout
Mercury 3
Outputs Comparison
Judge for yourself.
Run your own prompts against Llama 4 Scout and Mercury 3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Llama 4 Scout vs Mercury 3.