GPT-4.1 nano vs Mercury 3
Mercury 3 leads the LLM Stats Score 36.1 to 1.1. Mercury 3 is 1.1x cheaper per token.
OpenAI · Inception · Updated for 2026
Which is better?
Mercury 3 leads the overall LLM Stats Score 36.1 to 1.1, ranking #98 overall.
In the 2 individual benchmarks reported for both models, Mercury 3 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Mercury 3 is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-4.1 nano also accepts a larger context window (1,047,576 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 GPT-4.1 nano
- you process long inputs — it offers a 1,047,576 token context window
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 2 of 2 exact shared results
- cost matters — it's about 1.1x cheaper per token
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
24 reported for GPT-4.1 nano · 12 for Mercury 3
GPT-4.1 nano outperforms in 0 benchmarks, while Mercury 3 is better at 2 benchmarks (GPQA, Multi-Challenge).
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, GPT-4.1 nano ($0.10/1M tokens) costs the same as Mercury 3 ($0.10/1M tokens).
For output processing, GPT-4.1 nano ($0.40/1M tokens) is 1.1x more expensive than Mercury 3 ($0.35/1M tokens).
In conclusion, GPT-4.1 nano is more expensive than Mercury 3.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-4.1 nano accepts 1,047,576 input tokens compared to Mercury 3's 1,000,000 tokens. Mercury 3 can generate longer responses up to 131,072 tokens, while GPT-4.1 nano is limited to 32,768 tokens.
Input capabilities
Documented input modalities across available providers
GPT-4.1 nano supports multimodal inputs, whereas Mercury 3 does not.
GPT-4.1 nano can handle both text and other forms of data like images, making it suitable for multimodal applications.
GPT-4.1 nano
Mercury 3
License
Usage and distribution terms
Both models are licensed under proprietary licenses.
Both models have usage restrictions defined by their respective organizations.
Proprietary
Closed source
Proprietary
Closed source
Release Timeline
When each model was launched
GPT-4.1 nano was released on 2025-04-14, while Mercury 3 was released on 2026-10-19.
Mercury 3 is 18 months newer than GPT-4.1 nano.
Apr 14, 2025
1.5 years ago
Oct 19, 2026
0 days ago
1.5yr newerKnowledge Cutoff
When training data ends
GPT-4.1 nano has a documented knowledge cutoff of 2024-05-31, while Mercury 3's cutoff date is not specified.
We can confirm GPT-4.1 nano's training data extends to 2024-05-31, but cannot make a direct comparison without Mercury 3's cutoff date.
May 2024
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Provider Availability
GPT-4.1 nano is available from OpenAI. Mercury 3 is available from Inception.
GPT-4.1 nano
Mercury 3
Outputs Comparison
Judge for yourself.
Run your own prompts against GPT-4.1 nano and Mercury 3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about GPT-4.1 nano vs Mercury 3.