Gemma 4 31B vs Mercury 3
Gemma 4 31B and Mercury 3 are closely matched at 32.7 and 36.1 on the LLM Stats Score. Gemma 4 31B is 1.1x cheaper per token.
Google · Inception · Updated for 2026
Which is better?
Gemma 4 31B and Mercury 3 are closely matched on the overall LLM Stats Score at 32.7 and 36.1.
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, Gemma 4 31B is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mercury 3 also accepts a larger context window (1,000,000 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 Gemma 4 31B
- cost matters — it's about 1.1x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Mercury 3
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you process long inputs — it offers a 1,000,000 token context window
- 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
12 reported for Gemma 4 31B · 12 for Mercury 3
Gemma 4 31B 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, Gemma 4 31B ($0.09/1M tokens) is 1.1x cheaper than Mercury 3 ($0.10/1M tokens).
For output processing, Gemma 4 31B ($0.34/1M tokens) is 1.0x cheaper than Mercury 3 ($0.35/1M tokens).
In conclusion, Mercury 3 is more expensive than Gemma 4 31B.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Mercury 3 accepts 1,000,000 input tokens compared to Gemma 4 31B's 262,144 tokens. Gemma 4 31B can generate longer responses up to 262,144 tokens, while Mercury 3 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Gemma 4 31B supports multimodal inputs, whereas Mercury 3 does not.
Gemma 4 31B can handle both text and other forms of data like images, making it suitable for multimodal applications.
Gemma 4 31B
Mercury 3
License
Usage and distribution terms
Gemma 4 31B is licensed under Apache 2.0, while Mercury 3 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Gemma 4 31B was released on 2026-04-02, while Mercury 3 was released on 2026-10-19.
Mercury 3 is 7 months newer than Gemma 4 31B.
Apr 2, 2026
6 months ago
Oct 19, 2026
1 days ago
6mo newerKnowledge Cutoff
When training data ends
Gemma 4 31B has a documented knowledge cutoff of 2025-01-01, while Mercury 3's cutoff date is not specified.
We can confirm Gemma 4 31B's training data extends to 2025-01-01, but cannot make a direct comparison without Mercury 3's cutoff date.
Jan 2025
—
Provider Availability
Gemma 4 31B is available from DeepInfra, FriendliAI, Novita, Together. Mercury 3 is available from Inception.
Gemma 4 31B
Mercury 3
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemma 4 31B and Mercury 3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemma 4 31B vs Mercury 3.