Model Comparison

Gemini 3.5 Flash-Lite vs Mercury 2Which is better in 2026?

Comparing Gemini 3.5 Flash-Lite and Mercury 2 across benchmarks, pricing, and capabilities.

Verdict: Gemini 3.5 Flash-Lite vs Mercury 2 — which is better?

Gemini 3.5 Flash-Lite (by Google) and Mercury 2 (by Inception) 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.

On price, Mercury 2 is roughly 2.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Gemini 3.5 Flash-Lite also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.

Choose Gemini 3.5 Flash-Lite if…

  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Jul 2026

Choose Mercury 2 if…

  • cost matters — it's about 2.3x cheaper per token

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Gemini 3.5 Flash-Lite and Mercury 2don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Mercury 2 costs less

For input processing, Gemini 3.5 Flash-Lite ($0.30/1M tokens) is 1.2x more expensive than Mercury 2 ($0.25/1M tokens).

For output processing, Gemini 3.5 Flash-Lite ($2.50/1M tokens) is 3.3x more expensive than Mercury 2 ($0.75/1M tokens).

In conclusion, Gemini 3.5 Flash-Lite is more expensive than Mercury 2.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Mon Jul 27 2026 • llm-stats.com
Google
Gemini 3.5 Flash-Lite
Input tokens$0.30
Output tokens$2.50
Best providerGoogle
Inception
Mercury 2
Input tokens$0.25
Output tokens$0.75
Best providerInception
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Gemini 3.5 Flash-Lite accepts 1,048,576 input tokens compared to Mercury 2's 128,000 tokens. Gemini 3.5 Flash-Lite can generate longer responses up to 65,536 tokens, while Mercury 2 is limited to 8,192 tokens.

Google
Gemini 3.5 Flash-Lite
Input1,048,576 tokens
Output65,536 tokens
Inception
Mercury 2
Input128,000 tokens
Output8,192 tokens
Mon Jul 27 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 3.5 Flash-Lite supports multimodal inputs, whereas Mercury 2 does not.

Gemini 3.5 Flash-Lite can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 3.5 Flash-Lite

Text
Images
Audio
Video

Mercury 2

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

Gemini 3.5 Flash-Lite

Proprietary

Closed source

Mercury 2

Proprietary

Closed source

Release Timeline

When each model was launched

Gemini 3.5 Flash-Lite was released on 2026-07-21, while Mercury 2 was released on 2026-02-24.

Gemini 3.5 Flash-Lite is 5 months newer than Mercury 2.

Gemini 3.5 Flash-Lite

Jul 21, 2026

4 days ago

4mo newer
Mercury 2

Feb 24, 2026

5 months ago

Knowledge Cutoff

When training data ends

Gemini 3.5 Flash-Lite has a documented knowledge cutoff of 2026-03-31, while Mercury 2's cutoff date is not specified.

We can confirm Gemini 3.5 Flash-Lite's training data extends to 2026-03-31, but cannot make a direct comparison without Mercury 2's cutoff date.

Gemini 3.5 Flash-Lite

Mar 2026

Mercury 2

—

Provider Availability

Gemini 3.5 Flash-Lite is available from Google. Mercury 2 is available from Inception.

Gemini 3.5 Flash-Lite

google logo
Google
Input Price:Input: $0.30/1MOutput Price:Output: $2.50/1M

Mercury 2

inception logo
Inception
Input Price:Input: $0.25/1MOutput Price:Output: $0.75/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Google
Larger context window (1,048,576 tokens)
Supports multimodal inputs
Inception
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against Gemini 3.5 Flash-Lite and Mercury 2 side-by-side, then vote on the output you prefer.

Gemini 3.5 Flash-Lite
✓ Preferred
Mercury 2
Open in Playground
AI Model Comparison Table
Feature
Google
Gemini 3.5 Flash-Lite
Inception
Mercury 2

FAQ

Common questions about Gemini 3.5 Flash-Lite vs Mercury 2.

Which is better, Gemini 3.5 Flash-Lite or Mercury 2?

Gemini 3.5 Flash-Lite (Google) and Mercury 2 (Inception) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Gemini 3.5 Flash-Lite compare to Mercury 2 in benchmarks?

Gemini 3.5 Flash-Lite scores CharXiv-R: 76.5%, OSWorld-Verified: 74.0%, SWE-Bench Pro: 54.2%, Terminal-Bench 2.1: 54.0%, MLE-Bench: 39.2%. Mercury 2 scores AIME 2025: 91.1%, GPQA: 74.0%, IFBench: 71.0%, LiveCodeBench: 67.0%, Tau2 Airline: 53.0%.

Is Gemini 3.5 Flash-Lite cheaper than Mercury 2?

Mercury 2 is 1.2x cheaper for input tokens. Gemini 3.5 Flash-Lite costs $0.30/M input and $2.50/M output via google. Mercury 2 costs $0.25/M input and $0.75/M output via inception.

What are the context window sizes for Gemini 3.5 Flash-Lite and Mercury 2?

Gemini 3.5 Flash-Lite supports 1.0M tokens and Mercury 2 supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemini 3.5 Flash-Lite and Mercury 2?

Key differences include context window (1.0M vs 128K), input pricing ($0.30 vs $0.25/M), multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 3.5 Flash-Lite and Mercury 2?

Gemini 3.5 Flash-Lite is developed by Google and Mercury 2 is developed by Inception.

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