Gemma 4 E4B vs Ling 3.0 Flash Fin
Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 13.9. Gemma 4 E4B is 2.3x cheaper per token.
Google · InclusionAI · Updated for 2026
Which is better?
Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.3 to 13.9, ranking #39 overall.
On price, Gemma 4 E4B is roughly 2.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Ling 3.0 Flash Fin also accepts a larger context window (262,144 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 E4B
- cost matters — it's about 2.3x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Ling 3.0 Flash Fin
- overall performance matters — it scores 43.3 and ranks #39 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2026
At a glance
The differences that matter most.
Individual benchmarks
11 reported for Gemma 4 E4B · 6 for Ling 3.0 Flash Fin
Gemma 4 E4B and Ling 3.0 Flash Findon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Gemma 4 E4B ($0.02/1M tokens) is 3.0x cheaper than Ling 3.0 Flash Fin ($0.06/1M tokens).
For output processing, Gemma 4 E4B ($0.10/1M tokens) is 1.8x cheaper than Ling 3.0 Flash Fin ($0.18/1M tokens).
In conclusion, Ling 3.0 Flash Fin is more expensive than Gemma 4 E4B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Ling 3.0 Flash Fin has 116.0B more parameters than Gemma 4 E4B, making it 1450.0% larger.
Context Window
Maximum input and output token capacity
Ling 3.0 Flash Fin accepts 262,144 input tokens compared to Gemma 4 E4B's 131,072 tokens. Ling 3.0 Flash Fin can generate longer responses up to 262,144 tokens, while Gemma 4 E4B is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Gemma 4 E4B supports multimodal inputs, whereas Ling 3.0 Flash Fin does not.
Gemma 4 E4B can handle both text and other forms of data like images, making it suitable for multimodal applications.
Gemma 4 E4B
Ling 3.0 Flash Fin
Release Timeline
When each model was launched
Gemma 4 E4B was released on 2026-04-02, while Ling 3.0 Flash Fin was released on 2026-09-03.
Ling 3.0 Flash Fin is 5 months newer than Gemma 4 E4B.
Apr 2, 2026
5 months ago
Sep 3, 2026
5 days ago
5mo newerKnowledge Cutoff
When training data ends
Gemma 4 E4B has a documented knowledge cutoff of 2025-01-01, while Ling 3.0 Flash Fin's cutoff date is not specified.
We can confirm Gemma 4 E4B's training data extends to 2025-01-01, but cannot make a direct comparison without Ling 3.0 Flash Fin's cutoff date.
Jan 2025
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Provider Availability
Gemma 4 E4B is available from DeepInfra. Ling 3.0 Flash Fin is available from DeepInfra.
Gemma 4 E4B
Ling 3.0 Flash Fin
Outputs Comparison
Judge for yourself.
Run your own prompts against Gemma 4 E4B and Ling 3.0 Flash Fin side-by-side, then vote on the output you prefer.
FAQ
Common questions about Gemma 4 E4B vs Ling 3.0 Flash Fin.