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DiffusionGemma 26B-A4B vs Ling 3.0 Flash Fin

Ling 3.0 Flash Fin leads the LLM Stats Score 43.2 to 19.4.

Google · InclusionAI · Updated for 2026

Which is better?

Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.2 to 19.4, ranking #41 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DiffusionGemma 26B-A4B

  • you need open weights you can self-host or fine-tune

Choose Ling 3.0 Flash Fin

  • overall performance matters — it scores 43.2 and ranks #41 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

DiffusionGemma 26B-A4B
Google
Ling 3.0 Flash Fin
InclusionAI
Core performance indexes
LLM Stats Score
19.4
#204
43.2
#41
Reasoning
19.1
#201
44.6
#33
Agents
0.5
#170
29.5
#36
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.06 / M
Output price
— / M
$0.18 / M
Context window
—
262,144

Individual benchmarks

14 reported for DiffusionGemma 26B-A4B · 6 for Ling 3.0 Flash Fin

No common benchmarks found

DiffusionGemma 26B-A4B 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

Model Size

Parameter count comparison

98.8B diff

Ling 3.0 Flash Fin has 98.8B more parameters than DiffusionGemma 26B-A4B, making it 392.1% larger.

Google
DiffusionGemma 26B-A4B
25.2Bparameters
InclusionAI
Ling 3.0 Flash Fin
124.0Bparameters
25.2B
DiffusionGemma 26B-A4B
124.0B
Ling 3.0 Flash Fin

Context Window

Maximum input and output token capacity

Only Ling 3.0 Flash Fin specifies input context (262,144 tokens). Only Ling 3.0 Flash Fin specifies output context (262,144 tokens).

Google
DiffusionGemma 26B-A4B
Input- tokens
Output- tokens
InclusionAI
Ling 3.0 Flash Fin
Input262,144 tokens
Output262,144 tokens
Fri Sep 11 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DiffusionGemma 26B-A4B supports multimodal inputs, whereas Ling 3.0 Flash Fin does not.

DiffusionGemma 26B-A4B can handle both text and other forms of data like images, making it suitable for multimodal applications.

DiffusionGemma 26B-A4B

Text
Images
Audio
Video

Ling 3.0 Flash Fin

Text
Images
Audio
Video

Release Timeline

When each model was launched

DiffusionGemma 26B-A4B was released on 2026-06-10, while Ling 3.0 Flash Fin was released on 2026-09-03.

Ling 3.0 Flash Fin is 3 months newer than DiffusionGemma 26B-A4B.

DiffusionGemma 26B-A4B

Jun 10, 2026

3 months ago

Ling 3.0 Flash Fin

Sep 3, 2026

6 days ago

2mo newer

Knowledge Cutoff

When training data ends

DiffusionGemma 26B-A4B has a documented knowledge cutoff of 2025-01-01, while Ling 3.0 Flash Fin's cutoff date is not specified.

We can confirm DiffusionGemma 26B-A4B's training data extends to 2025-01-01, but cannot make a direct comparison without Ling 3.0 Flash Fin's cutoff date.

DiffusionGemma 26B-A4B

Jan 2025

Ling 3.0 Flash Fin

—

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DiffusionGemma 26B-A4B and Ling 3.0 Flash Fin side-by-side, then vote on the output you prefer.

DiffusionGemma 26B-A4B
✓ Preferred
Ling 3.0 Flash Fin
Open in Playground

FAQ

Common questions about DiffusionGemma 26B-A4B vs Ling 3.0 Flash Fin.

Which is better, DiffusionGemma 26B-A4B or Ling 3.0 Flash Fin?

Ling 3.0 Flash Fin leads the LLM Stats Score 43.2 to 19.4. DiffusionGemma 26B-A4B is made by Google and Ling 3.0 Flash Fin is made by InclusionAI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DiffusionGemma 26B-A4B compare to Ling 3.0 Flash Fin in benchmarks?

DiffusionGemma 26B-A4B scores MMMLU: 81.5%, MMLU-Pro: 77.6%, GPQA: 73.2%, MathVision: 70.5%, AIME 2026: 69.1%. Ling 3.0 Flash Fin scores SpreadSheetBench-v1: 86.5%, Finance Agent v1.1: 69.2%, Finance Agent v2: 59.8%, Tau3 Banking: 41.0%, APEX-Agents: 29.2%.

What are the context window sizes for DiffusionGemma 26B-A4B and Ling 3.0 Flash Fin?

DiffusionGemma 26B-A4B supports an unknown number of tokens and Ling 3.0 Flash Fin supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DiffusionGemma 26B-A4B and Ling 3.0 Flash Fin?

Key differences include LLM Stats Score (19.4 vs 43.2), multimodal support (yes vs no), licensing (Apache 2.0 vs Unknown). See the full comparison above for benchmark-by-benchmark results.

Who makes DiffusionGemma 26B-A4B and Ling 3.0 Flash Fin?

DiffusionGemma 26B-A4B is developed by Google and Ling 3.0 Flash Fin is developed by InclusionAI.

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