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GPT-4.1 vs Ling 3.0 Flash Fin

Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 18.1. Ling 3.0 Flash Fin is 38.9x cheaper per token.

OpenAI · InclusionAI · Updated for 2026

Which is better?

Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.3 to 18.1, ranking #39 overall.

On price, Ling 3.0 Flash Fin is roughly 38.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

GPT-4.1 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

  • you process long inputs — it offers a 1,047,576 token context window

Choose Ling 3.0 Flash Fin

  • overall performance matters — it scores 43.3 and ranks #39 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • cost matters — it's about 38.9x cheaper per token
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

Core performance indexes
18.1
#213
43.3
#39
18.5
#205
44.7
#32
Cost, coverage & limits
Benchmark wins
Input price
$2.00 / M
$0.06 / M
Output price
$8.00 / M
$0.18 / M
Context window
1,047,576
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
GPT-4.1
Ling 3.0 Flash Fin
11.5#111
20.5#56
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

29 reported for GPT-4.1 · 6 for Ling 3.0 Flash Fin

No common benchmarks found

GPT-4.1 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

Ling 3.0 Flash Fin costs less

For input processing, GPT-4.1 ($2.00/1M tokens) is 33.3x more expensive than Ling 3.0 Flash Fin ($0.06/1M tokens).

For output processing, GPT-4.1 ($8.00/1M tokens) is 44.4x more expensive than Ling 3.0 Flash Fin ($0.18/1M tokens).

In conclusion, GPT-4.1 is more expensive than Ling 3.0 Flash Fin.*

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

Lowest available price from all providers
Tue Sep 08 2026 • llm-stats.com
OpenAI
GPT-4.1
Input tokens$2.00
Output tokens$8.00
Best providerOpenAI
InclusionAI
Ling 3.0 Flash Fin
Input tokens$0.06
Output tokens$0.18
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue

Context Window

Maximum input and output token capacity

GPT-4.1 accepts 1,047,576 input tokens compared to Ling 3.0 Flash Fin's 262,144 tokens. Ling 3.0 Flash Fin can generate longer responses up to 262,144 tokens, while GPT-4.1 is limited to 32,768 tokens.

OpenAI
GPT-4.1
Input1,047,576 tokens
Output32,768 tokens
InclusionAI
Ling 3.0 Flash Fin
Input262,144 tokens
Output262,144 tokens
Tue Sep 08 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

GPT-4.1 supports multimodal inputs, whereas Ling 3.0 Flash Fin does not.

GPT-4.1 can handle both text and other forms of data like images, making it suitable for multimodal applications.

GPT-4.1

Text
Images
Audio
Video

Ling 3.0 Flash Fin

Text
Images
Audio
Video

Release Timeline

When each model was launched

GPT-4.1 was released on 2025-04-14, while Ling 3.0 Flash Fin was released on 2026-09-03.

Ling 3.0 Flash Fin is 17 months newer than GPT-4.1.

GPT-4.1

Apr 14, 2025

1.4 years ago

Ling 3.0 Flash Fin

Sep 3, 2026

5 days ago

1.4yr newer

Knowledge Cutoff

When training data ends

GPT-4.1 has a documented knowledge cutoff of 2024-06-01, while Ling 3.0 Flash Fin's cutoff date is not specified.

We can confirm GPT-4.1's training data extends to 2024-06-01, but cannot make a direct comparison without Ling 3.0 Flash Fin's cutoff date.

GPT-4.1

Jun 2024

Ling 3.0 Flash Fin

Provider Availability

GPT-4.1 is available from OpenAI. Ling 3.0 Flash Fin is available from DeepInfra.

GPT-4.1

openai logo
OpenAI
Input Price:Input: $2.00/1MOutput Price:Output: $8.00/1M

Ling 3.0 Flash Fin

deepinfra logo
Deepinfra
Input Price:Input: $0.06/1MOutput Price:Output: $0.18/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against GPT-4.1 and Ling 3.0 Flash Fin side-by-side, then vote on the output you prefer.

GPT-4.1
✓ Preferred
Ling 3.0 Flash Fin
Open in Playground

FAQ

Common questions about GPT-4.1 vs Ling 3.0 Flash Fin.

Which is better, GPT-4.1 or Ling 3.0 Flash Fin?

Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 18.1. GPT-4.1 is made by OpenAI 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 GPT-4.1 compare to Ling 3.0 Flash Fin in benchmarks?

GPT-4.1 scores MMLU: 90.2%, CharXiv-D: 87.9%, IFEval: 87.4%, MMMLU: 87.3%, MMMU: 74.8%. 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%.

Is GPT-4.1 cheaper than Ling 3.0 Flash Fin?

Ling 3.0 Flash Fin is 33.3x cheaper for input tokens. GPT-4.1 costs $2.00/M input and $8.00/M output via openai. Ling 3.0 Flash Fin costs $0.06/M input and $0.18/M output via deepinfra.

What are the context window sizes for GPT-4.1 and Ling 3.0 Flash Fin?

GPT-4.1 supports 1.0M 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 GPT-4.1 and Ling 3.0 Flash Fin?

Key differences include LLM Stats Score (18.1 vs 43.3), context window (1.0M vs 262K), input pricing ($2.00 vs $0.06/M), multimodal support (yes vs no), licensing (Proprietary vs Unknown). See the full comparison above for benchmark-by-benchmark results.

Who makes GPT-4.1 and Ling 3.0 Flash Fin?

GPT-4.1 is developed by OpenAI and Ling 3.0 Flash Fin is developed by InclusionAI.