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Kimi-k1.5 vs Ling 3.0 Flash Fin

Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 17.3.

Moonshot AI · InclusionAI · Updated for 2026

Which is better?

Ling 3.0 Flash Fin leads the overall LLM Stats Score 43.3 to 17.3, ranking #39 overall.

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

Choose Kimi-k1.5

  • you are already invested in the Moonshot AI ecosystem

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
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

Kimi-k1.5
Moonshot AI
Ling 3.0 Flash Fin
InclusionAI
Core performance indexes
LLM Stats Score
17.3
#216
43.3
#39
Reasoning
16.5
#213
44.7
#32
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.06 / M
Output price
— / M
$0.18 / M
Context window
—
262,144

Individual benchmarks

9 reported for Kimi-k1.5 · 6 for Ling 3.0 Flash Fin

No common benchmarks found

Kimi-k1.5 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

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).

Moonshot AI
Kimi-k1.5
Input- tokens
Output- tokens
InclusionAI
Ling 3.0 Flash Fin
Input262,144 tokens
Output262,144 tokens
Wed Sep 09 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Kimi-k1.5 supports multimodal inputs, whereas Ling 3.0 Flash Fin does not.

Kimi-k1.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.

Kimi-k1.5

Text
Images
Audio
Video

Ling 3.0 Flash Fin

Text
Images
Audio
Video

Release Timeline

When each model was launched

Kimi-k1.5 was released on 2025-01-20, while Ling 3.0 Flash Fin was released on 2026-09-03.

Ling 3.0 Flash Fin is 20 months newer than Kimi-k1.5.

Kimi-k1.5

Jan 20, 2025

1.6 years ago

Ling 3.0 Flash Fin

Sep 3, 2026

5 days ago

1.6yr newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against Kimi-k1.5 and Ling 3.0 Flash Fin side-by-side, then vote on the output you prefer.

Kimi-k1.5
✓ Preferred
Ling 3.0 Flash Fin
Open in Playground

FAQ

Common questions about Kimi-k1.5 vs Ling 3.0 Flash Fin.

Which is better, Kimi-k1.5 or Ling 3.0 Flash Fin?

Ling 3.0 Flash Fin leads the LLM Stats Score 43.3 to 17.3. Kimi-k1.5 is made by Moonshot AI 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 Kimi-k1.5 compare to Ling 3.0 Flash Fin in benchmarks?

Kimi-k1.5 scores MATH-500: 96.2%, CLUEWSC: 91.4%, C-Eval: 88.3%, MMLU: 87.4%, IFEval: 87.2%. 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 Kimi-k1.5 and Ling 3.0 Flash Fin?

Kimi-k1.5 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 Kimi-k1.5 and Ling 3.0 Flash Fin?

Key differences include LLM Stats Score (17.3 vs 43.3), multimodal support (yes vs no), licensing (Proprietary vs Unknown). See the full comparison above for benchmark-by-benchmark results.

Who makes Kimi-k1.5 and Ling 3.0 Flash Fin?

Kimi-k1.5 is developed by Moonshot AI and Ling 3.0 Flash Fin is developed by InclusionAI.

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