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DeepSeek-V4.1-Flash-1017 vs K2 Horizon 0.9B

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 50.7 to 0.5.

DeepSeek · Institute of Foundation Models · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 50.7 to 0.5, ranking #17 overall.

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

Choose DeepSeek-V4.1-Flash-1017

  • overall performance matters — it scores 50.7 and ranks #17 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

Choose K2 Horizon 0.9B

  • you are already invested in the Institute of Foundation Models ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
K2 Horizon 0.9B
Institute of Foundation Models
Core performance indexes
LLM Stats Score
50.7
#17
0.5
#358
Reasoning
48.0
#22
0.6
#350
Agents
39.0
#11
-5.3
#218
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash-1017
K2 Horizon 0.9B
Math
31.8#70
3.3#308
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 8 for K2 Horizon 0.9B

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and K2 Horizon 0.9Bdon'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

762.3B diff

DeepSeek-V4.1-Flash-1017 has 762.3B more parameters than K2 Horizon 0.9B, making it 84700.6% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Institute of Foundation Models
K2 Horizon 0.9B
900,000,000parameters
763.2B
DeepSeek-V4.1-Flash-1017
0.9B
K2 Horizon 0.9B

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Institute of Foundation Models
K2 Horizon 0.9B
Input- tokens
Output- tokens
Fri Oct 30 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas K2 Horizon 0.9B does not.

DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

K2 Horizon 0.9B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while K2 Horizon 0.9B uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V4.1-Flash-1017

MIT

Open weights

K2 Horizon 0.9B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while K2 Horizon 0.9B was released on 2026-09-03.

DeepSeek-V4.1-Flash-1017 is 1 month newer than K2 Horizon 0.9B.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

1 weeks ago

1mo newer
K2 Horizon 0.9B

Sep 3, 2026

1 months ago

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?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and K2 Horizon 0.9B side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
K2 Horizon 0.9B
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs K2 Horizon 0.9B.

Which is better, DeepSeek-V4.1-Flash-1017 or K2 Horizon 0.9B?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 50.7 to 0.5. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and K2 Horizon 0.9B is made by Institute of Foundation Models. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V4.1-Flash-1017 compare to K2 Horizon 0.9B in benchmarks?

DeepSeek-V4.1-Flash-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%. K2 Horizon 0.9B scores HumanEval+: 79.9%, MBPP+: 68.0%, AIME 2026: 48.5%, AIME 2025: 41.7%, LiveCodeBench v6: 37.4%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and K2 Horizon 0.9B?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and K2 Horizon 0.9B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V4.1-Flash-1017 and K2 Horizon 0.9B?

Key differences include LLM Stats Score (50.7 vs 0.5), multimodal support (yes vs no), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and K2 Horizon 0.9B?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and K2 Horizon 0.9B is developed by Institute of Foundation Models.

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