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DeepSeek-V3.2 (Non-thinking) vs DeepSeek-V4.1-Flash-1017

Comparing DeepSeek-V3.2 (Non-thinking) and DeepSeek-V4.1-Flash-1017 across benchmarks, pricing, and capabilities.

DeepSeek · DeepSeek · Updated for 2026

Which is better?

DeepSeek-V3.2 (Non-thinking) and DeepSeek-V4.1-Flash-1017 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

On price, DeepSeek-V3.2 (Non-thinking) is roughly 1.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

DeepSeek-V4.1-Flash-1017 also accepts a larger context window (1,048,576 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 DeepSeek-V3.2 (Non-thinking)

  • cost matters — it's about 1.7x cheaper per token

Choose DeepSeek-V4.1-Flash-1017

  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

DeepSeek-V3.2 (Non-thinking)
DeepSeek
DeepSeek-V4.1-Flash-1017
DeepSeek
Benchmark wins
—
—
Input price
$0.28 / M
$0.30 / M
Output price
$0.42 / M
$1.20 / M
Context window
131,072
1,048,576

Individual benchmarks

0 reported for DeepSeek-V3.2 (Non-thinking) · 11 for DeepSeek-V4.1-Flash-1017

No common benchmarks found

DeepSeek-V3.2 (Non-thinking) and DeepSeek-V4.1-Flash-1017don'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

DeepSeek-V3.2 (Non-thinking) costs less

For input processing, DeepSeek-V3.2 (Non-thinking) ($0.28/1M tokens) is 1.1x cheaper than DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens).

For output processing, DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens) is 2.9x cheaper than DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens).

In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than DeepSeek-V3.2 (Non-thinking).*

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

Lowest available price from all providers
Fri Oct 23 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2 (Non-thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
DeepSeek
DeepSeek-V4.1-Flash-1017
Input tokens$0.30
Output tokens$1.20
Best providerDeepSeek
Notice missing or incorrect data?

Model Size

Parameter count comparison

78.2B diff

DeepSeek-V4.1-Flash-1017 has 78.2B more parameters than DeepSeek-V3.2 (Non-thinking), making it 11.4% larger.

DeepSeek
DeepSeek-V3.2 (Non-thinking)
685.0Bparameters
DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
685.0B
DeepSeek-V3.2 (Non-thinking)
763.2B
DeepSeek-V4.1-Flash-1017

Context Window

Maximum input and output token capacity

DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to DeepSeek-V3.2 (Non-thinking)'s 131,072 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while DeepSeek-V3.2 (Non-thinking) is limited to 8,192 tokens.

DeepSeek
DeepSeek-V3.2 (Non-thinking)
Input131,072 tokens
Output8,192 tokens
DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Fri Oct 23 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas DeepSeek-V3.2 (Non-thinking) 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-V3.2 (Non-thinking)

Text
Images
Audio
Video

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek-V3.2 (Non-thinking)

MIT

Open weights

DeepSeek-V4.1-Flash-1017

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while DeepSeek-V4.1-Flash-1017 was released on 2026-10-17.

DeepSeek-V4.1-Flash-1017 is 11 months newer than DeepSeek-V3.2 (Non-thinking).

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

10 months ago

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

2 days ago

10mo 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

Provider Availability

DeepSeek-V3.2 (Non-thinking) is available from DeepSeek. DeepSeek-V4.1-Flash-1017 is available from DeepSeek.

DeepSeek-V3.2 (Non-thinking)

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M

DeepSeek-V4.1-Flash-1017

deepseek logo
DeepSeek
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V3.2 (Non-thinking) and DeepSeek-V4.1-Flash-1017 side-by-side, then vote on the output you prefer.

DeepSeek-V3.2 (Non-thinking)
✓ Preferred
DeepSeek-V4.1-Flash-1017
Open in Playground

FAQ

Common questions about DeepSeek-V3.2 (Non-thinking) vs DeepSeek-V4.1-Flash-1017.

Which is better, DeepSeek-V3.2 (Non-thinking) or DeepSeek-V4.1-Flash-1017?

DeepSeek-V3.2 (Non-thinking) (DeepSeek) and DeepSeek-V4.1-Flash-1017 (DeepSeek) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-V3.2 (Non-thinking) compare to DeepSeek-V4.1-Flash-1017 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%.

Is DeepSeek-V3.2 (Non-thinking) cheaper than DeepSeek-V4.1-Flash-1017?

DeepSeek-V3.2 (Non-thinking) is 1.1x cheaper for input tokens. DeepSeek-V3.2 (Non-thinking) costs $0.28/M input and $0.42/M output via deepseek. DeepSeek-V4.1-Flash-1017 costs $0.30/M input and $1.20/M output via deepseek.

What are the context window sizes for DeepSeek-V3.2 (Non-thinking) and DeepSeek-V4.1-Flash-1017?

DeepSeek-V3.2 (Non-thinking) supports 131K tokens and DeepSeek-V4.1-Flash-1017 supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.2 (Non-thinking) and DeepSeek-V4.1-Flash-1017?

Key differences include context window (131K vs 1.0M), input pricing ($0.28 vs $0.30/M), multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.

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