DeepSeek-V4.1-Flash-1017 vs GPT-6 Luna
DeepSeek-V4.1-Flash-1017 and GPT-6 Luna are closely matched at 51.2 and 43.1 on the LLM Stats Score. GPT-6 Luna is 2.6x cheaper per token.
DeepSeek · OpenAI · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 and GPT-6 Luna are closely matched on the overall LLM Stats Score at 51.2 and 43.1.
The models split the 2 individual benchmarks reported for both models evenly.
On price, GPT-6 Luna is roughly 2.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-6 Luna also accepts a larger context window (1,050,000 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-V4.1-Flash-1017
- your work emphasizes agents — it leads those capability indexes
- you want the most recent training data — it shipped Oct 2026
- you need open weights you can self-host or fine-tune
Choose GPT-6 Luna
- cost matters — it's about 2.6x cheaper per token
- you process long inputs — it offers a 1,050,000 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 5 for GPT-6 Luna
DeepSeek-V4.1-Flash-1017 outperforms in 1 benchmarks (DeepSWE 1.1), while GPT-6 Luna is better at 1 benchmark (Agents' Last Exam).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) is 3.0x more expensive than GPT-6 Luna ($0.10/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 2.4x more expensive than GPT-6 Luna ($0.50/1M tokens).
In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than GPT-6 Luna.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-6 Luna accepts 1,050,000 input tokens compared to DeepSeek-V4.1-Flash-1017's 1,048,576 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while GPT-6 Luna is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash-1017 and GPT-6 Luna support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash-1017
GPT-6 Luna
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 is licensed under MIT, while GPT-6 Luna uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while GPT-6 Luna was released on 2026-09-22.
DeepSeek-V4.1-Flash-1017 is 1 month newer than GPT-6 Luna.
Oct 17, 2026
5 days ago
3w newerSep 22, 2026
1 months ago
Knowledge Cutoff
When training data ends
GPT-6 Luna has a documented knowledge cutoff of 2026-05-18, while DeepSeek-V4.1-Flash-1017's cutoff date is not specified.
We can confirm GPT-6 Luna's training data extends to 2026-05-18, but cannot make a direct comparison without DeepSeek-V4.1-Flash-1017's cutoff date.
—
May 2026
Provider Availability
DeepSeek-V4.1-Flash-1017 is available from DeepSeek. GPT-6 Luna is available from OpenAI.
DeepSeek-V4.1-Flash-1017
GPT-6 Luna
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and GPT-6 Luna side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs GPT-6 Luna.