AI model comparison
DeepSeek V4.1 Flash 1017 vs GPT-5.6 Luna
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the `deepseek-flash` API model name.
- Input / 1M
- $0.15
- Output / 1M
- $0.60
- Context
- 1.05M
GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series.
- Input / 1M
- $0.20
- Output / 1M
- $1.20
- Context
- 1.05M
Comparing image generators? Compare image models
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
DeepSeek V4.1 Flash 1017
1.7x cheaper for a typical chat
Higher intelligence score
DeepSeek V4.1 Flash 1017
39.5 vs 37.3 on Artificial Analysis
Larger context window
GPT-5.6 Luna
1.05M vs 1.05M tokens
Newer release
DeepSeek V4.1 Flash 1017
Released October 17, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
DeepSeek V4.1 Flash 101739.5GPT-5.6 Luna37.3Coding index
DeepSeek V4.1 Flash 1017Not availableGPT-5.6 Luna71.4Agentic index
DeepSeek V4.1 Flash 1017Not availableGPT-5.6 Luna42.1Pricing
DeepSeek V4.1 Flash 1017 vs GPT-5.6 Luna API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | DeepSeek V4.1 Flash 1017 | GPT-5.6 Luna |
|---|---|---|
| Input tokensPer 1M tokens | $0.15 | $0.20 |
| Output tokensPer 1M tokens | $0.60 | $1.20 |
| Cached input (read)Per 1M tokens | $0.00 | $0.02 |
| Cache writePer 1M tokens | Not available | $0.25 |
| Long-context pricingPer 1M tokens, for very long prompts | Same rate at any length | $0.40 in / $1.80 out above 272K |
| Web searchPer request | Not available | $0.01 |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
DeepSeek V4.1 Flash 1017$0.60GPT-5.6 Luna$1Coding task
30K in, 4K out
DeepSeek V4.1 Flash 1017$6.90$2.49 cachedGPT-5.6 Luna$10.80$5.40 cachedLong document summary
150K in, 2K out
DeepSeek V4.1 Flash 1017$23.70$1.65 cachedGPT-5.6 Luna$32.40$5.40 cachedSpecs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | DeepSeek V4.1 Flash 1017 | GPT-5.6 Luna |
|---|---|---|
| Context window | 1.05M tokens | 1.05M tokens |
| Max output | 393K tokens | 128K tokens |
| Input types | Image, Text | File, Image, Text |
| Output types | Text | Text |
| Reasoning effort levels | Max, High, Low, None | Max, Extra high, High, Medium, Low, None |
| Default reasoning effort | High | Medium |
| Release date | October 17, 2026 | July 9, 2026 |
Features
Supported features
API features available for each model.
| Metric | DeepSeek V4.1 Flash 1017 | GPT-5.6 Luna |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Not supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Supported |
| Temperature | Supported | Not supported |
| Stop sequences | Supported | Not supported |
| Deterministic seed | Not supported | Supported |
| Verbosity control | Not supported | Supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
DeepSeek V4.1 Flash 1017View full model details- DeepSeek V4.1 Flash 1017 vs Step 5 Preview
- DeepSeek V4.1 Flash 1017 vs Claude Haiku 5.5
- DeepSeek V4.1 Flash 1017 vs GLM 5.3 Flash
- DeepSeek V4.1 Flash 1017 vs Ling 3.1 Flash
- DeepSeek V4.1 Flash 1017 vs GPT-6 Luna
- DeepSeek V4.1 Flash 1017 vs MiMo-V2.6-Flash
- DeepSeek V4.1 Flash 1017 vs DeepSeek V4 Pro 0813
- DeepSeek V4.1 Flash 1017 vs DeepSeek V4 Flash Vision Exp
- DeepSeek V4.1 Flash 1017 vs DeepSeek V4 Flash 0731
GPT-5.6 LunaView full model details- GPT-5.6 Luna vs Step 5 Preview
- GPT-5.6 Luna vs GLM 5.3 Flash
- GPT-5.6 Luna vs MiMo-V2.6-Flash
- GPT-5.6 Luna vs GPT-6 Luna
- GPT-5.6 Luna vs DeepSeek V4 Flash 0731
- GPT-5.6 Luna vs Claude Haiku 5.5
- GPT-5.6 Luna vs MiMo-V2.6-Pro
- GPT-5.6 Luna vs GLM 5.2
- GPT-5.6 Luna vs DeepSeek V4 Pro 0813
- GPT-5.6 Luna vs Ling 3.1 Flash
- GPT-5.6 Luna vs Gemini 3.7 Flash
- GPT-5.6 Luna vs Muse Spark 1.2
- GPT-5.6 Luna vs Qwen3.8 27B
- GPT-5.6 Luna vs Mistral Large 4
- GPT-5.6 Luna vs GPT-5.5
- GPT-5.6 Luna vs GPT-6 Luna Pro
- GPT-5.6 Luna vs DeepSeek V4 Flash Vision Exp
- GPT-5.6 Luna vs Gemini 3.6 Flash
- GPT-5.6 Luna vs Muse Spark 1.1
- GPT-5.6 Luna vs GLM 5.3 FlashX
- GPT-5.6 Luna vs GPT-5.6 Luna Pro
Our take
DeepSeek V4.1 Flash 1017 is 1.7x cheaper for a typical chat. DeepSeek V4.1 Flash 1017 scores higher on the Artificial Analysis Intelligence Index (39.5 vs 37.3). GPT-5.6 Luna has the larger context window (1.05M vs 1.05M tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is DeepSeek V4.1 Flash 1017 or GPT-5.6 Luna cheaper?
DeepSeek V4.1 Flash 1017 is cheaper for a typical chat (2K input tokens and 500 output tokens). DeepSeek V4.1 Flash 1017 costs $0.15 per 1M input tokens and $0.60 per 1M output tokens, while GPT-5.6 Luna costs $0.20 per 1M input tokens and $1.20 per 1M output tokens.
Which has a bigger context window, DeepSeek V4.1 Flash 1017 or GPT-5.6 Luna?
GPT-5.6 Luna supports up to 1.05M tokens of context, compared with 1.05M for DeepSeek V4.1 Flash 1017.
Is DeepSeek V4.1 Flash 1017 smarter than GPT-5.6 Luna?
On the Artificial Analysis Intelligence Index, DeepSeek V4.1 Flash 1017 scores 39.5 and GPT-5.6 Luna scores 37.3, putting DeepSeek V4.1 Flash 1017 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use DeepSeek V4.1 Flash 1017 and GPT-5.6 Luna at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see DeepSeek V4.1 Flash 1017 and GPT-5.6 Luna answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to DeepSeek V4.1 Flash 1017 and GPT-5.6 Luna at the same time. Compare the answers side by side and keep the best one.