AI Models · Open-Source vs Paid

DeepSeek V4 Flash Open vs GPT-5.6 Luna Paid

DeepSeek V4 Flash vs GPT-5.6 Luna compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.

Prices & specs refreshed from live data · OpenSourceAI.tech

Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.

Choose DeepSeek V4 Flash if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose GPT-5.6 Luna if you want frontier capability through a managed API with zero infrastructure to run.

DeepSeek V4 Flash vs GPT-5.6 Luna specs

SpecDeepSeek V4 FlashGPT-5.6 Luna
MakerDeepSeekOpenAI
TypeOpen-weightProprietary
Context window1M tokens1.1M tokens
Input price$0.09/M · free self-host$1/M
Output price$0.19/M · free self-host$6/M
Vision / multimodalNoYes
Tool / function callingYesYes
Self-hostableYesNo (API only)
LicenseMITProprietary

Feature comparison

CapabilityDeepSeek V4 FlashGPT-5.6 Luna
Open weights (downloadable)
Self-hostable
Runs fully offline
Vision / multimodal
Tool / function calling
1M+ context window

Benchmarks: DeepSeek V4 Flash vs GPT-5.6 Luna

Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).

DeepSeek V4 Flash delivers 10× more intelligence per dollar.
DeepSeek V4 Flash
GPT-5.6 Luna
Intelligence index
40.3
51.2
Coding index
56.2
71.4
GPQA
89.4%
91.1%
Humanity's Last Exam
32.1%
37.2%
Long Context Reasoning
63%
74%
SciCode
44.9%
52.5%
IFBench
79.2%
τ²-Bench
95%
τ-Bench Banking
22.9%
27.2%
Terminal-Bench
61.8%
80.9%
Terminal-Bench Hard
35.6%
Speed
112.7 tok/s
212.1 tok/s
Latency
0.88s
63.49s
Intelligence per $
230.3
22.8

Benchmark data by Artificial Analysis.

How DeepSeek V4 Flash and GPT-5.6 Luna score

🏆 Best value & openness: DeepSeek V4 Flash (4.7 vs 3.3 / 5)
CriterionDeepSeek V4 FlashGPT-5.6 Luna
Cost-efficiency5.04.0
Context window5.05.0
Openness5.01.5
Self-hosting5.01.0
Multimodality3.55.0

Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.

What each model is

DeepSeek V4 Flash Open

DeepSeek · Open-weight

DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...

GPT-5.6 Luna Paid

OpenAI · Proprietary

GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for...

Other models in these families

These variants are tracked but not compared here — one page per family keeps the comparison readable.

Other variants tracked
DeepSeek V4 ProDeepSeek V3.2DeepSeek V3.1DeepSeek V3.1 TerminusDeepSeek V3 0324DeepSeek V3DeepSeek V3.2 ExpR1 0528R1

Frequently asked questions

Is DeepSeek V4 Flash as good as GPT-5.6 Luna?

DeepSeek V4 Flash is open-weight and competitive on many tasks, but GPT-5.6 Luna may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.

Can I run DeepSeek V4 Flash locally?

Yes. DeepSeek V4 Flash has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. GPT-5.6 Luna is API-only and cannot be self-hosted.

How much cheaper is DeepSeek V4 Flash?

DeepSeek V4 Flash costs $0.19/M output vs $6/M for GPT-5.6 Luna — roughly 32x cheaper via API, and free if you self-host.

DeepSeek V4 Flash vs GPT-5.6 Luna — which should I pick in 2026?

Choose DeepSeek V4 Flash if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose GPT-5.6 Luna if you want frontier capability through a managed API with zero infrastructure to run.

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