GPT-5.6 Luna vs DeepSeek V4-Flash
OpenAI's GPT-5.6 Luna and DeepSeek's DeepSeek V4-Flash both target production language workloads, but they price and behave differently. Here is the side-by-side.
Pricing checked against provider documentation on . How we verify
The short answer
DeepSeek V4-Flash is the cheaper option — roughly 1.4x less on a blended workload, and it suits the cheapest capable open-weight option. GPT-5.6 Luna justifies its premium when you need high-volume work on a tight budget.
OpenAI • GPT-5.6
$0.20 / $1.20
/ 1M tokens (input / output)
After an 80% price cut in July 2026, Luna became the value outlier among frontier-family models: it outperforms the previous generation's top-end Opus tier on coding evals while costing about a fiftieth of Fable 5 per input token. This is the model to route bulk traffic through in a tiered architecture.
DeepSeek • DeepSeek V4
$0.22 / $0.66
/ 1M tokens (input / output)
A 284B-parameter MoE with only 13B active, which is why it runs fast and prices low. Notable for a 384K maximum output — far beyond the 128K most frontier models cap at — and for supporting both thinking and non-thinking modes, so you can switch reasoning off on easy requests rather than paying for it.
Input price
DeepSeek V4-Flash is 10% more expensive than GPT-5.6 Luna on input tokens.
Output price
DeepSeek V4-Flash is 45% cheaper than GPT-5.6 Luna on output tokens — usually the side that dominates the bill.
Monthly cost at three workload sizes
Standard (non-batch, non-cached) rates. Reasoning models will exceed these figures because thinking tokens bill as output.
| Workload | GPT-5.6 Luna | DeepSeek V4-Flash | Difference |
|---|---|---|---|
| Light — 1M in / 200K out | $0 | $0 | $0 |
| Moderate — 10M in / 2M out | $4 | $4 | $1 |
| Heavy — 100M in / 20M out | $44 | $35 | $9 |
Specification comparison
| Attribute | GPT-5.6 Luna | DeepSeek V4-Flash |
|---|---|---|
| Input (/ 1M tokens) | $0.20 | $0.22 |
| Output (/ 1M tokens) | $1.20 | $0.66 |
| Cached input | $0.02 | $0.01 |
| Context window | 1,048,576 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | 384,000 tokens |
| Native reasoning | Yes | Yes |
| Knowledge cutoff | 2026-02 | 2026-03 |
| Relative latency | low | low |
| Open weights | No | MIT |
| API model ID | gpt-5.6-luna | deepseek-v4-flash |
| Status | stable | stable |
GPT-5.6 Luna: Cut 80% from the $1/$6 launch price on 2026-07-30.
DeepSeek V4-Flash: Off-peak rate shown. Peak rates double ($0.44 / $1.32) during 01:00–04:00 and 06:00–10:00 UTC on weekdays. Superseded the earlier flat-rate pricing of $0.14 / $0.28 that many comparison sites still quote.
Choose GPT-5.6 Luna if…
- Classification and content moderation
- High-frequency agent subtasks
- Bulk summarization
- Cost-sensitive chat features
Choose DeepSeek V4-Flash if…
- High-volume classification and extraction
- Very long generated outputs
- Budget coding agents
- Local deployment on modest hardware
Frequently asked
Is GPT-5.6 Luna or DeepSeek V4-Flash cheaper?
DeepSeek V4-Flash is cheaper. On a blended 3:1 input-to-output workload it costs about 1.4x less than GPT-5.6 Luna.
Which has the larger context window, GPT-5.6 Luna or DeepSeek V4-Flash?
GPT-5.6 Luna has the larger window at 1.05M tokens versus 1M.
Should I use GPT-5.6 Luna or DeepSeek V4-Flash?
Pick GPT-5.6 Luna for high-volume work on a tight budget. Pick DeepSeek V4-Flash for the cheapest capable open-weight option. If cost dominates the decision, DeepSeek V4-Flash wins; if you need the capability ceiling, benchmark both on your own evals before committing.