GPT-5.6 Luna vs DeepSeek V4.1 Flash: API Pricing, Speed, Token Usage, and Accuracy

OpenAI lists GPT-5.6 Luna at $0.20 per million input tokens, $0.02 per million cached input tokens, and $1.20 per million output tokens. DeepSeek's current documentation says legacy deepseek-v4-flash names route to V4.1 Flash, now also available as deepseek-flash. Its off-peak rate is $0.15 per million input tokens and $0.60 per million output tokens; peak pricing is $0.30 input and $1.20 output. Cache-hit input can be as low as $0.003 per million tokens.

For 1 million input tokens and 1 million output tokens, GPT-5.6 Luna costs about $1.40. DeepSeek V4.1 Flash costs about $0.75 off-peak and $1.50 at peak. DeepSeek is cheaper when you can schedule work outside peak hours, while Luna is more predictable because it does not use time-of-day pricing in the cited rate card.

DeepSeek V4.1 Flash is the lower-cost option for high-volume batch processing, long-lived caches, and workloads that can run off-peak. Its cache-hit price is attractive when the same system prompt or document prefix is reused.

Luna can still be cheaper operationally when an application needs stable pricing, fewer retries, or shorter answers. A lower token rate does not automatically mean a lower cost per completed task. If a model needs extra calls, longer outputs, or manual correction, the rate-card saving disappears. Track prompt tokens, completion tokens, retries, tool calls, cache hits, and review time for a representative week.

Artificial Analysis reports roughly 117 output tokens per second for GPT-5.6 Luna providers and about 213 tokens per second for the DeepSeek V4 Flash 0731 release. These are directional measurements, not a guarantee for your region or provider. First-token latency, queueing, streaming behavior, and rate limits can change the experience.

DeepSeek has the stronger raw output-speed signal in that benchmark. Luna may still feel faster if its provider has better first-token latency or if it produces a shorter answer. Measure time to first token and time to a usable answer separately.

Token consumption is not the same as intelligence. Artificial Analysis has observed that the DeepSeek V4 Flash family can be more verbose than the median model on its index. There is no public apples-to-apples token-count study proving that Luna always uses fewer tokens.

Send both models the same 100 to 200 prompts and record input and output tokens, tool calls, retries, first-token time, completion time, corrections, and total cost. For coding agents, include repository edits and test passes. For support, include resolution and escalation rates. This makes token efficiency a business metric instead of a vague claim.

Artificial Analysis currently places GPT-5.6 Luna slightly ahead of DeepSeek V4 Flash 0731 on its Intelligence Index, with a reported maximum score around 38 versus 35. That suggests a modest quality advantage for Luna across the evaluated mix, but it is not a universal accuracy score.

There is no trustworthy public apples-to-apples error-rate table for these exact versions. Coding accuracy, factual accuracy, instruction following, and tool-use reliability are different things. Build a small evaluation set from real prompts and measure pass rate, unsupported claims, schema failures, and recovery after a retry. High-risk work should require citations or human review regardless of leaderboard results.

Choose DeepSeek V4.1 Flash when price and throughput dominate: batch enrichment, classification, summarization, log processing, and agent steps that can run off-peak. It is compelling when prompt caching is effective and your provider exposes the documented cache rates.

Choose GPT-5.6 Luna when you want a stable rate card and a stronger quality signal for a general-purpose production workflow. It is a sensible default for coding assistants, structured extraction, and user-facing agents where a wrong answer costs more than a small token-price difference.

A hybrid route is often best. Send routine requests to DeepSeek, then escalate uncertain, long-horizon, or user-visible failures to Luna. Store outcomes so routing improves with real data.

DeepSeek V4.1 Flash is the cheaper and faster candidate on the published figures, especially off-peak. GPT-5.6 Luna has the simpler pricing model and a small current benchmark advantage. Neither source proves a universal winner on error rate or token efficiency.

For a price-sensitive batch pipeline, start with DeepSeek and monitor peak-hour cost. For a reliability-sensitive assistant, start with Luna and compare completed-task cost. Re-run the evaluation whenever a provider changes a model, tokenizer, rate card, or routing policy.