DeepSeek vs OpenAI API Cost: Cheap Tokens vs Total Task Cost

DeepSeek built its reputation on a simple headline: capable models at startlingly cheap token prices. That headline is now incomplete. In September 2026, DeepSeek prices the same request differently by model, cache status and time of day, while OpenAI's new GPT-6 Luna is cheaper than DeepSeek Flash on the four token-identical workloads we tested.

That does not prove Luna is the better model, or that DeepSeek is expensive. DeepSeek V4 Pro remains far cheaper per token than GPT-6 Sol. It proves something more useful: provider comparisons should be made on the cost of a successful task, not a screenshot of one output-token rate.

This guide uses the providers' official September 26 rate cards, four reproducible workload shapes and a retry test. No model is treated as quality-equivalent without an evaluation.

The current rate cardThe old DeepSeek comparison is obsolete

DeepSeek says off-peak is half price. Peak hours are 01:00–04:00 and 06:00–10:00 UTC, Monday through Friday, excluding Chinese public holidays; every other hour is off-peak. A queue that can move a nightly batch outside those windows cuts its DeepSeek token bill in half without changing models.

OpenAI's Standard price does not change with the clock. Its short-context GPT-6 rates are $0.10/$0.50 for Luna and $2/$10 for Sol, plus separate cached-input and cache-write lines. OpenAI also offers Batch and Flex discounts and Fast mode, but we exclude those here so the base comparison stays readable. Our OpenAI API pricing guide explains those tiers in detail.

Four workloadsLuna beats Flash on identical token counts

We priced four shapes, not four cherry-picked prompts: 100,000 short extractions, 50,000 support turns, 5,000 document analyses and 2,000 agent runs. The middle three include reusable cached context where that is realistic.

The surprising row is Flash. At off-peak prices it costs 21%–38% more than Luna for these token-identical workloads; at peak it costs 2.42–2.76× more. The reason is not hidden: Luna's $0.10 fresh input and $0.50 output are below Flash's $0.15/$0.60 even in DeepSeek's cheaper window.

The Pro/Sol comparison points the other way. V4 Pro off-peak costs roughly one-quarter of Sol on these shapes; even at peak it costs 45%–53% less. But “Pro” and “Sol” are product labels, not a statement that they solve the same percentage of your tasks. The table is a rate-card result, not an intelligence leaderboard.

Cheap tokens are not a cheap task

A provider can win the first formula and lose the second. If a model needs more context, writes longer reasoning traces, retries a tool call or fails a JSON schema often enough to require a second pass, the task absorbs those tokens again. If a human must repair the answer, token price may become the smallest line.

For the agent workload above, one GPT-6 Luna run costs about $0.0073 and one peak DeepSeek Flash run $0.0176. Three Flash attempts cost $0.0527—more than seven Luna attempts at the original shape. Two peak V4 Pro attempts cost $0.136, close to one $0.145 GPT-6 Sol attempt. That does not predict which one succeeds; it identifies the exact quality threshold your evaluation must measure.

CachingDeepSeek rewards repeated prefixes more aggressively

Both providers discount repeated prompt prefixes, but the mechanisms and ratios differ. DeepSeek's disk cache is automatic and best-effort. Its documentation reports prompt_cache_hit_tokens and prompt_cache_miss_tokens; matching requires an already persisted prefix unit, and entries may survive from hours to days. DeepSeek does not promise every eligible prefix will hit.

Flash's off-peak cache-hit rate is $0.003 per million tokens—98% below its $0.15 miss rate. V4 Pro's $0.022 is 96.7% below $0.66. OpenAI's GPT-6 cached input is one-tenth of fresh input, while a new cache write is 1.25× fresh input. A long, stable repository or policy manual can therefore tilt toward DeepSeek even if a short, mostly fresh request does not.

Cache rate alone is not enough. Log the hit tokens returned by the provider, divide them by all input tokens and price the measured mixture. The AI Cost Calculator lets you enter that mix instead of choosing “cached” as a yes/no setting.

Tools, retries and latencyThe missing rows in token calculators

An agent does more than generate text. OpenAI lists web search at $10 per 1,000 calls plus search-content tokens, file search at $2.50 per 1,000 calls plus storage, and hosted containers from $0.03 per 20-minute session. DeepSeek exposes tool calling but your own search, browser, sandbox or MCP provider may bill separately. A cheap reasoning model that makes six searches can cost more than a dearer model that needs one.

Latency also has a price when it blocks a worker or a customer. DeepSeek publishes account concurrency limits of 2,500 for Flash and 500 for V4 Pro, but not a fixed latency guarantee on the price page. OpenAI sells faster processing at higher token rates. Neither fact tells you the queue time for your traffic pattern; load-test the region, hour and request size you will actually use.

For every evaluation row, record:

A fair 7-day bake-off

Do not send 50 random prompts to both models and average a vibe score. Build a small production-shaped evaluation.

1. Sample 100–500 real tasks after removing secrets and personally identifying data.
2. Freeze the system prompt, tools, output schema and retry policy per model; provider-specific prompting is allowed, but document it.
3. Define acceptance before running: tests pass, fields match, citations resolve or reviewers agree.
4. Route the same task set to each candidate across both DeepSeek pricing windows.
5. Record all usage, tool calls, latency, failures, retries and review minutes.
6. Calculate cost per accepted result and cost at your expected monthly volume.
7. Keep a fallback route. The cheapest default plus a stronger exception model often beats one model for everything.

If the workload is an agent, use the AI Agent Cost Calculator because context grows across steps. A flat “tokens per call” estimate misses that compounding.

VerdictDeepSeek is a candidate, not an automatic bargain

Choose GPT-6 Luna as the first cost baseline for short, focused, high-volume tasks. On current published rates it beats DeepSeek Flash even during Flash's off-peak window for identical token shapes. Then test whether its acceptance rate is sufficient.

Choose DeepSeek Flash when its measured quality, extreme cache discount, high account concurrency or OpenAI-compatible deployment path wins in your workload. Schedule batchable traffic off-peak and verify actual cache hits.

Choose DeepSeek V4 Pro when you need the stronger DeepSeek tier and can exploit its much lower per-token cost than GPT-6 Sol. Choose GPT-6 Sol when its quality, tool behavior, latency or operational fit produces enough more accepted tasks to pay the premium.

The honest winner is the lowest total cost per accepted task under your own test—not the company with the most memorable launch price.

Frequently asked questions

Is DeepSeek cheaper than OpenAI in 2026?

It depends on the model pair and workload. GPT-6 Luna's current fresh-input and output prices are below DeepSeek Flash even off-peak. DeepSeek V4 Pro is far cheaper per token than GPT-6 Sol. Quality and retry rates can reverse either rate-card result.

When is DeepSeek API off-peak?

DeepSeek says peak hours are 01:00–04:00 and 06:00–10:00 UTC Monday–Friday, excluding Chinese public holidays. All other hours, weekends and Chinese public holidays are off-peak at half the peak rate.

Does DeepSeek charge for cached input?

Yes, at a steep discount. Current off-peak rates are $0.003 per million cache-hit tokens for Flash and $0.022 for V4 Pro. The cache is automatic and best-effort.

Why compare Luna with Flash and Sol with Pro?

They are useful low-cost and stronger-tier shopping comparisons, not claims of capability equivalence. A fair selection requires the same production evaluation and an acceptance-rate measurement.

What is the best metric for comparing AI API cost?

Total cost per accepted task: all model tokens, tools, retries, fallback requests and human validation divided by outputs that pass a predefined test.

Sources and methodology

DeepSeek model names, context, token rates, time windows, concurrency and caching behavior were checked on September 26, 2026 against the official DeepSeek Models & Pricing, Context Caching and Rate Limit & Isolation pages. OpenAI rates and tool fees were checked against the official OpenAI API Pricing and model catalog.

The four workloads are illustrative shapes, not measured provider benchmarks. The arithmetic is reproduced in drafts/deepseek-vs-openai-api-cost-numbers.mjs. Token counts are held equal specifically to isolate rate-card differences; the article then explains why production token counts and acceptance rates rarely remain equal.

CalculatorAI is not affiliated with or endorsed by DeepSeek or OpenAI. Product availability and prices can change. Verify the linked provider pages before committing spend.