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How Much Does DeepSeek V4 Flash Cost Compared With Other API Models?

At the published base rate, DeepSeek V4 Flash is $1 per 1M input tokens and $2 per 1M output tokens, with cache-hit input priced at $0.02 per 1M tokens. Its final billed amount depends on the assigned group multiplier, so the base rate is useful for comparing models but is not necessarily the amount on an individual account.

What does DeepSeek V4 Flash cost per million tokens?

DeepSeek V4 Flash has a published base rate of $1 per 1M input tokens, $2 per 1M output tokens, and $0.02 per 1M cache-hit input tokens. These are token rates, not a flat monthly subscription price. The final price is calculated as the base rate multiplied by the user’s assigned group multiplier.

The table below compares published base rates for selected text conversation models. It is a price comparison, not a claim that these models have equivalent capability, latency, context behavior, or output quality. Rates are in U.S. dollars per 1M tokens.

Model | Input | Output | Cache-hit input DeepSeek V4 Flash | $1 | $2 | $0.02 deepseek-v3.2 | $2 | $3 | — gpt-4.1-mini | $0.40 | $1.60 | — gpt-5-mini | $0.25 | $2 | $0.025 gemini-2.5-flash | $0.30 | $2.502 | $0.03 qwen-plus | $0.80 | $2 | —

For example, DeepSeek V4 Flash’s base price for 1M input tokens plus 1M output tokens is $3. Under the `default` group multiplier of ×0.07353, that calculation is $3 × 0.07353 = $0.22059. Under a different group, the same token usage produces a different final amount.

Is DeepSeek V4 Flash cheaper than OpenAI models?

Against the selected OpenAI models listed here, DeepSeek V4 Flash has a higher published base input rate than `gpt-4.1-mini`, `gpt-5-mini`, and `gpt-5-nano`; its $2 output rate matches `gpt-5-mini` and is higher than `gpt-4.1-mini` at $1.60. It is therefore not accurate to describe it as cheaper than every OpenAI model.

Compared with `gpt-4.1` at $2 input and $8 output, DeepSeek V4 Flash’s published base rates are lower for both input and output. Compared with `gpt-4o` at $2.50 input and $10 output, the same is true. Those comparisons only address listed token prices; they do not establish that one model can replace another for a given workload.

Output-heavy workloads deserve separate attention. At base rates, 1M input tokens and 4M output tokens cost $9 with DeepSeek V4 Flash: $1 for input plus $8 for output. A model with a lower input rate can still cost more or less overall depending on the input-to-output ratio, so compare the full request mix rather than input price alone.

How do I calculate a monthly DeepSeek V4 Flash API bill?

Calculate the monthly base cost as: (input tokens ÷ 1,000,000 × $1) + (output tokens ÷ 1,000,000 × $2) + (cache-hit input tokens ÷ 1,000,000 × $0.02). Then multiply that result by the group multiplier assigned to the account.

For a reusable planning formula, let I be monthly input tokens, O be monthly output tokens, C be monthly cache-hit input tokens, and M be the group multiplier. The estimated bill is ((I ÷ 1,000,000 × 1) + (O ÷ 1,000,000 × 2) + (C ÷ 1,000,000 × 0.02)) × M dollars. Your monthly token volume is Not yet measured until you inspect production usage.

A normalized cost check is still useful before usage data exists. At the base rate, 1M input tokens costs $1, 1M output tokens costs $2, and 1M cache-hit input tokens costs $0.02. With the `default` multiplier of ×0.07353, those three amounts become $0.07353, $0.14706, and $0.0014706 respectively.

Do not project a monthly spend from request count alone. Prompt length, generated output length, repeated context, and the assigned multiplier all change the result. Measure input, output, and cache-hit token categories separately before treating an estimate as a budget.

What do I give up by choosing DeepSeek V4 Flash for cost?

DeepSeek V4 Flash is described as a lightweight DeepSeek V4 variant that prioritizes cost-effectiveness and high-throughput efficiency for general conversation and basic text tasks. It supports a 1M-token long context. That makes its published positioning relevant to high-volume general text workloads, but it is not evidence that it fits every coding, reasoning, or agent workflow.

A lower token rate does not measure response speed. No latency figure, throughput measurement, availability figure, benchmark score, or quality comparison for this deployment is provided here, so each is Not yet measured. Test the prompts, tools, output formats, and failure cases that matter to your application.

Context can affect cost even when a model’s input rate is low. Sending a long conversation or repeated document context on every turn increases billable input. The cache-hit rate is far below the standard input rate, but the supplied data does not define cache eligibility or cache behavior. Treat cache savings as something to verify in usage records rather than as a guaranteed reduction.

How can I reduce DeepSeek V4 Flash API spending?

First, measure and reduce repeated input. Separate stable instructions, repeated documents, and conversation history from task-specific content, then monitor whether repeated input is billed at the published $0.02 per 1M cache-hit rate rather than the $1 standard input rate. Cache eligibility and hit rate are Not yet measured, so validate this with actual billing data.

Second, select a model using the workload’s required behavior, not only its headline input price. DeepSeek V4 Flash is positioned for general conversation and basic text tasks. For each route, compare a small representative evaluation set, token consumption, and output acceptance rate. Performance results for a specific workload are Not yet measured until tested.

Third, batch only work that can tolerate delayed processing and only after confirming the available request behavior. The supplied pricing data does not state a batch discount, batch endpoint, or queueing policy, so do not assume batching changes the token rate. Its practical value may instead come from consolidating operational work, reducing duplicated prompts, and controlling when non-interactive jobs run.

When can DeepSeek V4 Flash pricing change, and where should I verify it?

Verify current pricing against the platform pricing endpoint at https://api.openlux.ai/api/pricing. The figures on this page were pulled from that endpoint on August 4, 2026, at 16:16:08 UTC. They are a snapshot, so a later endpoint response should take precedence over this page.

The listed rate is a base rate, and the final amount depends on a group multiplier. For `default`, the published multiplier is ×0.07353. Other groups have different multipliers, including `huawei-deepseek` at ×0.05736 and `Anthropic-Claude-1` at ×1.17647. Confirm the group assigned to your account before calculating final spend.

The pricing source reports 452 models for sale, while the provided list covers the 150 models with the highest call volume and leaves 302 models unlisted. This comparison is therefore not a complete catalog and does not indicate that only the displayed models are supported. The supplied pricing data also does not specify a free allowance, payment method, or purchase flow; those details are Not yet measured here.

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Last updated 2026-08-05　|　Written and maintained by OpenLux.
Latency and pricing figures come from our own measurements. Where they differ from the vendor's site, the vendor's live page wins.