DeepSeek V4 Pro API Cost vs V4.1 Flash Pricing [2026]

Aurora Inference currently publishes the following metered rates:

Model	Input / 1M tokens	Cached Input / 1M	Output / 1M	Context Window	Max Output
DeepSeek V4.1 Flash	$0.20	$0.006	$0.60	1,048,576	393,216
DeepSeek V4 Pro	$1.00	$0.10	$2.00	1,048,576	384,000

See Aurora Inference's full model pricing table and availability.

The biggest relative difference is cached input. That matters for applications that repeatedly send the same system prompts, repository context, documentation, tool definitions, or conversation history.

Both models support roughly one million tokens of context, so the pricing question is less about how much context they can accept and more about how much you pay to process it.

DeepSeek V4 Pro is the higher-priced model in the V4 family.

On Aurora Inference, its current rates are:

• Input: $1.00 per million tokens
• Cached input: $0.10 per million tokens
• Output: $2.00 per million tokens
• Context: 1,048,576 tokens
• Maximum output: 384,000 tokens 

The higher rate becomes relevant when the workload itself is more difficult. V4 Pro is positioned for stronger reasoning, coding, and longer-horizon agent workflows, including tasks such as full-codebase analysis and multi-step automation.

The pricing trade-off therefore comes down to whether the stronger model reduces retries, failed attempts, or additional intervention enough to offset the higher token rate.

For coding-specific performance, see our comparison of the best open-source LLMs for coding.

DeepSeek V4.1 Flash is the lower-cost option.

Aurora currently prices it at:

• Input: $0.20 per million tokens
• Cached input: $0.006 per million tokens
• Output: $0.60 per million tokens
• Context: 1,048,576 tokens
• Maximum output: 393,216 tokens (aurorainfra.ai)

That price profile makes Flash especially attractive for high-volume workloads or applications that repeatedly pass long contexts back into the model.

The difference is most pronounced for cached input. Sending one million cached tokens at Aurora's current rates costs $0.006 with V4.1 Flash versus $0.10 with V4 Pro.

For coding agents, this can be important when the same codebase, tool schemas, system instructions, or working context are reused across multiple calls.

Flash can also remain competitive on less demanding coding tasks. In our coding model comparison, V4 Flash performs well across several coding benchmarks, while V4 Pro becomes more compelling as task difficulty and agent complexity increase.

Per-million-token pricing becomes easier to compare when translated into a workload.

Consider an illustrative coding-agent task that uses:

• 40,000 total input tokens
• 30,000 tokens of repeatable context that can be cached
• 10,000 tokens of new input
• 5,000 output tokens

This is not an Aurora benchmark. It is a worked example showing how published token rates translate into task cost.

Without Cached Input

If all 40,000 input tokens are billed at the standard input rate:

Model	Input Cost	Output Cost	Total per Task
V4.1 Flash	$0.008	$0.003	$0.011
V4 Pro	$0.040	$0.010	$0.050

At that usage level, the illustrative task costs about 1.1 cents on Flash versus 5 cents on Pro.

With 30,000 Cached Input Tokens

If 30,000 of those input tokens qualify for cached pricing:

V4.1 Flash

• 10,000 fresh input tokens: $0.002
• 30,000 cached tokens: $0.00018
• 5,000 output tokens: $0.003
• Total: approximately $0.00518 per task

V4 Pro

• 10,000 fresh input tokens: $0.01
• 30,000 cached tokens: $0.003
• 5,000 output tokens: $0.01
• Total: approximately $0.023 per task

At higher volumes, the difference becomes easier to see:

Volume	V4.1 Flash	V4 Pro
1,000 tasks	~$5.18	~$23
10,000 tasks	~$51.80	~$230
100,000 tasks	~$518	~$2,300

This is why caching can matter as much as the headline input rate for repetitive workloads. For a deeper look at how to measure cost per task in production, see our guide on reducing AI costs without sacrificing quality.

Aurora's broader open-model cost breakdown looks at the same problem from a wider production perspective. Cost per completed task can include context, reasoning tokens, retries, repeated calls, and model reliability rather than treating token price as the final metric.

On a per-token basis, V4.1 Flash is clearly cheaper at Aurora's current published rates.

That does not mean it will be cheaper for every completed task.

If Flash needs multiple attempts to solve a difficult repository-level issue while Pro completes it in one, the difference in effective cost begins to narrow. More retries can mean more input tokens, more output tokens, additional tool calls, and more human review.

The reverse is also true. Using Pro for straightforward code generation, extraction, summarization, or routine transformations can mean paying for capability the task does not require.

A more useful production metric is:

Total model spend / successful completed tasks

For straightforward or high-volume coding workloads, V4.1 Flash is a logical model to test first. For longer-running agents or harder reasoning tasks, V4 Pro may justify its higher price if it improves completion rates enough to offset the additional inference cost.

The answer should come from workload-specific evals rather than token price or a single benchmark.

OpenRouter also provides access to DeepSeek models, but its pricing structure is different.

The same model may be served through multiple providers, each with its own input, output, cache-read, latency, and availability characteristics. OpenRouter pricing can therefore vary by provider, model version, routing settings, and temporary discounts.

That means searches for terms such as “DeepSeek V4 Flash OpenRouter pricing” or “DeepSeek V4 Pro OpenRouter pricing” may return different figures depending on which provider or release is being referenced.

This is important when comparing platforms. A single OpenRouter figure is best treated as a point-in-time view rather than a fixed universal rate.

Aurora Inference instead publishes per-model rates directly on the Aurora Inference pricing table, including separate pricing for fresh input, cached input, and output.

The choice between V4 Pro and V4.1 Flash depends on the workload more than the model name.

Workload	Model to Test First
High-volume coding assistance	V4.1 Flash
Straightforward code generation	V4.1 Flash
Repeated large repository context	V4.1 Flash
Latency-sensitive coding	V4.1 Flash
Difficult repository-level changes	V4 Pro
Long-running coding agents	V4 Pro
Complex multi-step tool use	V4 Pro
Mixed production workloads	Test both

(For deeper coding benchmarks specific to V4 Pro and Flash performance, see our comparison of the best open-source LLMs for coding.)

For mixed workloads, there is no requirement that every request go to the same model.

A coding product might route simple transformations or explanations to Flash while reserving Pro for jobs that require deeper reasoning or longer agent loops.

This is also one way to avoid decision paralysis when choosing between open models. The goal is not necessarily to find one universal model. It is to build a repeatable way to match the model to the task.

Aurora Inference serves DeepSeek V4.1 Flash and V4 Pro behind the same OpenAI-compatible API.

Models can be selected per call, allowing teams to route straightforward workloads to a lower-cost model while keeping more capable models available for tasks that need them. Aurora publishes token rates, cached-input rates, context windows, and maximum output limits directly on its model catalogue. 

Metered usage has no minimum commitment, and teams can move to reserved capacity when workloads become predictable. Switching models does not require rebuilding the integration.

The important metric is not the lowest token rate in isolation. It is the amount of useful work produced for the inference spend.

Compare current DeepSeek pricing on Aurora Inference.