Does DeepSeek V3.2 handle images?

No. DeepSeek V3.2 is text in, text out. Anything involving images, audio or video needs a separate multimodal model alongside it.

What is DeepSeek V3.2 best used for?

Agentic tool use and reasoning tasks that run at high volume. DeepSeek positions it around balancing efficiency with reasoning and tool-use performance, which makes it a fit for agent loops, evaluation runs and bulk text processing where cost per call compounds.

How much text can DeepSeek V3.2 take at once?

The context window comfortably holds a large document set or a long agent trajectory in a single pass, and the output ceiling supports long structured responses without splitting the job. Exact figures are in the specification tiles.

DeepSeek model°

DeepSeek V3.2

DeepSeek V3.2 is a text-only large language model from DeepSeek, built to hold up on reasoning and agentic tool use while staying cheap to run at volume. It is the open-weights option teams reach for when the work is high-throughput and the budget is per-token.

DeepSeekVerified 24/09/2026Released 01/12/2025

01 / decision

Decision snapshot

Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 126 models we track, not against an absolute standard.

Capability

Not measured

No published benchmark scores yet

Input / 1M tokens

$0.27

field median $0.30

Output / 1M tokens

$0.40

field median $1.25

Context

164K

field median 524K

02 / overview

What DeepSeek V3.2 is, and when to reach for it

Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.

What it is

The job it was built for, and the job it is not for.

DeepSeek V3.2 is a general-purpose text model whose stated design goal is balancing computational efficiency against reasoning and tool-use quality, using a fine-grained sparse attention mechanism DeepSeek calls DSA. The job it was built for is agentic work, chains of tool calls, long document reasoning, tasks where a model runs many times rather than once. It is not a multimodal model, so anything involving images, audio or video needs a different model in the stack.

When it arrived, and when to use it

Where it sits in its line, and when a sibling is the better pick.

DeepSeek V3.2 turned up at the start of December 2025 as the current head of the DeepSeek V3 line. It is the right pick when cost per call is the constraint and the workload is text reasoning or tool orchestration that runs at scale. It is the wrong pick when you need vision or audio in the same model, or when a task is rare enough that the price difference against a frontier model never shows up on the bill.

How you reach it

The API, the apps it powers, and what its limits let you do.

DeepSeek V3.2 is reached through the DeepSeek API and through the usual third-party inference hosts, in a standard chat completions shape with tool calling. The context window takes a substantial document set or a long agent trajectory in one pass, and the output ceiling is generous enough for long structured responses, full file rewrites or extended reasoning traces without chunking. Everything in and out is text.

Why it matters

What changes because this exists, or why it does not.

What changes is the arithmetic on agentic systems. Loops that call a model dozens of times per task, evaluation harnesses, classification over large corpora, background enrichment jobs, all of these were awkward to justify at frontier pricing and are straightforward here. For teams already running DeepSeek, it is an incremental step rather than a new category.

Follows DeepSeek V3.2 Exp, and is the newest in its line. See the whole line.

03 / evidence

How much of this is verified

Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.

No published benchmark scores for this model yet.

We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.

How we decide what counts as evidence

04 / ledger

Benchmark ledger

Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.

Nothing in the ledger yet.

Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.

How we decide what counts as evidence

05 / capability

Capability shape

Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.

No category scores to shape yet.

A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.

How we decide what counts as evidence

06 / cost

What it costs

List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.

DeepSeek V3.2 API pricingSurge45°
Charge	Price	Unit	Read on	Source
Input	$0.27	per 1M tokens	2026-09-24	Check
Output	$0.40	per 1M tokens	2026-09-24	Check

DeepSeek V3.2 sits at the low end of the market on both input and output, cheap enough that the cost of a single call stops being a design consideration. Against the frontier models it competes with on reasoning tasks, the gap is large enough to change what you are willing to build, particularly anything that loops.

What a month costsSurge45°
Workload	Input / month	Output / month	Cost
A small product team	20M tokens	5M tokens	$7.38
A busy support assistant	200M tokens	40M tokens	$69.80
A document pipeline	1000M tokens	100M tokens	$309.00

List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.

07 / specs

Specifications

As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.

SpecificationSurge45°
Context window	163,840 tokens
Maximum output	65,536 tokens
Modalities	text
Released	01/12/2025
Status	Current
Catalogue identifier	deepseek/deepseek-v3.2

08 / lineage

Lineage

What this model replaced, what replaced it, and what else its provider has in the field.

Also from DeepSeek

09 / line

The line

Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.

Came before

← DeepSeek V3.2 Exp

Came after

Nothing newer in this line yet.

01DeepSeek V3 032424/03/2025
02DeepSeek V3.121/08/2025
03DeepSeek V3.1 Terminus22/09/2025
04DeepSeek V3.2 Exp29/09/2025
05DeepSeek V3.201/12/2025

Ordered by release date and worked out from the naming, so a new member slots in as soon as its page exists. A retired model keeps its page and its place in the line.

10 / notes

Our notes

What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.

What it changes for you

Cheap capable models get embedded in the plumbing of other products, so your brand is increasingly being summarised by a model nobody chose deliberately. That raises the value of source material that is unambiguous on its own, clear product naming, plain statements of what you do and who for, facts that survive being read once without context. If your positioning only makes sense to a reader who already knows the category, a model running at this price point in someone else's agent loop will not work it out.

Where buyers meet this model

Buyers meet DeepSeek V3.2 in the DeepSeek chat app and through the API, directly or via the inference providers and routers that host open-weights models. It also shows up underneath other people's products, in coding assistants, research tools and agent frameworks that pick a cheap reasoning model for the high-volume steps and rarely name it in the interface.

Change log

Nothing published here yet. Changes appear within hours of a provider announcing them.

11 / questions

Questions

The things people ask about this model, answered from what is on this page rather than from anywhere else.

Does DeepSeek V3.2 handle images?: No. DeepSeek V3.2 is text in, text out. Anything involving images, audio or video needs a separate multimodal model alongside it.
What is DeepSeek V3.2 best used for?: Agentic tool use and reasoning tasks that run at high volume. DeepSeek positions it around balancing efficiency with reasoning and tool-use performance, which makes it a fit for agent loops, evaluation runs and bulk text processing where cost per call compounds.
How much text can DeepSeek V3.2 take at once?: The context window comfortably holds a large document set or a long agent trajectory in a single pass, and the output ceiling supports long structured responses without splitting the job. Exact figures are in the specification tiles.

Surge45°

Is DeepSeek V3.2 recommending you?

Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.

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