Model profile
DeepSeek

DeepSeek: DeepSeek V3.2 Exp

DeepSeek: DeepSeek V3.2 Exp is a budget-priced text-first model from DeepSeek with fast runtime profile, extended context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsFast latencyExtended contextBudget pricing
Intelligence
28.4

Benchmark blend

Coding
30.0

Dev workflow signal

Context
164K Tokens

Extended

Input Price
$0.28

Budget tier

Decision snapshot
54

DeepSeek: DeepSeek V3.2 Exp currently reads as a budget text-first option with extended context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
28.4
28

General reasoning and benchmark headroom.

Limited
Speed
80 tok/s
73

TTFT 0.77s

Competitive
Context
164K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.28
86

$0.42 output / 1M

Efficient

Editorial Profile

DeepSeek: DeepSeek V3.2 Exp in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 30Math score 58

DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...

Identity

DeepSeek text-first profile

Positioning

Long-context research / Agent workflows with extended context and fast runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit
  • Long-context summarization, repo analysis, and policy or document review.

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Context
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Input Price
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Context
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Intelligence
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Context
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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
28.4
MMLU Pro
83.6%
GPQA
73.8%
HLE
8.6%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
30.0
LiveCodeBench
0.554
SciCode
39.9%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
57.7
AIME 2025
57.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
43.1%
TAU2
33.9%
TerminalBench Hard
25.0%
LCR
43.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
164K Tokens
Vision
Text-first
Modalities
text
Tokenizer
DeepSeek
Max Completion
65536
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biasmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.28
Output
per 1M output tokens
$0.42
Blended
AA 3:1 mix
$0.32

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.