Claude Fable 5$22.000/MClaude Opus 4.8$11.000/MClaude Opus 4.7$11.000/MClaude Opus 4.6$11.000/MClaude Opus 4.5$33.000/MClaude Sonnet 3.7$6.600/MClaude Opus 3$33.000/MClaude 2.1$12.800/MClaude 2$12.800/MGPT-5.5$12.500/MGPT-5.2$5.425/MGPT-5.2-Codex$5.425/MGPT-5$3.875/MGPT-4.5$97.500/MGPT-4 Turbo Preview$16.000/MGPT-4$39.000/MGPT-4-32k$78.000/Mo3$19.000/Mo3-mini$2.090/Mo4-mini$2.090/Mo1$28.500/Mo1-mini$5.700/Mo1-preview$28.500/MGemini 3.5 Pro$5.000/MGemini 3.1 Pro$5.000/MGemini 3 Pro$5.000/MGemini 2.5 Pro$3.875/MGemini 1.5 Pro$2.375/MGemini 1.0 Ultra$12.000/MGemini 1.0 Pro$0.800/MClaude Fable 5$22.000/MClaude Opus 4.8$11.000/MClaude Opus 4.7$11.000/MClaude Opus 4.6$11.000/MClaude Opus 4.5$33.000/MClaude Sonnet 3.7$6.600/MClaude Opus 3$33.000/MClaude 2.1$12.800/MClaude 2$12.800/MGPT-5.5$12.500/MGPT-5.2$5.425/MGPT-5.2-Codex$5.425/MGPT-5$3.875/MGPT-4.5$97.500/MGPT-4 Turbo Preview$16.000/MGPT-4$39.000/MGPT-4-32k$78.000/Mo3$19.000/Mo3-mini$2.090/Mo4-mini$2.090/Mo1$28.500/Mo1-mini$5.700/Mo1-preview$28.500/MGemini 3.5 Pro$5.000/MGemini 3.1 Pro$5.000/MGemini 3 Pro$5.000/MGemini 2.5 Pro$3.875/MGemini 1.5 Pro$2.375/MGemini 1.0 Ultra$12.000/MGemini 1.0 Pro$0.800/M
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DeepSeek V3.2 Exp

text->text

Open-weights frontier model that matched GPT-4-class quality at a fraction of the inference cost. Sparked the late-2024 cost reset.

DeepSeek V3.2 Exp is a efficient AI model from DeepSeek. It costs $0.270 per million input tokens and $0.410 per million output tokens (blended $0.312/M), with a 164K-token context window.

Profile inherited from upstream DeepSeek-V3 ↗ — this is a hosted variant of the same open-weights model.

Modalities textOfficial model page ↗Provider pricing ↗API docs ↗Pricing source ↗Compare with another model →Estimate monthly cost →
INPUT
$0.270/M
per million input tokens
OUTPUT
$0.410/M
per million output tokens
BLENDED 70/30
$0.312/M
default reference rate · how it's calculated →
CONTEXT
164K
163,840 tokens
What it's good at
  • Frontier quality, open weights
  • Aggressive pricing
  • Strong code & math
  • MoE architecture
Typical use cases
  • Self-hosted frontier inference
  • Cost-sensitive chat
  • Code generation
Benchmarks
vs. best public score
Scores inherited from DeepSeek-V3 — this is a hosted variant of the same open-weights model, so the underlying benchmark scores are identical.
MMLU88%
Multitask academic knowledge across 57 subjects.
GPQA Diamond59%
Graduate-level science questions, "Google-proof".
MATH90%
High-school competition math problems.
HumanEval89%
Python function synthesis from docstrings.
SWE-bench Verified42%
Real GitHub issues solved end-to-end.
LMArena Elo1318 Elo
Crowd-sourced head-to-head preference Elo rating.
Hand-curated from each provider's published reports and public leaderboards. Methodology varies across sources — treat as directional rather than authoritative.
More from DeepSeek
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DeepSeek-V4-Pro
Frontier · 1M ctx
in $1.740/Mout $3.480/M
DeepSeek-V4-Flash
Efficient · 1M ctx
in $0.140/Mout $0.280/M
DeepSeek-V3
Frontier · 64K ctx
in $0.270/Mout $1.100/M
DeepSeek-V3-0324
Frontier · 128K ctx
in $0.270/Mout $1.100/M
DeepSeek-R1
Reasoning · 64K ctx
in $0.550/Mout $2.190/M
DeepSeek-R1-0528
Reasoning · 64K ctx
in $0.550/Mout $2.190/M
Frequently asked questions

How much does DeepSeek V3.2 Exp cost?

DeepSeek V3.2 Exp costs $0.270 per million input tokens and $0.410 per million output tokens, for a blended reference rate of $0.312 per million tokens.

What is DeepSeek V3.2 Exp's context window?

DeepSeek V3.2 Exp supports up to 164K tokens of context (163,840 tokens).

What is DeepSeek V3.2 Exp best for?

DeepSeek V3.2 Exp is well suited to Frontier quality, open weights, Aggressive pricing and Strong code & math.

Who makes DeepSeek V3.2 Exp?

DeepSeek V3.2 Exp is developed and served by DeepSeek.