Claude Fable 5$22.000/MClaude Opus 5$11.000/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-5.6 Sol$12.500/MGPT-5.6 Terra$5.000/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/MClaude Fable 5$22.000/MClaude Opus 5$11.000/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-5.6 Sol$12.500/MGPT-5.6 Terra$5.000/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/M
DeepSeek
DeepSeek
EfficientLIVE INDEX

DeepSeek V3.2

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 is a efficient AI model from DeepSeek. It costs $0.260 per million input tokens and $0.380 per million output tokens (blended $0.296/M), with a 163,840-token context window.

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

INPUT
$0.260/M
per million input tokens
OUTPUT
$0.380/M
per million output tokens
BLENDED 70/30
$0.296/M

-6.3%over 61 days · hover to read

DeepSeek V3.2 — blended price

Reconstructed from 61 days of recorded rate-card changes. Prices hold flat between changes because that is what a posted price does — no value here is interpolated.

CONTEXT
163,840
tokens
What it is 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.
Graduate-level science questions, "Google-proof".
MATH90%
High-school competition math problems.
Python function synthesis from docstrings.
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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Frequently asked questions

How much does DeepSeek V3.2 cost?

DeepSeek V3.2 costs $0.260 per million input tokens and $0.380 per million output tokens, for a blended reference rate of $0.296 per million tokens.

What is DeepSeek V3.2's context window?

DeepSeek V3.2 supports up to 163,840 tokens of context in a single request.

What is DeepSeek V3.2 best for?

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

Who makes DeepSeek V3.2?

DeepSeek V3.2 is developed and served by DeepSeek.

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