# DeepSeek-V3.2 Experimental ![Organization](https://img.shields.io/badge/Org-DeepSeek-3b82f6) ![Context](https://img.shields.io/badge/Context-128K%20tokens-1f6feb) ![License](https://img.shields.io/badge/License-MIT-2da44e) ![Weights](https://img.shields.io/badge/Weights-Open-2da44e) ![Released](https://img.shields.io/badge/Released-Sep%202025-555) ![Price](https://img.shields.io/badge/Price-%240.27%2F%241.10-555) > **Quick answer:** DeepSeek-V3.2 Experimental is DeepSeek's September 2025 updated MoE model, scoring 85% MMLU-Pro, 97.1% SimpleQA, 74.1% LiveCodeBench, and 37.7% TerminalBench. Under MIT at $0.27/$1.10 per 1M tokens, it represents a significant step up in coding and factual accuracy from the baseline V3. ## At a Glance **Where DeepSeek-V3.2 Exp leads** - 97.1% SimpleQA — outstanding factual accuracy - 74.1% LiveCodeBench — top-tier coding performance for an open model - 85% MMLU-Pro — strong academic knowledge - 37.7% TerminalBench — competitive terminal/agent tasks - MIT license — fully permissive commercial use - $0.27/$1.10 per 1M tokens — same cost-efficient pricing as V3 **Where it lags** - Experimental tag: not yet the stable production release - June 2025 knowledge cutoff - Text-only **Best for:** Production coding tasks; factual Q&A requiring high accuracy; cost-efficient frontier performance; teams using DeepSeek-V3 who want improved coding/factual scores. ## What DeepSeek-V3.2 Experimental Is DeepSeek-V3.2 Experimental is an iterative update to the V3 MoE architecture released September 2025. The "Experimental" tag indicates a preview release before the stable V3.2 GA version. The model's 97.1% SimpleQA score is exceptional — representing among the highest factual accuracy scores on that benchmark. This is paired with a 74.1% LiveCodeBench score, making it one of the strongest open models for coding at V3 pricing. At $0.27/$1.10 per 1M tokens (same as V3), it offers frontier performance improvements at no additional cost increase for API users. MIT license and open weights maintain DeepSeek's commitment to open access. ## Specifications | Field | Value | |---|---| | Organization | DeepSeek | | Context window | 128,000 tokens | | License | MIT | | HuggingFace | deepseek-ai/DeepSeek-V3-2-Experimental | | Release date | September 2025 (experimental) | | Knowledge cutoff | June 2025 | | Modality | Text only | ## Pricing | Tier | Price | |---|---| | Input | $0.27 / 1M tokens | | Output | $1.10 / 1M tokens | ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [MMLU-Pro](/benchmarks/mmlu-pro) | 85% | Benchgen evaluation | 2025-09 | | [SimpleQA](/benchmarks/simpleqa) | 97.1% | Benchgen evaluation | 2025-09 | | [LiveCodeBench](/benchmarks/livecodebench) | 74.1% | Benchgen evaluation | 2025-09 | | [TerminalBench](/benchmarks/terminalbench) | 37.7% | Benchgen evaluation | 2025-09 | ## DeepSeek-V3.2 Exp vs Alternatives | Model | MMLU-Pro | LiveCodeBench | SimpleQA | Price (in/out) | |---|---|---|---|---| | **DeepSeek-V3.2 Experimental** | 85% | 74.1% | 97.1% | $0.27/$1.10 | | [DeepSeek-V3](/models/deepseek/deepseek-v3) | — | 27.2% | 24.9% | $0.27/$1.10 | | [GPT-4.1](/models/openai/gpt-4-1) | — | — | — | $2/$8 | V3.2 Experimental vs V3: +63pp LiveCodeBench improvement (74.1% vs 27.2% — V3 score likely from earlier version), near-perfect SimpleQA (97.1% vs 24.9%), all at identical pricing. A substantial upgrade for coding tasks. ## Related Models & Benchmarks - Compare: [DeepSeek-V3](/models/deepseek/deepseek-v3) · [GPT-4.1](/models/openai/gpt-4-1) · [Llama 4 Maverick](/models/meta/llama-4-maverick) - Benchmarks: [MMLU-Pro](/benchmarks/mmlu-pro) · [LiveCodeBench](/benchmarks/livecodebench) · [SimpleQA](/benchmarks/simpleqa) ## Frequently Asked Questions

What is DeepSeek-V3.2 Experimental?

DeepSeek-V3.2 Experimental is a September 2025 experimental update to V3, scoring 85% MMLU-Pro, 97.1% SimpleQA, and 74.1% LiveCodeBench at $0.27/$1.10 per 1M tokens under MIT.

Is DeepSeek-V3.2 Experimental available?

It is available as an experimental release via the DeepSeek API. The "Experimental" tag means it is a preview — not yet the stable V3.2 production release.

Should I use DeepSeek-V3.2 Exp or V3?

V3.2 Experimental for coding and factual tasks — significantly better LiveCodeBench and SimpleQA at identical pricing. Monitor for stability issues as it is an experimental release.

--- *Specs from DeepSeek's official V3.2 Experimental release (September 2025) and Benchgen evaluations. Last updated 2026-07-24.*

DeepSeek-V3.2-Exp — Evaluations

Evaluations