Compare the listed model data for DeepSeek-V3.1 by DeepSeek and DeepSeek-V3.2 Exp by DeepSeek. Benchmark and pricing records are not a controlled test and may change.
Data last updated: 2026-10-16. Verify current model details with each provider.
What the listed data says
DeepSeek-V3.1: 128K tokens; DeepSeek-V3.2 Exp: 128K tokens. The listed context-window values are equal. DeepSeek-V3.1 is listed for no specific capability flags; DeepSeek-V3.2 Exp is listed for no specific capability flags. Shared catalog benchmark entries: MMLU: DeepSeek-V3.1 91.7%; DeepSeek-V3.2 Exp 89.5%; HumanEval: DeepSeek-V3.1 93.8%; DeepSeek-V3.2 Exp 91.2%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
Catalog source links
Use these model and provider links to verify current specifications, pricing, and availability.
The listed context windows are 128K tokens and 128K tokens; the larger is 1× the smaller.
On shared numeric benchmark entries, MMLU: DeepSeek-V3.1 − DeepSeek-V3.2 Exp = +2.2 percentage points (91.7% vs 89.5%); HumanEval: DeepSeek-V3.1 − DeepSeek-V3.2 Exp = +2.6 percentage points (93.8% vs 91.2%).
A release-date gap cannot be calculated from the catalog values (DeepSeek-V3.1: June 2025; DeepSeek-V3.2 Exp: 2025).
Cost example: 1M input and 1M output tokens
A cost example for 1M input plus 1M output tokens cannot be calculated reliably because separate numeric input and output rates are not listed for both models.
Choose DeepSeek-V3.1 if…
Choose DeepSeek-V3.1 when your task aligns with catalog-listed uses or strengths such as Enterprise software development requiring high quality at low latency, Document intelligence and knowledge base applications, API providers serving general business customers, DeepSeek's most capable non-reasoning model as of June 2025.
Choose DeepSeek-V3.2 Exp if…
Choose DeepSeek-V3.2 Exp when your task aligns with catalog-listed uses or strengths such as AI research teams studying next-generation DeepSeek architectures, Experimental next-gen DeepSeek architecture, MIT license for research access.
Feature Comparison — DeepSeek-V3.1 vs DeepSeek-V3.2 Exp
Catalog specifications for DeepSeek-V3.1 and DeepSeek-V3.2 Exp
Specification
DeepSeek-V3.1
DeepSeek-V3.2 Exp
Provider
DeepSeek
DeepSeek
Parameters
671B total (37B active via MoE)
Experimental
Context window
128K tokens
128K tokens
Maximum output
Not listed
Not listed
Catalog pricing
MIT open source. DeepSeek API: ~$0.27/MTok input, ~$1.10/MTok output
MIT — free for research
Listed input/output cost per 1M tokens
Separate numeric input/output rates not listed
Separate numeric input/output rates not listed
License
MIT
MIT
Coding support
Not listed
Not listed
Reasoning support
Not listed
Not listed
Vision input
Not listed
Not listed
Multimodal
Not listed
Not listed
Open weights
Yes
Yes
Benchmark: MMLU
91.7%
89.5%
Benchmark: HumanEval
93.8%
91.2%
Figures are transcribed from LITHOVEX model records. Benchmark versions and evaluation setups may differ; pricing and specifications can change. Check each provider's current documentation before making a decision.
Frequently Asked Questions
What is different between DeepSeek-V3.1 and DeepSeek-V3.2 Exp?
DeepSeek-V3.1: 128K tokens; DeepSeek-V3.2 Exp: 128K tokens. The listed context-window values are equal. DeepSeek-V3.1 is listed for no specific capability flags; DeepSeek-V3.2 Exp is listed for no specific capability flags. Shared catalog benchmark entries: MMLU: DeepSeek-V3.1 91.7%; DeepSeek-V3.2 Exp 89.5%; HumanEval: DeepSeek-V3.1 93.8%; DeepSeek-V3.2 Exp 91.2%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
What benchmark values are listed for DeepSeek-V3.1 and DeepSeek-V3.2 Exp?
MMLU: DeepSeek-V3.1 91.7%; DeepSeek-V3.2 Exp 89.5%; HumanEval: DeepSeek-V3.1 93.8%; DeepSeek-V3.2 Exp 91.2%. The catalog values may use different versions and evaluation setups, so they are reference points rather than a controlled head-to-head result.
Which model has the larger listed context window?
DeepSeek-V3.1: 128K tokens; DeepSeek-V3.2 Exp: 128K tokens. The listed context-window values are equal.
What are the listed costs per million tokens for DeepSeek-V3.1 and DeepSeek-V3.2 Exp?
DeepSeek-V3.1: Separate numeric input/output rates not listed (MIT open source. DeepSeek API: ~$0.27/MTok input, ~$1.10/MTok output). DeepSeek-V3.2 Exp: Separate numeric input/output rates not listed (MIT — free for research). Where separate numeric input and output rates are available, the worked example uses 1M input plus 1M output tokens. Confirm current terms with each provider.
How far apart are the listed release dates for DeepSeek-V3.1 and DeepSeek-V3.2 Exp?
The catalog lists DeepSeek-V3.1 as June 2025 and DeepSeek-V3.2 Exp as 2025; a release-date gap cannot be calculated from both records.
When was the comparison data last updated?
The model catalog records 2026-10-16 as its data update date. Provider specifications and prices may have changed since then.
How should I choose between DeepSeek-V3.1 and DeepSeek-V3.2 Exp?
Compare the listed specifications and capabilities, review the benchmark notes, then test both with the same representative prompt for your task. This page does not claim a universal winner.
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