DeepSeek · Open weights
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp is an experimental V3-series model with improved long-context efficiency compared with earlier V3 releases. FP8.
- Input
- Text
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.28
- Output
- $0.42
Reference prices as published by the model provider. API example
Specifications
- Context window
- 163.8K
- Max output
- 65.5K
- Average output (7 days)
- 716 tokens
- Longer than 62% of 468 models
Side by side
DeepSeek V3.2 Exp compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| DeepSeek V3.2 ExpDeepSeekThis model | DeepSeek V4.1 Flash 1017 ThinkingDeepSeekRemove | |
|---|---|---|
| Provider | DeepSeek | DeepSeek |
| Discovery tier | Open weights | Open weights |
| Added | Jan 1, 2024 | Oct 17, 2026 |
| Limits | ||
| Context window | 163.84K | 1.05MBest |
| Max output | 65.54K | 393.22KBest |
| Average output | 716 tokens | 1.2K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.28 | $0.10Best |
| Output | $0.42Best | $0.60 |
| Cache read | — | $0.003 |
| Performance | ||
| Throughput | 17.9 tok/s | 255.6 tok/sBest |
| Time to first token | 3.7 s | 2.4 sBest |
| Request success (24h) | 99.8% | 99.8% |
| Benchmark indexes | ||
| Intelligence Index | 13.9 | 45.1Best |
| Math Index | 57.7 | — |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | No | Yes |
| Vision | No | Yes |
| Structured output | No | Yes |
| Reasoning effort | — | none, low, high, max |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- DeepSeek V3.2 Exp
- DeepSeek V4.1 Flash 1017 Thinking
Intelligence Index
- DeepSeek V3.2 Exp13.9
- DeepSeek V4.1 Flash 1017 Thinking45.1
DeepSeek V3.2 Exp: Better than 34% of 326 scored models
Indexes
Math Index
Composite of the math evaluations
- DeepSeek V3.2 Exp: 57.7
- DeepSeek V4.1 Flash 1017 Thinking: —
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- DeepSeek V3.2 Exp: —
- DeepSeek V4.1 Flash 1017 Thinking: 66.6%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- DeepSeek V3.2 Exp: —
- DeepSeek V4.1 Flash 1017 Thinking: 36.7%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- DeepSeek V3.2 Exp: —
- DeepSeek V4.1 Flash 1017 Thinking: 1,570 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- DeepSeek V3.2 Exp: —
- DeepSeek V4.1 Flash 1017 Thinking: 1,746 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- DeepSeek V3.2 Exp: —
- DeepSeek V4.1 Flash 1017 Thinking: 17.2%
AA-LCRv1.1
Reasoning across long documents
- DeepSeek V3.2 Exp: 44.7%
- DeepSeek V4.1 Flash 1017 Thinking: 82.7%Top
MLCR-AA
Long-context reasoning over medical records
- DeepSeek V3.2 Exp: —
- DeepSeek V4.1 Flash 1017 Thinking: 26.7%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- DeepSeek V3.2 Exp: 9.0%
- DeepSeek V4.1 Flash 1017 Thinking: 43.3%Top
GPQA Diamond
Graduate-level science questions
- DeepSeek V3.2 Exp: 73.8%
- DeepSeek V4.1 Flash 1017 Thinking: —
MMLU-Pro
Multi-subject knowledge and reasoning
- DeepSeek V3.2 Exp: 83.6%
- DeepSeek V4.1 Flash 1017 Thinking: —
IFBench
Following precise output instructions
- DeepSeek V3.2 Exp: 43.1%
- DeepSeek V4.1 Flash 1017 Thinking: —
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- DeepSeek V3.2 Exp: —
- DeepSeek V4.1 Flash 1017 Thinking: 33.8%
SciCode
Scientific computing in Python
- DeepSeek V3.2 Exp: —
- DeepSeek V4.1 Flash 1017 Thinking: 55.9%
LiveCodeBench
Recent competitive programming problems
- DeepSeek V3.2 Exp: 55.4%
- DeepSeek V4.1 Flash 1017 Thinking: —
Math
AIME 2025
Competition mathematics, 2025 problems
- DeepSeek V3.2 Exp: 57.7%
- DeepSeek V4.1 Flash 1017 Thinking: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- DeepSeek V3.2 Exp: 25.0%
- DeepSeek V4.1 Flash 1017 Thinking: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- DeepSeek V3.2 Exp: 33.9%
- DeepSeek V4.1 Flash 1017 Thinking: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- DeepSeek V3.2 Exp: 44.7%
- DeepSeek V4.1 Flash 1017 Thinking: 82.7%Top
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Throughput
- 17.9 tok/s
- Faster than 9% of 261 models
- Time to first token
- 3.7 s
- Quicker than 47% of 261 models
- Prompt cache hits
- 58.2%
- Request success (24h)
- 99.8%
- Reliable
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-ai/deepseek-v3.2-exp",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="deepseek-ai/deepseek-v3.2-exp",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)DeepSeek
Related models
Other DeepSeek models, starting with the same family.