DeepSeek · Open weights
DeepSeek V4 Pro (Thinking)
DeepSeek V4 Pro Thinking enables DeepSeek's chain-of-thought mode on the large-scale Mixture-of-Experts model with a 1M-token context window, built for advanced reasoning, coding, long-horizon agent workflows, knowledge, math, and software engineering tasks.
- Reasoning
- Tools
- Structured output
- Input
- Text
- Output
- Text
- Reasoning effort
- none · high · max
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.70
- Output
- $1.40
- Cache read
- $0.07
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- 384K
- Average output (7 days)
- 1.6K tokens
- Longer than 88% of 468 models
Side by side
DeepSeek V4 Pro (Thinking) compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| DeepSeek V4 Pro (Thinking)DeepSeekThis model | DeepSeek V4 Pro 0813 ThinkingDeepSeekRemove | |
|---|---|---|
| Provider | DeepSeek | DeepSeek |
| Discovery tier | Open weights | Open weights |
| Added | Apr 24, 2026 | Aug 12, 2026 |
| Limits | ||
| Context window | 1.05M | 1.05M |
| Max output | 384K | 384K |
| Average output | 1.6K tokens | 2.4K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.70 | $0.13Best |
| Output | $1.40 | $0.40Best |
| Cache read | $0.07 | $0.0042Best |
| Performance | ||
| Throughput | 47.1 tok/s | 73.1 tok/sBest |
| Time to first token | 2.5 sBest | 3.1 s |
| Request success (24h) | 99.9%Best | 99.7% |
| Benchmark indexes | ||
| Intelligence Index | 36.0 | 36.0 |
| Coding Index | 68.8 | 68.8 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Structured output | Yes | Yes |
| Reasoning effort | none, high, max | none, low, high, max |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- DeepSeek V4 Pro (Thinking)
- DeepSeek V4 Pro 0813 Thinking
Intelligence Index
- DeepSeek V4 Pro (Thinking)36.0
- DeepSeek V4 Pro 0813 Thinking36.0
DeepSeek V4 Pro (Thinking) tested at max reasoning effort
DeepSeek V4 Pro (Thinking): Better than 79% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- DeepSeek V4 Pro (Thinking): 68.8Top
- DeepSeek V4 Pro 0813 Thinking: 68.8Top
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- DeepSeek V4 Pro (Thinking): 56.7%Top
- DeepSeek V4 Pro 0813 Thinking: 56.7%Top
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- DeepSeek V4 Pro (Thinking): 25.9%Top
- DeepSeek V4 Pro 0813 Thinking: 25.9%Top
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- DeepSeek V4 Pro (Thinking): 1,256 EloTop
- DeepSeek V4 Pro 0813 Thinking: 1,256 EloTop
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- DeepSeek V4 Pro (Thinking): 1,449 EloTop
- DeepSeek V4 Pro 0813 Thinking: 1,449 EloTop
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- DeepSeek V4 Pro (Thinking): 11.4%Top
- DeepSeek V4 Pro 0813 Thinking: 11.4%Top
AA-LCRv1.1
Reasoning across long documents
- DeepSeek V4 Pro (Thinking): 80.3%Top
- DeepSeek V4 Pro 0813 Thinking: 80.3%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- DeepSeek V4 Pro (Thinking): 41.0%Top
- DeepSeek V4 Pro 0813 Thinking: 41.0%Top
GPQA Diamond
Graduate-level science questions
- DeepSeek V4 Pro (Thinking): 92.8%Top
- DeepSeek V4 Pro 0813 Thinking: 92.8%Top
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- DeepSeek V4 Pro (Thinking): 14.1%Top
- DeepSeek V4 Pro 0813 Thinking: 14.1%Top
SciCode
Scientific computing in Python
- DeepSeek V4 Pro (Thinking): 51.0%Top
- DeepSeek V4 Pro 0813 Thinking: 51.0%Top
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- DeepSeek V4 Pro (Thinking): 80.3%Top
- DeepSeek V4 Pro 0813 Thinking: 80.3%Top
Scores from Artificial Analysis, updated Oct 26, 2026. DeepSeek V4 Pro (Thinking) was tested at max reasoning effort; compare scores measured at the same effort.Artificial Analysis
- DeepSeek V4 Pro (Thinking)
- DeepSeek V4 Pro 0813 Thinking
| Leaderboard | DeepSeek V4 Pro (Thinking) | DeepSeek V4 Pro 0813 Thinking |
|---|---|---|
| Korean | Not in top 20 | #10 · 1464.1 |
Ranks among Noumen catalog models on LMArena leaderboards. Only top-20 placements are listed.LMArena
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
- 47.1 tok/s
- Faster than 34% of 254 models
- Time to first token
- 2.5 s
- Quicker than 67% of 254 models
- Prompt cache hits
- 61.0%
- About $0.32 per 1M input tokens with caching
- Request success (24h)
- 99.9%
- 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/deepseek-v4-pro:thinking",
"reasoning_effort": "none",
"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/deepseek-v4-pro:thinking",
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.