DeepSeek · Open weights
DeepSeek V3/Deepseek Chat
DeepSeek original V3 model, trained on nearly 15 trillion tokens, matches leading closed-source models at a far lower price. Quantized at FP8.
- Tools
- Structured output
- PDF input
- Input
- Text
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.10
- Output
- $0.43
Reference prices as published by the model provider. API example
Specifications
- Context window
- 128K
- Max output
- 8.2K
- Average output (7 days)
- 293 tokens
- Longer than 30% of 465 models
Side by side
DeepSeek V3/Deepseek Chat compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| DeepSeek V3/Deepseek ChatDeepSeekThis model | DeepSeek Chat 0324DeepSeekRemove | |
|---|---|---|
| Provider | DeepSeek | DeepSeek |
| Discovery tier | Open weights | Open weights |
| Added | Feb 27, 2025 | Jan 1, 2024 |
| Limits | ||
| Context window | 128K | 128K |
| Max output | 8.19K | 8.19K |
| Average output | 293 tokens | 280 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.10Best | $0.20 |
| Output | $0.43Best | $0.77 |
| Cache read | — | $0.14 |
| Performance | ||
| Throughput | 27.1 tok/sBest | 25.1 tok/s |
| Time to first token | 2.5 sBest | 2.9 s |
| Request success (24h) | 99.9% | 99.9% |
| Benchmark indexes | ||
| Intelligence Index | 16.0Best | 9.7 |
| Coding Index | — | 21.2 |
| Math Index | 59.0Best | 41.0 |
| Capabilities | ||
| Tools | Yes | Yes |
| Structured output | Yes | Yes |
| PDF input | Yes | No |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- DeepSeek V3/Deepseek Chat
- DeepSeek Chat 0324
Intelligence Index
- DeepSeek V3/Deepseek Chat16.0
- DeepSeek Chat 03249.7
DeepSeek V3/Deepseek Chat: Better than 40% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- DeepSeek V3/Deepseek Chat: —
- DeepSeek Chat 0324: 21.2
Math Index
Composite of the math evaluations
- DeepSeek V3/Deepseek Chat: 59.0Top
- DeepSeek Chat 0324: 41.0
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- DeepSeek V3/Deepseek Chat: —
- DeepSeek Chat 0324: 0.3%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- DeepSeek V3/Deepseek Chat: —
- DeepSeek Chat 0324: 0.0%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- DeepSeek V3/Deepseek Chat: —
- DeepSeek Chat 0324: 68 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- DeepSeek V3/Deepseek Chat: —
- DeepSeek Chat 0324: 69 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- DeepSeek V3/Deepseek Chat: —
- DeepSeek Chat 0324: 1.6%
AA-LCRv1.1
Reasoning across long documents
- DeepSeek V3/Deepseek Chat: 45.7%Top
- DeepSeek Chat 0324: 40.7%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- DeepSeek V3/Deepseek Chat: 11.2%Top
- DeepSeek Chat 0324: 4.7%
GPQA Diamond
Graduate-level science questions
- DeepSeek V3/Deepseek Chat: 75.1%Top
- DeepSeek Chat 0324: 65.5%
MMLU-Pro
Multi-subject knowledge and reasoning
- DeepSeek V3/Deepseek Chat: 83.7%Top
- DeepSeek Chat 0324: 81.9%
IFBench
Following precise output instructions
- DeepSeek V3/Deepseek Chat: 49.0%Top
- DeepSeek Chat 0324: 41.0%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- DeepSeek V3/Deepseek Chat: —
- DeepSeek Chat 0324: 0.0%
SciCode
Scientific computing in Python
- DeepSeek V3/Deepseek Chat: —
- DeepSeek Chat 0324: 39.0%
LiveCodeBench
Recent competitive programming problems
- DeepSeek V3/Deepseek Chat: 59.3%Top
- DeepSeek Chat 0324: 40.5%
Math
AIME 2025
Competition mathematics, 2025 problems
- DeepSeek V3/Deepseek Chat: 59.0%Top
- DeepSeek Chat 0324: 41.0%
AIME
Competition mathematics
- DeepSeek V3/Deepseek Chat: —
- DeepSeek Chat 0324: 52.0%
MATH-500
Competition math problems
- DeepSeek V3/Deepseek Chat: —
- DeepSeek Chat 0324: 94.2%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- DeepSeek V3/Deepseek Chat: 32.6%Top
- DeepSeek Chat 0324: 15.2%
τ²-Bench Telecom
Tool use in a customer-support simulation
- DeepSeek V3/Deepseek Chat: 78.9%Top
- DeepSeek Chat 0324: 47.1%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- DeepSeek V3/Deepseek Chat: 45.7%Top
- DeepSeek Chat 0324: 40.7%
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
- 27.1 tok/s
- Faster than 18% of 250 models
- Time to first token
- 2.5 s
- Quicker than 67% of 250 models
- Prompt cache hits
- 68.1%
- 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-chat",
"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-chat",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)DeepSeek
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