DeepSeek
DeepSeek V3
deepseek-v3Context
128K
Input / 1M tokens
$0.1400
Output / 1M tokens
$0.2800
Weighted tokens formula
1× provider × 1× tier
About this model
DeepSeek V3
DeepSeek V3 is a 671B-parameter Mixture-of-Experts model that activates 37B parameters per token. Designed for general-purpose chat, document analysis, and structured output, it punches well above its cost class on coding, reasoning, and long-context comprehension. The MoE architecture means inference is roughly as fast as a dense 37B model while quality matches dense 70B+ flagships.
Released
2024-12
Training cutoff
2024-07
Parameters
MoE 671B (37B active)
Best for
Use cases
- ✓Cost-sensitive production chat
- ✓Long-context document analysis (128K window)
- ✓Code generation + review
- ✓Structured-output extraction (JSON, function calls)
- ✓Multilingual workloads (Chinese, English, code)
Strengths
What it does well
- ●Top-tier coding ability for the price (HumanEval 89%)
- ●Strong on Chinese-language benchmarks (CMMLU 88.8)
- ●Native function calling + JSON mode
- ●128K input + 8K output — fits most agent loops
- ●Mixture-of-Experts keeps inference cheap at scale
Limitations
Know the trade-offs
- !Knowledge cutoff is mid-2024 — agents may need search augmentation for fresh facts
- !Reasoning depth trails specialist reasoners (use DeepSeek R1 for math-heavy work)
- !Outputs longer responses than GPT-4o by default — set max_tokens explicitly for cost control
Benchmarks
Published scores
Scores from official model cards and public leaderboards. Higher is better unless noted.
| Benchmark | Score | Measures |
|---|---|---|
| MMLU | 87.1 | General knowledge across 57 subjects |
| MMLU-Pro | 75.9 | Harder MMLU variant, 10 options per question |
| HumanEval | 89.0 | Python code generation, pass@1 |
| MATH-500 | 89.3 | Competition mathematics |
| DROP | 90.1 | Reading comprehension + numeric reasoning |
Code example
Chat completion with DeepSeek V3
from openai import OpenAI
client = OpenAI(
base_url="https://api.anoman.io/v1",
api_key="anm-sk-..."
)
response = client.chat.completions.create(
model="deepseek-v3",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)Weighted tokens
weighted_tokens = raw_tokens × 1 (provider) × 1 (tier)
Pro plan: 20M weighted tokens/month. Combined multiplier 1×: 20,000,000 raw tokens available.
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