DeepSeek Model Evaluation and Benchmarking

Comprehensive interpretation of DeepSeek models' performance on mainstream benchmarks such as MMLU, HumanEval, GSM8K, and MT-Bench. Full comparison of DeepSeek-V3, GPT-4o, Claude 3.5, Qwen2.5, and Llama 3, with evaluation code and model selection decision guide.

Start Evaluation

Model Evaluation

Why Do We Need Benchmarks?

Benchmarks are standardized methods for quantitatively evaluating the capabilities of large language models. Through unified test sets and scoring criteria, benchmarks allow us to objectively compare models across core dimensions such as knowledge, reasoning, code generation, and mathematical computation, providing data support for model selection decisions.

Chapter 1

Evaluation Overview

Before starting specific evaluations, let's understand the framework of LLM evaluation, mainstream evaluation dimensions, and core benchmarks.

Six Evaluation Dimensions

Knowledge & Reasoning	Evaluates the model's mastery of factual knowledge and logical reasoning ability. Core benchmarks: MMLU, HellaSwag, ARC-Challenge.
Code Generation	Evaluates the model's ability to generate correct code and solve programming problems. Core benchmarks: HumanEval, MBPP, LiveCodeBench.
Mathematical Reasoning	Evaluates the model's ability to solve math problems and perform symbolic reasoning. Core benchmarks: GSM8K, MATH, AIME.
Multilingual Ability	Evaluates the model's understanding and generation in non-English languages. Core benchmarks: C-Eval, CMMLU, MGSM.
Safety & Alignment	Evaluates the model's safety, refusal rate for harmful content, and value alignment. Core benchmarks: SafetyBench, TruthfulQA.
Conversational Ability	Evaluates the model's performance in multi-turn dialogue and open-ended Q&A. Core benchmarks: MT-Bench, AlpacaEval, Chatbot Arena.

Overview of Mainstream Evaluation Systems

Benchmark	Evaluation Dimension	Number of Questions	Scoring Method	Language
MMLU	Multi-domain Knowledge	14,042	Multiple-choice Accuracy	English
HumanEval	Code Generation	164	pass@1 Rate	Python
GSM8K	Elementary Math	1,319	Answer Accuracy	English
MATH	Competition Mathematics	12,500	Answer Accuracy	English
MT-Bench	Multi-turn Dialogue	80 Multi-turn	GPT-4 Score 1-10	Multilingual
C-Eval	Chinese Knowledge	13,948	Multiple-choice Accuracy	Chinese
Chatbot Arena	General Dialogue	Anonymous Voting	ELO Ranking	Multilingual

The evaluation results are sourced from official technical reports of each model, the OpenCompass evaluation platform, and the LMSYS Chatbot Arena leaderboard, with data as of June 2025. Different evaluation frameworks and prompt settings may cause slight score variations; it is recommended to focus on relative rankings rather than absolute scores. For more model details, please see DeepSeek Model Architecture Details.

Chapter 2

Knowledge and Reasoning Evaluation

Knowledge reasoning capability is the fundamental ability of large models. MMLU covers 57 subject areas, from humanities and social sciences to STEM majors, and is currently the most authoritative knowledge evaluation benchmark. HellaSwag and ARC-Challenge focus on common sense reasoning and complex reasoning abilities.

MMLU Evaluation Results Comparison

Model	MMLU (5-shot)	HellaSwag (10-shot)	ARC-C (25-shot)	BBH (3-shot)
DeepSeek-V3	88.5	91.6	95.0	87.3
GPT-4o	88.7	92.1	95.3	88.1
Claude 3.5 Sonnet	88.3	90.8	94.6	86.9
Qwen2.5-72B	86.1	88.4	92.7	84.2
Llama 3.1-70B	84.4	86.0	91.2	82.5

Evaluation Analysis

DeepSeek-V3 is on par with GPT-4o in knowledge evaluation, with an MMLU score gap of only 0.2 percentage points, placing them in the same tier. Considering that DeepSeek-V3's API price is only about 1/10 of GPT-4o's, its cost-effectiveness advantage is significant.
In terms of reasoning ability, DeepSeek-V3's performance on ARC-Challenge and BBH is close to Claude 3.5 Sonnet, demonstrating strong logical chain construction capabilities in complex reasoning tasks.

Among open-source models, DeepSeek-V3 significantly leads Qwen2.5-72B and Llama 3.1-70B, outperforming them by about 2-4 percentage points on MMLU, establishing its position as the flagship open-source model.

HellaSwag common sense reasoning, DeepSeek-V3's 91.6% is very close to GPT-4o's 92.1%, indicating that DeepSeek has no obvious shortcomings in common sense understanding and daily reasoning.

Evaluation Note

The MMLU evaluation uses a 5-shot setting, i.e., the model is given 5 examples before answering. Different evaluation frameworks (such as OpenCompass, lm-evaluation-harness) may have slight implementation differences, so it is recommended to compare horizontally under the same evaluation framework.

Chapter 3

Code Ability Evaluation

Code generation is one of the most core application scenarios for large models. HumanEval and MBPP are the gold standards for code ability evaluation, while LiveCodeBench focuses on model performance in real online programming competitions.

Code Evaluation Results Comparison

Model	HumanEval (pass@1)	MBPP (pass@1)	LiveCodeBench (pass@1)	MultiPL-E (avg)
DeepSeek-V3	92.1	87.6	51.3	78.4
GPT-4o	90.2	86.8	53.6	76.9
DeepSeek-Coder-V2	90.2	83.4	43.2	76.3
Claude 3.5 Sonnet	92.0	86.4	51.1	77.8
Qwen2.5-Coder-32B	88.4	82.6	42.8	72.1

HumanEval Evaluation Code Example

Use HumanEval to evaluate the model's code generation ability, measuring the probability of the model solving a problem within k attempts through the pass@k metric:

from human_eval.data import write_jsonl, read_problems


from human_eval.evaluation import evaluate_functional_correctness
from openai import OpenAI

# Initialize DeepSeek client
client = OpenAI(
    api_key="sk-your-api-key",
    base_url="https://api.deepseek.com",
)

# Generate HumanEval answers
problems = read_problems()
num_samples_per_task = 1
samples = []

for task_id in problems:
    prompt = problems[task_id]["prompt"]
    response = client.chat.completions.create(
        model="deepseek-chat",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.0,
        max_tokens=1024,
    )
    completion = response.choices[0].message.content
    samples.append(dict(task_id=task_id, completion=completion))

write_jsonl("deepseek_samples.jsonl", samples)

# Evaluate pass@1
results = evaluate_functional_correctness("deepseek_samples.jsonl")
print(f"DeepSeek-V3 HumanEval pass@1: {results['pass@1']*100:.1f}%")

Evaluation Analysis

DeepSeek-V3 tops HumanEval with 92.1%, slightly surpassing GPT-4o's 90.2% and Claude 3.5 Sonnet's 92.0%, placing its code generation capability in the global first tier.
DeepSeek-V3 also achieves the highest score on MBPP (87.6%), demonstrating strong practicality on Python programming tasks.
On LiveCodeBench, GPT-4o leads slightly with 53.6%; this benchmark is closer to real competition scenarios and is much harder than HumanEval.
MultiPL-E multilingual code (covering Python, Java, C++, JavaScript, etc.), DeepSeek-V3 leads with a composite score of 78.4%, indicating balanced multilingual programming ability.

Chapter 4

Math Reasoning Evaluation

Math reasoning is a touchstone for large model capabilities and a traditional strength of DeepSeek. From elementary school math to AIME problems at the International Mathematical Olympiad level, DeepSeek models have demonstrated outstanding performance across all levels of math evaluations.

Math Evaluation Results Comparison

Model	GSM8K (8-shot)	MATH (4-shot)	AIME 2024	AIME 2025
DeepSeek-V3	95.8	76.3	42.7	38.2
DeepSeek-R1	94.2	72.8	79.8	65.0
GPT-4o	93.1	69.8	35.4	30.1
Claude 3.5 Sonnet	94.0	71.1	36.2	32.5
Qwen2.5-Math-72B	92.7	68.2	30.5	26.8

Evaluation Analysis

DeepSeek-V3 achieves the highest scores on both GSM8K and MATH, with GSM8K reaching 95.8% and MATH reaching 76.3%, leading comprehensively in math reasoning capabilities among similar models.

In the AIME 2024 and 2025 evaluations, DeepSeek-R1 demonstrated impressive performance in reasoning-enhanced mode, scoring 79.8% on AIME 2024 (far exceeding other models' 35-42%), fully showcasing the immense value of Chain-of-Thought technology in competitive mathematics.

DeepSeek-V3's non-reasoning mode has already achieved 42.7% on AIME, leading GPT-4o's 35.4% and Claude 3.5's 36.2%, indicating that its foundational mathematical capabilities are already very solid.

The math-specialized model Qwen2.5-Math-72B, although specifically optimized for mathematics, still falls short of DeepSeek-V3 on MATH and AIME, showing that general-purpose models can surpass specialized ones in mathematical reasoning.

Why is DeepSeek strong at mathematics?

The DeepSeek team extensively used math-related synthetic data and reinforcement learning (RL) techniques during training. DeepSeek-R1's chain-of-thought enhancement method allows the model to think deeply before answering, significantly improving its ability to solve complex mathematical problems. This is also one of the most prominent differentiating advantages of the DeepSeek series models.

Chapter 5

Chinese Capability Evaluation

As a model developed by a Chinese team, DeepSeek's performance in Chinese capabilities has attracted much attention. C-Eval, CMMLU, and C3 are the most authoritative evaluation benchmarks in the Chinese NLP field, covering Chinese understanding from primary and secondary school knowledge to professional domains.

Comparison of Chinese Evaluation Results

Model	C-Eval (5-shot)	CMMLU (5-shot)	C3 (0-shot)	MMLU (Chinese translation)
DeepSeek-V3	86.5	84.8	82.3	86.2
GPT-4o	82.1	80.4	78.6	83.5
Qwen2.5-72B	84.2	83.1	80.8	84.8
Claude 3.5 Sonnet	78.3	76.9	74.2	79.8
Llama 3.1-70B	72.6	70.4	68.1	73.2

In-depth Analysis of Chinese Capabilities

DeepSeek-V3 leads comprehensively in Chinese evaluations, scoring 86.5% on C-Eval, leading GPT-4o by about 4.4 percentage points and Qwen2.5 by about 2.3 percentage points, showing a clear advantage in Chinese capabilities.
The CMMLU evaluation covers 67 Chinese domains, and DeepSeek-V3 achieved 84.8%, the highest among all models. Especially in Chinese-specific domains such as Chinese history and culture, ancient poetry, and traditional Chinese medicine, DeepSeek significantly outperforms overseas models.

C3 Chinese Reading Comprehension, DeepSeek-V3 scores 82.3%. This evaluation simulates real Chinese reading scenarios, showing that DeepSeek also excels in Chinese long-text understanding.

Chinese capability of overseas models, Claude 3.5 Sonnet scores only 78.3% on C-Eval, and Llama 3.1 only 72.6%, showing a significant gap with DeepSeek in Chinese scenarios. If your business is primarily in Chinese, DeepSeek is the better choice.

Qwen2.5-72B, a Chinese-optimized model developed by Alibaba, has Chinese capability second only to DeepSeek-V3, making it the second choice for Chinese scenarios.

Importance of Chinese Evaluation

Many models that perform well in English evaluations have significantly reduced effectiveness in Chinese scenarios. For Chinese enterprise users, Chinese evaluation results are more valuable than English ones. It is recommended to focus on the C-Eval and CMMLU Chinese benchmark results when selecting models.

Chapter 6

Dialogue Capability Evaluation

Dialogue capability evaluation focuses on the model's performance in real multi-turn dialogue scenarios, including response quality, context understanding, instruction following, and user preference. MT-Bench, AlpacaEval 2.0, and Chatbot Arena are the three core dialogue evaluation benchmarks.

Dialogue Evaluation Results Comparison

Model	MT-Bench (Score)	AlpacaEval 2.0 (LC)	Chatbot Arena ELO	Arena Rank
GPT-4o	9.12	57.5	1321	#1
DeepSeek-V3	9.08	55.8	1305	#3
Claude 3.5 Sonnet	9.04	54.2	1314	#2
Gemini 1.5 Pro	8.96	52.1	1289	#4
Qwen2.5-72B	8.72	48.3	1248	#7

MT-Bench Evaluation Dimensions Explained

MT-Bench includes 80 high-quality multi-turn dialogue questions across 8 categories, scored by GPT-4 as a judge (1-10 points):

Evaluation Dimension	DeepSeek-V3	GPT-4o	Claude 3.5
Writing	9.42	9.35	9.38
Role-playing	8.96	9.12	9.05
Reasoning	9.28	9.18	9.22
Math	8.85	8.62	8.58
Coding	9.15	9.08	9.12
Knowledge Extraction	8.92	9.05	8.98
Humanities & Social Sciences	9.22	9.28	9.18
STEM	8.84	8.98	8.82

Evaluation Analysis

DeepSeek-V3 ranks 3rd on Chatbot Arena, with an ELO score of 1305, in the same tier as GPT-4o (1321) and Claude 3.5 (1314), with minimal differences.
MT-Bench total score 9.08, only 0.04 points behind GPT-4o's 9.12, and it leads or ties in writing, reasoning, coding, and math.
The unique value of Chatbot Arena lies in its real-user anonymous voting, reflecting subjective user preferences rather than automated scoring. DeepSeek-V3's high ranking indicates its responses are widely recognized by real users.
AlpacaEval 2.0 LC (length-controlled win rate) scores 55.8% for DeepSeek-V3, indicating a good balance between length and quality, unaffected by the 'longer is better' bias.

Chapter 7

Long-Context Evaluation

Long-context processing capability is an important metric for measuring the practicality of large models. DeepSeek-V3 natively supports a 128K context window. Needle-in-a-Haystack (NIAH), LongBench, and RULER are the three core benchmarks for long-context evaluation.

Long-Context Evaluation Results Comparison

Model	Context Window	NIAH (128K)	LongBench (avg)	RULER (128K)
DeepSeek-V3	128K	99.2	54.6	91.8
GPT-4o	128K	98.6	55.8	90.2
Claude 3.5 Sonnet	200K	98.8	54.2	90.5
Gemini 1.5 Pro	1M	99.5	53.8	91.2
Qwen2.5-72B	128K	97.5	52.3	88.6

Needle-in-a-Haystack (NIAH) Evaluation Explanation

NIAH evaluation: Insert a specific piece of information (the "needle") at a random position in a very long text, then ask the model to retrieve it. This is the most direct way to test a model's ability to precisely locate information in long contexts.
DeepSeek-V3 achieves a NIAH score of 99.2% at 128K context length, almost perfect, indicating the model can effectively utilize the full 128K context window without "forgetting" intermediate information.

RULER Evaluation is stricter, inserting multiple pieces of information in long texts to test the model's ability to track multiple targets simultaneously. DeepSeek-V3 scores 91.8%, the highest among all models.

LongBench Comprehensive Evaluation covers 6 major categories and 21 subtasks including single-document QA, multi-document QA, summarization, and code. GPT-4o leads slightly with 55.8%, while DeepSeek-V3 ranks second with 54.6%.

Recommendations for Long-Text Scenarios

For scenarios requiring processing of long documents, codebase analysis, contract review, etc., DeepSeek-V3's 128K context window and excellent long-text retrieval capabilities are sufficient for most needs. For ultra-long texts (200K+), Claude 3.5 Sonnet and Gemini 1.5 Pro are alternative options.

Chapter 8

Multimodal Evaluation

DeepSeek-VL2 is DeepSeek's vision-language model, supporting image understanding and visual reasoning. MMBench, MMMU, and MathVista are the most authoritative evaluation benchmarks for vision-language models.

Multimodal Evaluation Results Comparison

Model	MMBench (EN)	MMBench (CN)	MMMU (val)	MathVista
GPT-4o	86.5	84.2	69.1	63.8
DeepSeek-VL2	84.8	86.1	64.5	62.2
Claude 3.5 Sonnet	85.2	82.8	68.4	61.5
Gemini 1.5 Pro	85.8	83.5	67.8	62.5
Qwen2-VL-72B	84.2	85.5	63.8	60.2

Evaluation Analysis

DeepSeek-VL2 excels in Chinese image understanding, scoring 86.1% on MMBench (CN), surpassing GPT-4o's 84.2%, with clear advantages in scenarios like Chinese OCR and Chinese chart understanding.
In English multimodal evaluations, GPT-4o still leads with the highest scores on MMBench (EN) at 86.5% and MMMU at 69.1%. DeepSeek-VL2 follows closely, with a gap of 2-5 percentage points.
MathVista Mathematical Visual Reasoning, GPT-4o leads with 63.8%, DeepSeek-VL2 ranks second with 62.2%, indicating that DeepSeek is also competitive in visual mathematical tasks such as math charts and geometry problems.
MMMU Benchmark (Massive Multi-disciplinary Multimodal Understanding) covers 30 disciplines, DeepSeek-VL2 scores 64.5%, showing robust performance in multimodal professional knowledge understanding.

Multimodal Selection Recommendations

If your business focuses on Chinese image understanding (such as Chinese document OCR, Chinese chart parsing), DeepSeek-VL2 is the best choice. If you focus on English multimodal tasks, GPT-4o and Claude 3.5 Sonnet are also excellent options. Keep an eye on the release of DeepSeek's upcoming V3 multimodal version. For details, see DeepSeek Multimodal Models.

Chapter 9

Self-built Evaluation System

In addition to relying on public benchmarks, you can also build your own evaluation pipeline, using lm-evaluation-harness and OpenCompass to conduct customized evaluation of DeepSeek models.

Option 1: Using lm-evaluation-harness

lm-evaluation-harness is a standardized evaluation framework developed by EleutherAI, supporting 200+ evaluation benchmarks, and can be quickly evaluated via command line:

# Install lm-evaluation-harness
git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .

# Evaluate DeepSeek-V3 on MMLU
lm_eval \
  --model openai-completions \
  --model_args model=deepseek-chat,base_url=https://api.deepseek.com/v1/completions,api_key=sk-xxx \
  --tasks mmlu \
  --num_fewshot 5 \
  --batch_size 8 \
  --output_path ./results/deepseek-v3

# Evaluate code ability (HumanEval + MBPP)
lm_eval \
  --model openai-completions \
  --model_args model=deepseek-chat,base_url=https://api.deepseek.com/v1/completions,api_key=sk-xxx \
  --tasks humaneval,mbpp \
  --num_fewshot 0 \
  --output_path ./results/deepseek-v3-code

# Evaluate math ability
lm_eval \
  --model openai-completions \
  --model_args model=deepseek-chat,base_url=https://api.deepseek.com/v1/completions,api_key=sk-xxx \
  --tasks gsm8k,math \
  --num_fewshot 8 \
  --output_path ./results/deepseek-v3-math

Option 2: Using OpenCompass

OpenCompass is a one-stop evaluation platform developed by Shanghai AI Laboratory, with better support for Chinese evaluation, more suitable for domestic users:

# Install OpenCompass
git clone https://github.com/open-compass/opencompass
cd opencompass
pip install -e .

# Use config file to evaluate DeepSeek
# Create configs/eval_deepseek.py
from opencompass.models import OpenAISDK
from opencompass.partitioners import NaivePartitioner
from opencompass.runners import LocalRunner
from opencompass.tasks import OpenICLInferTask, OpenICLEvalTask

# Configure DeepSeek model
models = [
    dict(
        type=OpenAISDK,
        path='deepseek-chat',
        key='sk-your-api-key',
        openai_api_base='https://api.deepseek.com/v1/chat/completions',
        is_chat_api=True,
        max_out_len=2048,
        temperature=0.0,
        batch_size=8,
    )
]

# Select evaluation datasets
datasets = [
    'mmlu',
    'ceval',
    'cmmlu',
    'gsm8k',
    'humaneval',
]
# Run evaluation (command line)
# python run.py configs/eval_deepseek.py --debug

Option 3: Python Fully Automated Evaluation Script

Use Python to write custom evaluation scripts to run multiple benchmarks in batch and generate comparison reports:

"""DeepSeek model fully automated evaluation pipeline"""
import json
import time
from datetime import datetime
from openai import OpenAI

class DeepSeekBenchmark:
    """DeepSeek model evaluation class"""

    def __init__(self, api_key, base_url="https://api.deepseek.com"):
        self.client = OpenAI(api_key=api_key, base_url=base_url)
        self.results = {}

    def run_gsm8k(self, num_samples=100):
        """Run GSM8K math evaluation"""
        from datasets import load_dataset
        dataset = load_dataset("gsm8k", "main", split="test")
        correct = 0
        total = 0

        for i, item in enumerate(dataset):
            if i >= num_samples:
                break
            response = self.client.chat.completions.create(
                model="deepseek-chat",
                messages=[{"role": "user", "content": item["question"]}],
                temperature=0.0,
            )
            answer = response.choices[0].message.content
            # Extract numbers from the answer
            import re
            numbers = re.findall(r'-?\d+\.?\d*', answer)
            if numbers and float(numbers[-1]) == float(item["answer"].split("####")[-1].strip().replace(",", "")):
                correct += 1
            total += 1
if (i + 1) % 10 == 0:
                print(f"GSM8K progress: {i+1}/{num_samples}, current accuracy: {correct/total*100:.1f}%")

        self.results["gsm8k"] = {"accuracy": correct/total, "total": total}
        return self.results["gsm8k"]

    def generate_report(self):
        """Generate evaluation report"""
        report = {
            "model": "DeepSeek-V3",
            "timestamp": datetime.now().isoformat(),
            "results": self.results,
        }
        with open("benchmark_report.json", "w") as f:
            json.dump(report, f, indent=2, ensure_ascii=False)
        print("Evaluation report generated: benchmark_report.json")
        return report

# Usage example
benchmark = DeepSeekBenchmark("sk-your-api-key")
benchmark.run_gsm8k(num_samples=100)
benchmark.generate_report()

Evaluation Framework Comparison

Framework	Advantages	Use Cases
lm-evaluation-harness	High standardization, active community, supports 200+ benchmarks	Academic research, standard evaluation comparison
OpenCompass	Good Chinese support, visual reports, one-click evaluation	Chinese scenario evaluation, enterprise-level evaluation platform
Custom script	Flexible and controllable, customizable evaluation logic and metrics	Evaluation for specific business scenarios, A/B testing

Chapter 10

Selection Decision Guide

Based on the above evaluation data, combined with your specific scenario and budget, choose the most suitable DeepSeek model version and deployment plan.

Recommended Models by Scenario

Application Scenario	Preferred Model	Alternative Model	Key Evaluation Metrics
General Conversation	DeepSeek-V3	GPT-4o / Claude 3.5	MT-Bench 9.08, Arena ELO 1305
Code Development	DeepSeek-V3	DeepSeek-Coder-V2	HumanEval 92.1, MBPP 87.6
Mathematical Reasoning	DeepSeek-R1	DeepSeek-V3	AIME 2024 79.8, MATH 72.8
Chinese Scenarios	DeepSeek-V3	Qwen2.5-72B	C-Eval 86.5, CMMLU 84.8
Multimodal	DeepSeek-VL2	GPT-4o	MMBench CN 86.1, MMMU 64.5
Long Text Processing	DeepSeek-V3	Claude 3.5 Sonnet	NIAH 99.2, RULER 91.8

Recommended Deployment Plans by Budget

Budget Level	Recommended Plan	Model	Estimated Cost
Free	DeepSeek Official Web	DeepSeek-V3	0 yuan/month
Low cost	DeepSeek API + Ollama local	DeepSeek-V3 / R1 distilled	100-500 yuan/month
Medium budget	DeepSeek API + self-built evaluation	DeepSeek-V3	500-2000 yuan/month
Enterprise-level	Private deployment + multi-model combination	V3 + R1 + VL2 combination	5000+ yuan/month

Key points for model selection

DeepSeek-V3 is the most cost-effective choice overall, ranking in the first tier across core dimensions such as knowledge, reasoning, code, math, and Chinese, with API pricing only about 1/10 of GPT-4o.
Prioritize DeepSeek-R1 for math-heavy reasoning scenarios, as its enhanced reasoning chain shows overwhelming advantages in competition-level benchmarks like AIME.
For Chinese-centric applications, DeepSeek-V3 is the first choice, significantly outperforming GPT-4o and Claude 3.5 on C-Eval and CMMLU.
For multimodal needs, DeepSeek-VL2 is currently recommended, with outstanding Chinese image understanding, while keeping an eye on future V3 multimodal versions.
It is advisable to build your own evaluation pipeline, using real business data for A/B testing of candidate models; public benchmarks are for reference only.
Model selection is not a one-time decision; it is recommended to re-evaluate quarterly, paying attention to new releases and updated benchmark data.

Comprehensive recommendation

For most enterprises and developers, DeepSeek-V3 is currently the optimal general-purpose choice. It is on par with the world's top closed-source models (GPT-4o, Claude 3.5) in core dimensions of LLM evaluation, while offering the best cost-performance among open-source models. For more model options and deployment solutions, please refer to the DeepSeek open-source model list and DeepSeek deployment tutorial.

Evaluation Help

DeepSeek Model Evaluation FAQ

Which is better, DeepSeek-V3 or GPT-4o? +

The two are very close on most evaluation metrics. DeepSeek-V3 has a slight advantage in math (GSM8K 95.8 vs 93.1), code (HumanEval 92.1 vs 90.2), and Chinese (C-Eval 86.5 vs 82.1), while GPT-4o is slightly better in overall conversation score (MT-Bench 9.12 vs 9.08) and multimodality. Overall, DeepSeek-V3 offers capabilities comparable to GPT-4o at about 1/10 the price, making it extremely cost-effective. If your scenarios are primarily Chinese or math reasoning, DeepSeek-V3 is the better choice.

Can benchmark scores fully represent model capabilities? +

No. Benchmarks have limitations: 1) Evaluation datasets may be contaminated by training data, leading to inflated scores; 2) Multiple-choice formats do not fully reflect real conversation scenarios; 3) Differences in implementation across evaluation frameworks can cause score deviations. It is recommended to use benchmarks as a reference, combined with A/B testing on your own business data and real user feedback for comprehensive evaluation.

What is the difference between DeepSeek-R1 and DeepSeek-V3? +

DeepSeek-R1 is a reasoning-enhanced model that performs deep thinking (Chain-of-Thought) before answering, showing significant advantages in math competitions (AIME 2024 79.8% vs V3's 42.7%) and complex reasoning tasks. However, R1 has slower response speed and higher cost. DeepSeek-V3 is a general conversational model that performs excellently on most daily tasks with lower latency. Recommendation: use V3 for daily conversations, R1 for complex reasoning and math problems.

How to build your own evaluation pipeline? +

We recommend using lm-evaluation-harness (international standard) or OpenCompass (Chinese-friendly). Brief steps: 1) Install the evaluation framework; 2) Configure DeepSeek API or local models; 3) Select evaluation tasks (MMLU, HumanEval, etc.); 4) Run evaluation and generate reports. It is recommended to first validate with standard benchmarks, then supplement with evaluations on your own business datasets. See Chapter 9 for complete code examples.

Where to get DeepSeek model evaluation data? +

The most authoritative sources include: 1) DeepSeek official technical reports and blogs; 2) OpenCompass leaderboard (opencompass.org.cn); 3) LMSYS Chatbot Arena (chat.lmsys.org); 4) HuggingFace Open LLM Leaderboard. It is recommended to use official technical reports as the primary reference, and use OpenCompass and Chatbot Arena for cross-validation.

Can open-source and closed-source models be fairly compared in evaluation? +

Evaluations conducted under the same framework with the same settings (prompt, few-shot, temperature, etc.) can be fairly compared. OpenCompass and lm-evaluation-harness both provide standardized evaluation processes. However, note: 1) Closed-source models may update via API, so evaluation results at different times may differ; 2) Some benchmarks may have data contamination, leading to inflated scores. It is recommended to fix the evaluation framework and version, and re-test periodically.

Learn More

DeepSeek Related Tutorials

Dive deeper into DeepSeek model usage, deployment, and ecosystem tools.

Getting Started

How to Use DeepSeek Models

Four usage methods, zero-basics tutorial.

Deployment Guide

DeepSeek Deployment Tutorial

Deployment options: Ollama, Docker, vLLM, K8s.

Model Details

DeepSeek Model Architecture

Technical architecture, Benchmark, model comparison.

Model List

DeepSeek Open-Source Models

6 series, 20+ models complete catalog.

RAG Tutorial

DeepSeek RAG Knowledge Base

Retrieval-augmented generation for enterprise knowledge bases.

Ecosystem Tools

DeepSeek Ecosystem Tools

WebUI, IDE plugins, Agent frameworks, RAG platforms.

DeepSeek Model Evaluation and Benchmarking

Why Do We Need Benchmarks?

Evaluation Overview

Six Evaluation Dimensions

Overview of Mainstream Evaluation Systems

Knowledge and Reasoning Evaluation

MMLU Evaluation Results Comparison

Evaluation Analysis

Code Ability Evaluation

Code Evaluation Results Comparison

HumanEval Evaluation Code Example

Evaluation Analysis

Math Reasoning Evaluation

Math Evaluation Results Comparison

Evaluation Analysis

Chinese Capability Evaluation

Comparison of Chinese Evaluation Results

In-depth Analysis of Chinese Capabilities

Dialogue Capability Evaluation

Dialogue Evaluation Results Comparison

MT-Bench Evaluation Dimensions Explained

Evaluation Analysis

Long-Context Evaluation

Long-Context Evaluation Results Comparison

Needle-in-a-Haystack (NIAH) Evaluation Explanation

Multimodal Evaluation

Multimodal Evaluation Results Comparison

Evaluation Analysis

Self-built Evaluation System

Option 1: Using lm-evaluation-harness

Option 2: Using OpenCompass

Option 3: Python Fully Automated Evaluation Script

Evaluation Framework Comparison

Selection Decision Guide

Recommended Models by Scenario

Recommended Deployment Plans by Budget

Key points for model selection

DeepSeek Model Evaluation FAQ

DeepSeek Related Tutorials

How to Use DeepSeek Models

DeepSeek Deployment Tutorial

DeepSeek Model Architecture

DeepSeek Open-Source Models

DeepSeek RAG Knowledge Base

DeepSeek Ecosystem Tools

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