This article interprets the product philosophy behind the evolution of model capabilities from the microscope of third-party evaluation – how to grasp the wave of “cost reduction and efficiency increase” set off by small models in today’s inference track has become a new arena, or the survival proposition that every AI product person must face.
Looking at SuperCLUE’s latest evaluation report, a set of data is shocking: the head reasoning model has improved its mathematical ability by 420% compared with three years ago, and the 7B small model has outperformed the large model with hundreds of billions of parameters in specific tasks.
This industry “physical examination report” from March 2025 not only reveals the technological leap from barbaric growth to intensive cultivation of Chinese large models, but also implies deep changes in the process of AI productization.
When the o3-mini (high) refreshes its cognition with its near-perfect mathematical reasoning ability, and when the DeepSeek-R1 series breaks through the “parameter shackles” with distillation technology, we see not only the change of the technology list, but also a rehearsal of the efficiency revolution and business logic reconstruction.
The picture comes from the Internet
1. The industry pattern has changed drastically: from general ability competition to vertical track breakthrough
1.1 Reasoning ability becomes the core battlefield
The large model arena in 2025 is undergoing a fundamental transformation. OpenAI’s latest o3-mini (high) topped the SuperCLUE overall list with a score of 76.01, and its mathematical reasoning score of 94.74 points set an industry record. This marks that the competition for large models has shifted from a general ability competition to an in-depth competition on the vertical track. In the field of scientific reasoning, ByteDance’s Doubao-1.5-pro is on par with the international top level with a score of 70 points, while Tencent’s hunyuan-turbos shows its scenario-based landing ability with a score of 70.09 in the Agent task.
1.2 The “overtaking in corners” strategy of domestic manufacturers
Domestic models have formed differentiated advantages in specific fields:
- QwQ-32BIt scored 88.6 points on the mathematical reasoning task, surpassing GPT-4.5-Preview
- DeepSeek-R1In the code generation task, it is only 1.84 points behind o3-mini (high).
- 360 Brain O1.5The accuracy of semantic understanding in Chinese scenarios has been increased to 89.7%.
This “single point breakthrough” strategy is reconstructing the market competition pattern. By focusing on vertical scenarios to polish core capabilities, manufacturers form technical moats in medical consultation, financial risk control, industrial quality inspection and other fields.
2. Technological breakthrough: distillation technology has given birth to the small model revolution
2.1 The “Counterattack Myth” of Model 7B
The picture comes from the Internet
The picture comes from the Internet
The picture comes from the Internet
The DeepSeek-R1-Distill series sets a new paradigm for small models:
- The 7B version scored 77.23 points for mathematical reasoning, surpassing 70% of the closed-source large model
- The 14B version scored 79.46 points in scientific reasoning tasks, approaching the GPT-4.5 level
- The inference speed of the 1.5B model on the device reaches 180ms/query
This technical route of “knowledge distillation + field fine-tuning” allows small models to maintain 80% of their core capabilities while reducing the inference cost to 1/15 of large models. Measured data from an e-commerce platform shows that the ROI of the 7B model in product recommendation scenarios has increased by 300%.
2.2 The “Law of 28” of Model Deployment
In model deployment practice, the industry is forming an intelligent resource allocation strategy:
Real-time interaction layer:It uses a 70B-level basic model to deal with dialogue scenarios that require in-depth understanding. Although the cost of a single inference of this type of model is as high as 0.3-0.5 yuan, its rapid response ability within 500 milliseconds can meet high-value scenarios such as financial customer service and medical consultation that require strict accuracy (>98%). According to the measured data of an online education platform, after using the 70B model, the analysis accuracy of complex mathematical problems increased from 82% to 95%, and the paid conversion rate increased by 17 percentage points.
Business processing layer: Configure the 7B distillation model to focus on data analysis, document processing, and other tasks that can tolerate 1-2 seconds of delay. This type of model compresses operating costs to 1/15 of large models while maintaining 80% of core capabilities. Through this solution, a cross-border e-commerce company has increased the efficiency of automatic product description generation by 4 times and reduced monthly model expenses by 2.1 million yuan.
Device edge layer: Deploy 1.5B quantitative models, specializing in millisecond-level response scenarios such as smart homes and in-vehicle systems. The neural architecture search-optimized micromodel can achieve an inference speed of 150 tokens/s on a 256MB memory device. The intelligent cockpit system of a new energy vehicle company achieved an offline voice control success rate of 98.3% through this solution, and the wake-up response time was shortened to 70 milliseconds.
This deployment system of “capability grading and dynamic scheduling” enables enterprises to reduce comprehensive operating costs by 40-65% while ensuring the accuracy of key businesses. According to data from industry-leading cloud computing platforms, intelligent routing algorithms can automatically allocate 70% of regular requests to small models for processing, and GPU resource utilization has increased from 32% to 58%.
3. Three major contradictions in the process of productization
3.1 Scissors difference between performance and cost
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The evaluation data shows:
- 20x cost difference for head model inference (Claude 3.7 Sonnet vs QwQ-32B)
- The cost of a 70B model conversation ≈ 300 7B model calls
- Enterprise-level users are more inclined to choose the mid-range model with a cost-effective > of 0.8
This prompted manufacturers to launch a “dynamic computing power allocation” service, where a cloud platform automatically allocates high-value requests to large models through intelligent routing algorithms, and routine tasks are handled by small models, reducing comprehensive costs by 65%.
3.2 The dilemma of matching capabilities and scenarios
Maturity differences in assessment exposures:
- High maturity: Text generation (SC index 0.89)
- To break through the area: Agent Task (SC Index 0.12)
This leads to the coexistence of “excess capacity” and “lack of function” in practical applications. Cases in the education industry show that 70% of model capabilities in math tutoring scenarios are not effectively utilized, while 30% of key needs (such as problem-solving step disassembly) have insufficient support rates.
3.3 The double-edged sword effect of open source ecology
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The open source community shows two major trends:
- Technology inclusiveness: Qwen2.5 series open source models have exceeded 35k stars on GitHub
- Commercialization anxiety: The open source ratio of core code of some manufacturers has been reduced from 85% to 40%
- Ecological differentiation: The PR merger efficiency of the head project increased by 300%, and the activity of the waist and tail projects decreased by 60%
An AI startup acquired 300 enterprise customers within 6 months through the “core model open source + value-added service charging” model, verifying the feasibility path of open source commercialization.
4. Key trends in the next 12 months
4.1 The “barrel theory” of model capabilities fails
The traditional comprehensive evaluation system is collapsing, and industries such as medical care and finance have begun to establish evaluation standards in vertical fields. It is expected that by 2026, 50% of enterprises will adopt the hybrid architecture of “master model + fine-tuning module”, and the number of field special models of leading manufacturers will exceed 100.
4.2 The critical point of the outbreak of end-side intelligence
Technological breakthroughs drive end-side deployment:
- The 4B model has an inference speed of 230 tokens/s on the Snapdragon 8 Gen4 chip
- New memory technology enables 1.5B models to run on 256MB memory devices
- Federated learning framework achieves 80% increase in multi-device collaborative training efficiency
The upcoming folding screen flagship model of a mobile phone manufacturer will be equipped with a self-developed 7B model, which supports complex schedule planning functions in offline conditions and increases battery life by 3 hours.
4.3 Paradigm shift of the evaluation system
Third-party evaluation agencies began to introduce the “dynamic pollution detection” mechanism, and the frequency of question bank updates was increased from quarterly to weekly. Enterprise users pay more attention to:
- Long-tail scene coverage (e.g., dialect comprehension)
- Consistency across multiple rounds
- Security perimeter control capabilities
A bank added a “100-time dialogue offset rate” indicator to the model selection, requiring a core factual error rate of < 0.5% for 100 consecutive rounds of dialogue.
Epilogue:
When the technological dividend period comes to an end, the large model war is moving from the laboratory to the deep water area of the industry. The competitive map of 2025 reveals a key turning point: the era of purely pursuing parameter scale is over, and the winners of the next stage will be pragmatic innovators who can accurately match the needs of the scene and build a sustainable technology ecosystem. Product managers need to establish a new dimension of evaluation and find the best balance between model selection, architecture design, and cost control to stay ahead of this intelligent revolution.
