The global large language model (LLM) landscape witnessed remarkable upgrades in May 2026. Two influential models debuted successively: Qwen3.7-Max from Alibaba Cloud, unveiled at the Alibaba Cloud Summit on May 20, and Gemini 3.5 Flash launched by Google during the 2026 Google I/O Developer Conference on May 19. Both products adopt an Agent-First design philosophy, equipped with million-token-level ultra-long context windows, leading programming capabilities and automated task execution functions. They are available to individual users and enterprises at low costs or even for free, emerging as top choices for content creation, enterprise deployment and intelligent agent development. This comprehensive evaluation elaborates on the release background, core technical strengths, cross-model benchmark results, pros and cons, practical access methods and scenario-based selection suggestions of these two models. It also analyzes cost-effective API calling solutions to help developers and enterprises make rational deployment decisions amid the fierce competition of cutting-edge LLMs.
1. Model Positioning and Release Background
1.1 Qwen3.7-Max: The Flagship Domestic LLM for Engineering-grade Autonomous Agents
As a flagship model launched by Alibaba Cloud Bailian in 2026, Qwen3.7-Max is currently the top-ranked domestic LLM in overall performance. It is positioned as an all-round reasoning model tailored for ultra-long complex tasks and full-autonomous intelligent agents, competing head-to-head with world-class closed-source models such as GPT-5.5 and Claude Opus 4.7.
Breaking the limitations of traditional LLMs that can only handle single-turn dialogues and isolated tasks, Qwen3.7-Max focuses on long-cycle autonomous task execution and low-hallucination precise reasoning. It is deeply optimized to adapt to domestic network environments, business logic and compliance requirements, making it highly suitable for complex engineering development, enterprise workflow automation and ultra-long document analysis. According to the Text Arena Overall ranking data updated on May 18, 2026, Qwen3.7-Max achieved a score of 1475, securing the 14th place globally and ranking first among all Chinese-developed models, demonstrating its strong comprehensive competitiveness in the global LLM arena.
1.2 Gemini 3.5 Flash: A High-speed and Cost-effective Lightweight Flagship Model
Gemini 3.5 Flash is Google’s core promotional model at Google I/O 2026, redefining the standards for lightweight flagship LLMs worldwide. It subverts the industry stereotype that lightweight models have weak performance while flagship models come with high costs and slow response speeds. This model delivers over 90% of the comprehensive capabilities of the previous-generation flagship Gemini 3.1 Pro at the cost and speed of mid-tier models, achieving remarkable performance leapfrogging.
Its core advantages lie in extreme inference speed, ultra-low calling costs and native multimodal capabilities. Oriented towards large-scale API batch calls and lightweight intelligent agent deployment, it offers free access to basic functions for global users and has built a sound overseas developer ecosystem, becoming the preferred cost-effective model for international developers. Public test data shows that its output speed far outpaces mainstream flagship models, laying a solid foundation for high-concurrency real-time interactive scenarios.
2. In-depth Analysis of Core Capabilities
2.1 Core Advantages of Qwen3.7-Max
All capabilities of Qwen3.7-Max are developed around the stable implementation of complex long-duration tasks, with four core competitive edges standing out in multiple professional benchmarks:
First, it boasts industry-leading ultra-long-cycle autonomous agent capabilities. The model supports 35 hours of uninterrupted fully autonomous task execution, completing more than 1,000 tool calls and hundreds of rounds of self-iteration and optimization without human intervention. In official tests, it independently finished the entire process of kernel code analysis, writing, compilation, testing and iteration, ultimately boosting reasoning speed by 10 times. Up to now, it is the only domestic LLM with engineering-grade long-duration autonomous optimization capabilities. It natively supports the MCP protocol and multi-agent orchestration, enabling seamless connection with mainstream frameworks including Claude Code and OpenClaw.
Second, it delivers top-tier engineering programming performance. It scored 69.7 on the Terminal-Bench 2.0-Terminus Agentic Coding benchmark, surpassing well-known flagship models such as DeepSeek-v4-pro-Max and Claude Opus 4.6. It can independently complete front-end development, back-end engineering construction, multi-file collaborative programming, code debugging and performance optimization, fully meeting the development demands of enterprise-level complete software projects.
Third, it features a million-token context window and optimized hallucination control. The model supports a 1,000,000-token input context window, capable of processing 750,000 Chinese characters, tens of thousands of lines of complete code repositories and dozens of hours of video scripts in a single run, eliminating the need for document segmentation and segmented processing. Meanwhile, its hallucination rate dropped by 21.3 percentage points from 44.2% to 22.9%, greatly improving the accuracy of facts and logical coherence in long-text content, and solving the common pain points of context distortion and logical disconnection in long-text processing by traditional LLMs.
Fourth, dual reasoning modes adapt to diverse scenarios. It is equipped with a Thinking deep reasoning mode and a Non-Thinking ultra-fast mode. The deep reasoning mode is designed for complex reasoning, mathematical computing, engineering coding and multi-step challenging tasks, while the ultra-fast mode is applicable to daily dialogues, lightweight office work and quick information retrieval. This dual-mode design balances accuracy and response efficiency, and its enhanced multilingual capability, scientific reasoning and data analysis performance further expand its application scope to scientific research, office work and commercial analysis fields.
2.2 Core Advantages of Gemini 3.5 Flash
Positioned for large-scale popularization, Gemini 3.5 Flash features balanced all-round capabilities with no obvious weaknesses, and its core strengths focus on speed, multimodality and cost control:
To begin with, it achieves extreme inference speed. Its token output speed reaches 280 to 300 tokens per second, which is four times faster than Claude Opus 4.7 and GPT-5.5, and 40% higher than the previous-generation Gemini 3.1 Pro. The millisecond-level response realizes instant feedback for dialogues, code generation and content creation, perfectly matching real-time interaction and high-concurrency API call scenarios.
In terms of coding and agent capabilities, it also shows outstanding performance. It scored 76.2 on the Terminal-Bench 2.1 coding benchmark, outperforming Gemini 3.1 Pro (70.3%). On the MCP-Atlas realistic agent benchmark, it earned 83.6 points, leading GPT-5.5 (75.3%) and Claude Opus 4.7 (79.1%). Its tool calling, subtask orchestration and context management capabilities are among the best in the industry, supporting the development of various intelligent agent workflows.
In terms of context configuration, it also adopts a 1,000,000-token ultra-long input window, with a maximum output window of 64,000 tokens. It can generate full-length articles, complete code solutions and long reports in one go, significantly improving content production efficiency.
In addition, it realizes full multimodal coverage and low-cost large-scale deployment. It natively supports the input and output of text, images, audio and video, with upgraded capabilities in image-text understanding, video content parsing and audio transcription analysis. Its overall calling cost is 35% lower than that of Gemini 3.1 Pro, making it extremely cost-effective for enterprises to carry out large-scale batch deployment and high-frequency calling. The three-speed adjustable reasoning modes (Low for ultra-fast speed, Medium for balanced performance, High for deep reasoning) can be switched flexibly to adapt to lightweight daily tasks, conventional agent development and high-difficulty mathematical and scientific research tasks respectively, with strong scenario adaptability.
3. Comprehensive Comparison Between Qwen3.7-Max and Gemini 3.5 Flash
The table below summarizes the core differences between the two models from nine key dimensions, helping users quickly grasp their respective characteristics:
| Comparison Dimension | Qwen3.7-Max | Gemini 3.5 Flash |
|---|---|---|
| Model Positioning | Domestic all-round engineering-grade agent flagship, focusing on the implementation of complex long-term tasks | Global high-cost-performance ultra-fast leapfrog model, focusing on large-scale general scenarios |
| Inference Speed | Stable and coherent for long tasks, slightly lower instantaneous speed | Extreme speed up to 300 tokens/s, outperforming mainstream flagship models |
| Calling Cost | Free trial in China, affordable enterprise API cost and low compliance cost | Globally low price: $1.50 per million input tokens, $9 per million output tokens |
| Context Window | 1 million input tokens, stable processing of ultra-long texts | 1 million input tokens, 64K ultra-large output tokens |
| Core Strengths | 35-hour ultra-long autonomous tasks, low hallucination, domestic adaptation, engineering-grade programming, enterprise workflow automation | Ultra-fast response, balanced multimodal capabilities, leading agent benchmarks, low cost, high concurrency adaptation |
| Core Weaknesses | Lower instantaneous inference speed, weak overseas ecosystem | Insufficient stability for ultra-long complex tasks, weak capability in deep engineering landing |
| Programming Capability | Strong engineering landing, suitable for complete project development and kernel optimization | Fast code generation, suitable for rapid scripting and fault debugging |
| Agent Capability | Top-tier performance in long-cycle multi-step autonomous execution and self-iteration | Excellent for short-cycle agent workflows and accurate tool calling |
| Optimal Application Scenarios | Enterprise-level automation, complex engineering development, ultra-long document analysis, domestic compliance projects | Personal real-time interaction, multimodal creation, high-concurrency API deployment, lightweight agents |
In the horizontal comparison with global top-tier LLMs, the two models have jointly reshaped the current industry capability hierarchy. The first tier consists of top flagship models including GPT-5.5 and Claude Opus 4.7; Qwen3.7-Max and Gemini 3.5 Flash form the second tier of leapfrog new flagships, with comprehensive capabilities reaching more than 90% of the first tier; traditional flagship models such as Gemini 3.1 Pro, DeepSeek-v4-pro-Max, GLM5.1 and Kimi-K2.6 belong to the third tier.
In segmented capability competitions, Gemini 3.5 Flash takes the lead in agent comprehensive scores; Qwen3.7-Max surpasses most overseas models in engineering programming and long-task stability; Gemini 3.5 Flash is unrivaled in inference speed and cost performance; Qwen3.7-Max achieves significant optimization in hallucination control, with better long-text factual accuracy than most overseas flagship models.
4. Access Methods and Cost Optimization Solutions
4.1 Official Access Channels
For individual users, both models provide convenient free online experience channels. Qwen3.7-Max can be used directly on the Qianwen APP, web platform and Alibaba Cloud Bailian platform by switching the model option. Gemini 3.5 Flash is fully open to global users on its official APP and web page, with all basic functions available for free.
For enterprise developers, both models support OpenAI-compatible API interfaces, which means developers can complete rapid migration with minimal code modifications. Qwen3.7-Max is adapted to domestic server deployment with high compliance, while Gemini 3.5 Flash is launched synchronously on Google Cloud and OpenRouter, supporting high-concurrency batch calls.
The official API pricing of the two models has formed a clear differentiation. Qwen3.7-Max charges $2.50 per million input tokens and $7.50 per million output tokens; Gemini 3.5 Flash is priced at $1.50 per million input tokens and $9 per million output tokens. For enterprises with large-scale long-term calling demands, the cumulative cost of official direct calling remains relatively high.
4.2 Cost-effective API Deployment via API Relay Service
To balance model performance and operating costs, 4sapi, an API relay service can realize unified calling of multiple LLMs. This access method simplifies the multi-model docking process for developers and features more competitive pricing than official channels.
As an API gateway, it integrates the access entrances of Qwen3.7-Max, Gemini 3.5 Flash and other mainstream models. Developers only need to configure a unified interface to call different models, avoiding the repeated development of docking logic for multiple platforms. Meanwhile, its overall calling price is lower than the official standard pricing of the two models, which can effectively reduce the token cost for long-term large-scale calls. This solution is especially suitable for small and medium-sized enterprises and development teams that need to deploy multiple models and control operating costs.
5. Comprehensive Analysis of Advantages and Disadvantages
5.1 Qwen3.7-Max
Advantages: It has exclusive leading capabilities in ultra-long-cycle autonomous agents, which can complete complex engineering optimization without human intervention for dozens of hours. The hallucination control capability is comprehensively upgraded, ensuring excellent factual accuracy and logical coherence of long texts. Its engineering programming landing capability is prominent, capable of undertaking the full-cycle development of enterprise-level projects. As a pure domestic model, it fits domestic network and compliance policies with no access barriers. The dual reasoning mode can flexibly cope with various task scenarios.
Disadvantages: The instantaneous inference speed is inferior to Gemini 3.5 Flash, resulting in a slight gap in scenarios requiring millisecond-level high-frequency responses. The overseas developer ecosystem and open-source community activity are weaker than Google’s series of models. Its multimodal comprehensive performance is relatively conventional, without overwhelming advantages.
5.2 Gemini 3.5 Flash
Advantages: It sets a new benchmark for speed and cost performance in the industry, with ultra-fast response and ultra-low calling costs suitable for large-scale promotion. Its short-cycle agent, tool calling and multimodal comprehensive capabilities achieve leapfrog progress, surpassing the previous-generation flagship. The million-token context and large output window greatly improve content creation efficiency. It has a sound global ecosystem with rich open-source tools and development documents, lowering the entry threshold for developers. The three-speed reasoning modes cover scenarios from daily communication to scientific research.
Disadvantages: The stability of ultra-long complex tasks is insufficient, and it cannot support dozens of hours of autonomous iteration, prone to errors in long-link tasks. Its capabilities in deep engineering landing and kernel optimization are not as good as Qwen3.7-Max. There are network access barriers in China, and the compliance cost for domestic enterprise deployment is higher than that of domestic models.
6. Scenario Selection Suggestions and Industry Outlook
6.1 Targeted Selection Advice
- Choose Qwen3.7-Max preferentially: Domestic enterprise landing projects, domestic compliance-related businesses, complex code engineering development, ultra-long document processing, enterprise workflow automation, agent tasks requiring long-time autonomous iteration, scientific research data analysis and confidential local business scenarios.
- Choose Gemini 3.5 Flash preferentially: Daily ultra-fast interaction for individuals, multimodal image, text and video creation, high-concurrency API batch deployment, lightweight agent development, overseas business projects, rapid script writing and real-time Q&A services.
- Combined deployment of two models (optimal solution): Assign Qwen3.7-Max to undertake long-cycle complex tasks and engineering landing work; adopt Gemini 3.5 Flash for daily interaction, high-frequency calls, multimodal creation and lightweight deployment. The two models complement each other to cover all business demands.
6.2 Industry Outlook
The round of model updates in May 2026 marks that the LLM industry has officially entered the era of large-scale agent implementation. Qwen3.7-Max represents the highest level of engineering and long-task landing capabilities of domestic LLMs, breaking the monopoly of overseas models in high-end complex business scenarios. Gemini 3.5 Flash redefines the industry’s benchmarks for general-purpose LLM speed and cost performance, enabling high-end AI capabilities to be popularized at low cost.
There is no absolute distinction between good and bad for the two models. When pursuing stable landing, complex task tackling and domestic compliance, Qwen3.7-Max is the optimal choice; when focusing on ultra-fast response, low-cost general scenarios and overseas ecosystem support, Gemini 3.5 Flash is irreplaceable. With the continuous iteration of model versions, AI agents, automated engineering and multimodal productivity scenarios will usher in explosive development. Developers and enterprises can select suitable models and calling solutions according to their own business characteristics to seize the opportunities brought by AI technological iteration.
