Qwen3.5 Flash / 채팅
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the 3 series, these models deliver a leap forward in performance for both pure text and multimodal tasks, offering fast response times while balancing inference speed and overall performance.
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이 모델의 실제 요금은 API 요청에서 전달된 특정 매개변수를 기반으로 동적으로 계산됩니다. 아래는 구체적인 조합과 해당 가격입니다:
| 모달리티 | 입력 크레딧 | 출력 크레딧 | 입력 가격 | 출력 가격 | 암시적 캐시 적중 | 명시적 캐시 적중 | 캐시 생성 |
|---|---|---|---|---|---|---|---|
| Standard | 100/ 1M Tokens | 400/ 1M Tokens | $0.100 | $0.400 | -- | $0.010 10/ 1M Tokens | $0.125 125/ 1M Tokens |
Models from the Same Channel
Explore complementary models and alternative versions from the same provider channel.
Qwen3.8 Flash
qwen3.8-flash
Alibaba's cost-efficient multimodal reasoning model. Supports text, image, and video inputs with text output. Features a native 1M-token context window for long documents, codebases, and agentic workflows. Excels in coding assistance, desktop interaction, chart analysis, and long-video understanding. Compatible with OpenAI/Anthropic protocols for seamless integration
Qwen3 Max
qwen3-max
Compared with the September 23, 2025 version, the newly upgraded Qwen-3 Max seamlessly integrates thinking and non-thinking modes, bringing an all-round obvious performance boost. Its thinking mode supports web search, web content extraction and code interpreter. It can conduct in-depth logical reasoning and call external tools to solve intricate problems more precisely
Qwen3 Coder Plus
qwen3-coder-plus
Powered by Qwen3, this is a powerful Coding Agent that excels in tool calling and environment interaction to achieve autonomous programming. It combines outstanding coding proficiency with versatile general-purpose abilities.
Qwen3.6 Plus
qwen3.6-plus
The Qwen3.6 native vision-language Plus series models demonstrate exceptional performance on par with the current state-of-the-art models, with a significant improvement in overall results compared to the 3.5 series. The models have been markedly enhanced in code-related capabilities such as agentic coding, front-end programming, and Vibe coding, as well as in multi-modal general object recognition, OCR, and object localization.
Recommended Related Models
Explore complementary video and multimodal models with your unified API key.
GLM-5.3
glm-5.3
Zhipu AI's flagship text model optimized for complex software engineering and long-horizon Agent tasks. Features a 1M-token context window with mandatory reasoning (3 levels: low/high/max). Coding capability improved 50% over GLM-5.2 on Z.ai Code Bench; scores SOTA on Terminal Bench 3.0. Excels in cybersecurity tasks (cyber vulnerability discovery)
GLM-5.2
glm-5.2
Flagship text model purpose-built for long-horizon agentic workflows. Features a 1M context window supporting project-level engineering in a single session. Excels at autonomous coding: can complete development, testing, and multi-platform deployment from a single prompt. Top open-weight model per Artificial Analysis; #1 globally on Code Arena. MIT-licensed and Day-0 optimized for domestic AI chips
GLM-5
glm-5
GLM-5 is Z.ai’s flagship open-source foundation model engineered for complex systems design and long-horizon agent workflows. Built for expert developers, it delivers production-grade performance on large-scale programming tasks, rivaling leading closed-source models. With advanced agentic planning, deep backend reasoning, and iterative self-correction, GLM-5 moves beyond code generation to full-system construction and autonomous execution.
MiniMax M3
MiniMax-M3
Flagship multimodal foundation model supporting text, image, and video inputs with text output. Features a 1M-token context window via MiniMax Sparse Attention (MSA), cutting per-token compute to ~1/20 of previous gen at full context. Excels at long-horizon agentic work, coding, and tool use. Native multimodal training from step zero ensures deep semantic alignment. Scores 59.0% on SWE-Bench Pro and 83.5 on BrowseComp, surpassing Opus 4.7