模型

浏览来自所有提供商的 41 个标准化 LLM 模型

显示第 25–41 项,共 41 个模型

MiniMax M2.7

中国

MiniMax's latest large language model with strong multilingual and multimodal capabilities. Competitive pricing with high-quality text generation and improved reasoning performance.

上下文
200K
发布日期
2026年3月

Mistral Medium 3.5

法国

Mistral AI's balanced model offering strong multilingual performance with excellent price-performance ratio. Optimized for production workloads requiring reliable quality across European and global languages.

上下文
128K
发布日期
2026年2月

DeepSeek V4

中国

DeepSeek's fourth-generation model with improved mixture-of-experts architecture, enhanced reasoning and coding capabilities, and stronger multilingual performance. Competitive with frontier proprietary models.

上下文
256K
发布日期
2026年2月

Devstral 2

法国

Mistral AI's frontier code agents model designed for solving software engineering tasks. Open-weight model optimized for agentic coding workflows and complex development tasks.

上下文
128K
发布日期
2025年12月

GigaChat 3.1 Lightning

俄罗斯

Sber's compact Mixture-of-Experts model with 10B total parameters and 1.8B active. Designed for fast multilingual assistant workloads, reasoning, code, function calling, and product-style deployment on edge devices.

上下文
8K
发布日期
2025年12月

Grok 4.1 Fast

美国

xAI's fast and cost-effective model with 2M token context window. Offers both reasoning and non-reasoning modes at significantly lower pricing than flagship models.

上下文
2.0M
发布日期
2025年11月

GLM-4.7

中国

Zhipu AI's multilingual agentic coding model with strong reasoning, tool use, and UI generation capabilities. Predecessor to GLM-5.1 with competitive performance on coding benchmarks.

上下文
131K
发布日期
2025年10月

Trendyol LLM 8B T1

土耳其

Turkish-optimized 8B chat model developed by Trendyol, Turkey's largest e-commerce platform. Built on Qwen3-8B and fine-tuned on large-scale Turkish e-commerce datasets. Features advanced chain-of-thought reasoning in Turkish with dual operation modes (/think and /no_think), strong instruction following, summarization, coding, and attribute extraction for catalogue enrichment. English reasoning capabilities are preserved alongside Turkish.

上下文
33K
发布日期
2025年7月

Gemini 2.5 Flash

美国

Google's cost-effective model optimized for high throughput tasks. Balances speed and intelligence with strong multimodal capabilities and 1M token context window.

上下文
1.0M
发布日期
2025年6月

Llama 4 Scout

美国

Meta's efficient MoE model with 17B active parameters (109B total, 16 experts). Supports up to 10M token context — the longest of any production model. Strong performance on reasoning and multilingual tasks.

上下文
10.0M
发布日期
2025年4月

Llama 4 Maverick

美国

Meta's quality-focused MoE model with 17B active parameters (400B total, 128 experts). Targets quality-critical tasks with benchmark scores competitive with GPT-4o and Gemini 2.5 Pro.

上下文
1.0M
发布日期
2025年4月

Qwen3 Coder

中国

Alibaba's Qwen3 Coder model optimized for software development tasks including code generation, debugging, code review, and technical documentation with strong multilingual programming support.

上下文
131K
发布日期
2025年4月

Qwen3 32B

中国

Alibaba's Qwen3 32B dense language model with strong reasoning and multilingual capabilities, supporting function calling and code generation across diverse tasks.

上下文
131K
发布日期
2025年4月

Llama 3.3 70B Instruct

美国

Meta's flagship open-weight model with 70 billion parameters. Strong multilingual capabilities with competitive performance on reasoning and coding benchmarks. Available for self-hosting and through various inference providers.

上下文
131K
发布日期
2024年12月

DeepSeek V3

中国

DeepSeek's third-generation large language model featuring mixture-of-experts architecture, strong multilingual capabilities, and competitive performance on reasoning and coding benchmarks.

上下文
128K
发布日期
2024年12月

Command R7B

美国

Cohere's compact 7B parameter model optimized for RAG, tool use, and code tasks. Delivers top-tier speed and efficiency on commodity GPUs and edge devices with 128K context window.

上下文
128K
发布日期
2024年12月

Llama 3.1 8B Instruct

美国

Meta's efficient open-weight model with 8 billion parameters from the Llama 3.1 family. Optimized for instruction following with strong performance on general tasks, coding, and multilingual benchmarks. Ideal for cost-effective deployment and edge inference scenarios.

上下文
131K
发布日期
2024年7月