See how AI Gateway compares to other AI API providers
Baidu and Alibaba are China's two earliest big AI players, and their open-source routes diverge sharply: ERNIE spreads five product lines bound to the Paddle ecosystem, while Qwen rides the most active community ecosystem across all scales. This comparison covers product matrices, ecosystem activity, deployment, and scenario fit.
ERNIE and Hunyuan take the most different routes in Chinese open models: Baidu spreads a full product matrix (flagship MoE, reasoning, vision, edge, OCR), while Tencent made the lightweight line's official quantization the most complete (FP8/GPTQ/AWQ in three formats). This comparison covers breadth, quantization openness, deployment ecosystem, and flagship-vs-lightweight tiers.
The essential difference between open Chinese models (Qwen/DeepSeek/Kimi/GLM) and US flagships (GPT-6/Claude class) is not benchmark scores — it is whether the weights are open. Open weights mean local deployment, data sovereignty, quantization freedom, and zero per-token cost. This article compares six verifiable dimensions and lands on a hybrid-routing conclusion.
Compare the two overseas families callable on EXAIV by endpoint format, model IDs and support boundaries, without inventing benchmarks or prices.
DeepSeek and Baidu ERNIE represent different open-model routes: DeepSeek drills into text reasoning, long context, and speculative decoding, while ERNIE offers flagship MoE, reasoning, vision, edge, and OCR lines. This comparison covers structure, ecosystem, deployment, and scenario fit.
DeepSeek drills into reasoning and long context, plus its own open-sourced speculative decoding; GLM polishes tool calling and agent scenarios into a strength. This comparison covers positioning, ecosystem, deployment, and cloud availability, with a runnable GLM call example.
DeepSeek drills into text reasoning and long context with open speculative decoding; Tencent Hunyuan offers hy4 with official FP8/GPTQ/AWQ quantization formats. This comparison covers family structure, ecosystem, deployment, and scenario fit.
DeepSeek puts all its weight behind text reasoning and long context; MiniMax broke out with H3 video generation as the family biggest hit. This comparison covers product lines, deployment toolchains, ecosystem, and scenario fit across two entirely different routes.
Baidu ERNIE excels with a complete matrix of OCR, VL, edge, and flagship MoE; MiniMax built a creation line with H3 video and Music3 audio. This comparison covers positioning, deployment stacks, ecosystem, and scenario fit.
GLM polishes tool calling and agent orchestration with direct cloud availability; Baidu ERNIE spreads flagship MoE, reasoning, vision, edge, and OCR across five lines. This comparison covers positioning, ecosystem, deployment, and scenario fit.
GLM excels at tool calling and agent orchestration with direct cloud availability; Hunyuan hy4 is known for official FP8/GPTQ/AWQ formats and local control. This comparison covers positioning, ecosystem, deployment, and scenario fit.
GLM polishes tool calling and agent orchestration with direct cloud availability; MiniMax broke out with H3 video generation. This comparison covers positioning, product lines, deployment stacks, and scenario fit across two routes.
Hunyuan stands out with hy4 official FP8/GPTQ/AWQ formats and A13B MoE; MiniMax became the creation-side pick with H3 ComfyUI video ecosystem. This comparison covers positioning, deployment stacks, ecosystem, and scenario fit.
Hunyuan hy4 is a 4.22 billion parameter dense model with a native 256K context, while DeepSeek-V4.1-Flash is a 763 billion parameter MoE with a native 1M context. This comparison covers architecture, community activity, quantization options, licensing, and deployment fit.
Hunyuan hy4 is a 4.22 billion parameter dense model while GLM-5.3-Flash is a 321 billion parameter MoE released under MIT. This comparison covers architecture, licensing, quantization ecosystem, community activity, deployment reality, and the scenarios each one wins.
Hunyuan hy4 is a 4.22 billion parameter dense model while Kimi K3 is a 2.78 trillion parameter MoE flagship. This comparison covers architecture, community activity, licensing, hardware floors, and why small models become more valuable once a large flagship is available.
Hunyuan hy4 is a 4.22 billion parameter dense model while Qwen3.8-Flash is a 180 billion parameter MoE flagship. This comparison walks through architecture, community activity, licensing, deployment reality, and scenario fit to help you decide between self-hosting a small model and calling a cloud one.
Kimi K3 and the DeepSeek V4 series are both open-source flagships callable on EXAIV: K3 leads on multimodal long-context reasoning while V4 covers a full tier ladder from high-frequency calls to heavy reasoning. This comparison covers specs, capability focus, API differences, and task routing, with copy-paste-ready code.
Kimi K3 and GLM-5.3-Flash are two complementary domestic flagships callable on EXAIV: one excels at deep reading and reasoning over long inputs, the other at tool calling and agent orchestration. This comparison covers positioning, real API calls, streaming behavior, and a ready-to-copy task-routing pattern. Example code runs as-is.
Kimi K3 and the GPT-6 family (sol / astra / luna) represent the two routes of current AI: one publishes weights you can self-host, the other is API-only. This comparison lays out verifiable differences across weights and licensing, deployment and data control, and capability boundaries — no side-taking.
Kimi grew out of long-context work with deep reading as its signature strength; Baidu ERNIE spreads flagship MoE, reasoning, vision, edge, and OCR across five lines. This comparison covers positioning, ecosystem, deployment, and scenario fit.
Kimi K3 is a long-context specialist callable directly on EXAIV cloud; Hunyuan hy4 stands out with official FP8/GPTQ/AWQ quantization formats. This comparison covers positioning, ecosystem, deployment, and scenario fit.
Kimi excels at long context and deep reading with direct cloud calling; MiniMax became the biggest Chinese open hit with H3 video generation. This comparison covers positioning, product lines, deployment, and scenario fit across two complementary routes.
Compare Mistral 7B, Llama 3.3 70B, and Gemma 3 27B by parameters, licences, ecosystem, hardware and local deployment.
Qwen and GLM represent two directions in Chinese open models: Qwen builds the broadest family and community ecosystem across all scales and modalities; GLM polishes tool calling and agent orchestration into a strength. This comparison covers family structure, quantization coverage, cloud availability, and agent capability, with a runnable GLM example.
Qwen and Hunyuan differ most in community ecosystem activity: Qwen's GGUF quantization coverage and developer tooling are overwhelming, while Hunyuan offers the most complete official quantization formats (FP8/GPTQ/AWQ for hy4). This comparison covers family structure, ecosystem, deployment, and scenario fit.
Qwen and Kimi take two different routes in open Chinese models: one covers nearly every scale and modality with a broad family, the other grew out of long-context work and drilled into coding and reasoning. This comparison covers product lines, open ecosystem, cloud availability, and local deployment, with runnable dual-model call examples.
Qwen and MiniMax take different routes in open Chinese AI: Qwen serves developers with broad scales and ecosystem, while MiniMax differentiates with H3 video, Music3 audio, and M3 multimodal text. This comparison covers product lines, local toolchains, ecosystem, and scenario fit.
Qwen3.8 and DeepSeek V4 are the two poles of open Chinese models: one has the broadest family and ecosystem, the other the most-proven long context plus speculative decoding acceleration. This comparison covers product lines, open ecosystem, local deployment, and quantization coverage — no fabricated benchmarks, scenario-based verdicts only.
