China's two MIT-licensed 1M-context flagships, three weeks apart. DeepSeek V4 Pro is the stronger generalist with the more efficient long-context attention stack; GLM-5.2 is deliberately coding-first, with native tool calling, MCP support, and configurable thinking effort aimed at agent harnesses. For coding agents specifically, GLM-5.2's focus shows; for everything else, V4 Pro is the safer default.
GLM vs DeepSeek at a glance
| GLM-5.2 | DeepSeek V4 | |
|---|---|---|
| Vendor | Zhipu AI (Z.ai) (China) | DeepSeek (China) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | MIT — weights free to download, self-host, fine-tune, and use commercially | MIT — weights free to download, self-host, fine-tune, and use commercially |
| Parameters | 753B (~40B active) | 1.6T (49B active) |
| Context window | 1M tokens | 1M tokens |
| Modalities | text | text |
| Pricing | Varies by hosting provider (open weights) | $1.74 in / $3.48 out per 1M tokens |
| Released | 2026-06-13 | 2026-04-24 |
Specs and pricing verified July 2026.
About GLM-5.2
GLM-5.2 is Zhipu AI's June 13, 2026 flagship: a ~753B-parameter mixture-of-experts model with ~40B active parameters, a 1-million-token context window, up to 128K output tokens, and MIT-licensed weights on Hugging Face. It is built deliberately as a coding and agent model — configurable thinking effort, native tool calling, MCP integration, structured JSON output — rather than a general-purpose chat model.
The engineering hook is IndexShare, Zhipu's sparse-attention refinement that reuses indexers across sparse-attention layers to cut per-token compute at long context. That is what makes the 1M-token window usable in practice for long-horizon coding agents, not just impressive on a spec sheet.
Full specs, benchmarks, and hardware guidance: the GLM-5.2 page.
About DeepSeek V4
DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier. It is a 1.6-trillion-parameter mixture-of-experts model that activates just 49B parameters per token, pairs a 1M-token context window with up to 384K tokens of output, and ships under a plain MIT license — no usage thresholds, no acceptable-use gate.
The headline engineering story is the attention stack. V4 combines Compressed Sparse Attention (CSA), which compresses KV entries 4× along the sequence with softmax-gated pooling, with Heavily Compressed Attention (HCA) at 128× compression. The practical result: at 1M-token context, V4 Pro needs roughly 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek-V3.2. Long context stopped being a luxury and became the default across DeepSeek's services.
Full specs, benchmarks, and hardware guidance: the DeepSeek V4 page.
Choosing between them
Choose GLM-5.2 for:
- Long-horizon coding agents (1M-token context built for repository-scale work)
- Teams that want an MIT-licensed Claude-for-coding alternative
- Tool-driven agentic workloads (native tool calling, MCP, JSON output)
Choose DeepSeek V4 for:
- Frontier-level reasoning and coding on an open license
- Very long documents and repositories (1M-token context as the default)
- Teams that need Anthropic/OpenAI API compatibility with open-weight economics
Frequently asked questions
Is GLM-5.2 better than DeepSeek V4?
China's two MIT-licensed 1M-context flagships, three weeks apart. DeepSeek V4 Pro is the stronger generalist with the more efficient long-context attention stack; GLM-5.2 is deliberately coding-first, with native tool calling, MCP support, and configurable thinking effort aimed at agent harnesses. For coding agents specifically, GLM-5.2's focus shows; for everything else, V4 Pro is the safer default.
Which is cheaper: GLM-5.2 or DeepSeek V4?
GLM-5.2: Varies by hosting provider (open weights). DeepSeek V4: $1.74 in / $3.48 out per 1M tokens. Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host GLM-5.2 and DeepSeek V4?
GLM-5.2: yes — weights are published under MIT. DeepSeek V4: yes — weights are published under MIT.