| Specification | Value |
|---|---|
| Parameters (Total) | 754 billion (MoE) |
| Parameters (Active per token) | ~40 billion |
| Architecture | Mixture-of-Experts (MoE) Transformer |
| Context Window | 200K tokens (~203,000 tokens) |
| Max Output Tokens | 128,000 tokens |
| Input Modalities | Text (Vision via GLM-5V-Turbo variant) |
| Output Modalities | Text |
| Languages | Multilingual; strong Chinese & English |
| Training Data Cutoff | Not publicly disclosed |
| Open Weights | Yes — HuggingFace |
| License | MIT License |
| API Availability | Yes — Z.ai (api.z.ai), BigModel.cn, OpenRouter |
| Training Hardware | Huawei chips (no NVIDIA GPUs) |
| Training Method | Multi-task SFT → Reasoning RL → Agentic RL → General RL → On-policy cross-stage distillation |
| Self-Hosting | Yes — SGLang, vLLM, xLLM, Transformers, KTransformers |
| Release Date | April 7, 2026 |
The API is OpenAI-SDK-compatible, meaning existing integrations built for GPT models can switch to GLM-5.1 with minimal code changes. No consumer chatbot interface has been prominently announced at time of research.
| Item | Cost | Notes |
|---|---|---|
| Input (per 1M tokens) | $1.40 | Z.ai official pricing |
| Output (per 1M tokens) | $4.40 | Z.ai official pricing |
| Cached Input | $0.26 / 1M | ~81% discount vs input |
| Cached Input Storage | Free (limited time) | Promotional pricing |
| Free Tier | Not publicly confirmed | Check docs.z.ai |
| Batch API | Not disclosed | — |
| GLM-5 (predecessor) Input | $1.00 / 1M | ~40% cheaper |
| GLM-5 (predecessor) Output | $3.20 / 1M | Comparison reference |
Source: docs.z.ai/guides/overview/pricing — retrieved April 11, 2026. Pricing subject to change; third-party aggregators show slight variation ($1.26–$1.40 input, $3.96–$4.40 output). GLM-5.1 is priced ~40% above GLM-5, which community discussion suggests may be revised downward over time.
| Metric | GLM-4.6 | GLM-5 | GLM-5.1 |
|---|---|---|---|
| Release | Sep 2025 | Feb 2026 | Apr 7, 2026 |
| Architecture | Transformer | MoE | MoE (refined) |
| Parameters | Not disclosed | 754B (est.) | 754B / ~40B active |
| Context Window | 128K | 200K | 200K |
| SWE-Bench Pro | N/A | 55.1% | 58.4% (+3.3pp) |
| NL2Repo | N/A | 35.9% | 42.7% (+6.8pp) |
| Coding Score (BenchLM) | N/A | 35.4 | 45.3 (+28%) |
| Max Autonomous Duration | Short tasks | ~Hours | Up to 8 hours |
| Open Weights | No | No | Yes (MIT) |
| License | Proprietary | Proprietary | MIT |
| API Input Price | ~$0.30/M | $1.00/M | $1.40/M |
The shift from GLM-5 to GLM-5.1 is a post-training refinement rather than a new architecture — but the 28% coding benchmark jump and the open-weights release represent significant qualitative leaps. Notably, GLM-5.1 is also the first model in this family released under a permissive MIT license.
| Model | SWE-Bench Pro | AIME 2026 | GPQA-Diamond | NL2Repo | Input $/M | Context | Open Weights |
|---|---|---|---|---|---|---|---|
| GLM-5.1 | 58.4% | 95.3% | 86.2% | 42.7% | $1.40 | 200K | ✅ MIT |
| Claude Opus 4.6 | 57.3% | 98.2% | N/A | 33.4% | ~$15.00 | 200K | ❌ |
| GPT-5.4 | 57.7% | 98.7% | N/A | 41.3% | ~$2.50 | 400K | ❌ |
| Gemini 3.1 Pro | 54.2% | N/A | N/A | N/A | ~$3.50 | 1M | ❌ |
| MiniMax M2.7 | N/A | N/A | N/A | N/A | ~$0.30 | 1M | Partial |
| Gemma 4 (31B) | N/A | N/A | N/A | N/A | Free/Low | 128K | ✅ |
Sources: SWE-Bench Pro scores from Z.ai blog, Reddit r/singularity (Apr 2026), lushbinary.com (Apr 2026), help.apiyi.com (Apr 2026). AIME 2026 and GPQA from lushbinary.com. Competitor pricing indicative — check providers for current rates. N/A = not available at time of research.
GLM-5.1 leads on agentic engineering benchmarks (SWE-Bench Pro, NL2Repo) but trails proprietary models on pure reasoning (AIME 2026). Its standout advantage is being the only top-tier agentic model available as open weights under MIT.
GLM-5.1 is engineered for long-horizon task execution — it can operate continuously on a single task for up to 8 hours, cycling through planning → execution → testing → bug-fixing → delivery without human hand-holding. This is fundamentally different from models designed for sub-minute interactions. Under the same evaluation standard, it is one of the few models globally — and the first Chinese model — to reach this level.
On KernelBench Level 3 (GPU kernel optimization), GLM-5.1 achieved a 3.6× geometric mean speedup over reference PyTorch implementations — vs. 1.49× for torch.compile max-autotune. The model ran thousands of tool-invocation-driven optimizations autonomously across 50 problems, improving continuously throughout the run.
GLM-5.1 uses a five-stage post-training pipeline: multi-task SFT → Reasoning RL → Agentic RL → General RL → on-policy cross-stage distillation. This stacked RL approach is specifically designed to produce stable, compound improvements across both general intelligence and agentic coding — avoiding the trade-offs common when optimizing for one at the expense of the other.
Despite 754B total parameters, only ~40B are activated per token inference — matching the active-parameter footprint of smaller dense models while retaining the breadth of the full MoE network. This design was pioneered in the open-source space by DeepSeek and Qwen and is now fully validated by GLM.
GLM-5.1 was trained entirely on Huawei chips — a notable geopolitical and supply-chain statement, demonstrating that frontier-quality LLM training is achievable without NVIDIA GPUs. This has significant implications for Chinese AI sovereignty and the global chip landscape.
Very few models at this capability tier are released as open weights at all — fewer still under the permissive MIT license. This makes GLM-5.1 freely usable for commercial products, fine-tuning, research, and redistribution without proprietary restrictions.
On release day (April 7, 2026), GLM-5.1 posted a 58.4% score on SWE-Bench Pro — topping Claude Opus 4.6 (57.3%), GPT-5.4 (57.7%), and Gemini 3.1 Pro (54.2%). For an open-source Chinese model to beat all three Western frontier labs simultaneously on a real-world software engineering benchmark made significant waves across AI communities on Reddit and Twitter/X.
In a representative demonstration cited by Z.ai, GLM-5.1 built a complete Linux desktop system from scratch within its 8-hour autonomous window — covering architecture planning, component coding, integration, testing, and delivery. This served as the headline proof-of-concept for the long-horizon agentic positioning.
At launch, GLM-5.1 was priced 2.5× higher than GLM-5 ($1.40 vs $1.00 input, $4.40 vs $3.20 output). Community discussions on Reddit noted this was unusual since the model's inference cost (40B active params) is not meaningfully higher. Speculation ran that Z.ai priced it as a "premium launch" and will revise pricing once initial demand normalizes.
The announcement that GLM-5.1 was trained entirely on Huawei chips — without NVIDIA GPUs — became a talking point beyond the AI community, picked up in technology policy and semiconductor circles. It was seen as a direct signal that US export restrictions on NVIDIA chips to China have not blocked frontier model development.
Early users on Reddit (r/ZaiGLM) noted that GLM-5.1 can become unstable near its maximum 200K context. The workaround circulating in the community — setting auto-compaction to trigger at ~50% of the advertised context window — suggests that real-world effective context may be somewhat lower than the spec, a detail not addressed in official documentation at time of research.