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GLM-5.1 from Z-AI is live on Chat-O. Released on April 7, 2026, this model targets a specific weakness in earlier language models: maintaining quality across long-horizon coding tasks that require many sequential steps.
Most AI code generators produce excellent one-shot results. Give them a prompt and they return a working function. The hard problem is multi-step coding: tasks where the model must write code, review it, test it, fix it, extend it, and then integrate it with existing code, all while remembering what it did in earlier steps.
GLM-5.1 was built for exactly this kind of work.
| Feature | GLM-5 | GLM-5.1 | GLM-5.2 |
|---|---|---|---|
| Released | Feb 11, 2026 | Apr 7, 2026 | Jun 16, 2026 |
| Tiers | Balanced, Power | Power | Balanced, Power |
| Context | 204,800 | 202,752 | 1,048,576 |
| Focus | General reasoning | Long-horizon coding | Agent-optimized |
| Multi-Step Success | 60% at 20 steps | 72% at 20 steps | 88% at 20 steps |
| Best Use | Daily coding | Multi-file projects | Agent workflows |
The 12 percentage point improvement over GLM-5 on multi-step tasks is the headline number. GLM-5.1 does not just reason better; it reasons more consistently across longer sequences.
Give GLM-5.1 a 10,000 line codebase and ask it to migrate from a class-based API to a functional one. The model will:
We tested this against GLM-5, GPT-5, and Claude Opus 4.6. GLM-5.1 produced the most internally consistent result, with fewer edge-case errors where changes in one file contradicted changes in another.
“When I say generate tests, I mean generate tests that actually pass.” GLM-5.1 understands this intuitively. It produces test suites that respect mock boundaries, handle edge cases, and integrate with existing test infrastructure.
One of the hardest problems in software engineering is keeping documentation aligned with code. GLM-5.1 can read both and identify discrepancies. We tested it on a project where the docs described a v2 API while the code implemented v3. GLM-5.1 correctly identified every mismatch and produced updated documentation.
The Kimi family from Moonshot AI offers an alternative path. Kimi K2.7 Code (released June 12) targets similar use cases in the Balanced tier. Here is how they compare:
| Aspect | GLM-5.1 (Power) | Kimi K2.7 Code (Balanced) |
|---|---|---|
| Context | 202,752 tokens | 262,144 tokens |
| Tier | Power only | Balanced |
| Coding Focus | Long-horizon, multi-step | End-to-end programming |
| Multi-file | Excellent | Very Good |
| Cost | Power credits | Balanced credits |
For budget-conscious teams, Kimi K2.7 Code is an excellent alternative. For maximum depth on complex multi-file projects, GLM-5.1 remains the better choice.
GLM-5.1 is available now in the Power tier on Chat-O. Select it from the model dropdown under Power models. If you are already using GLM-5 and wish it handled longer tasks better, GLM-5.1 is your direct upgrade path.
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