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GLM-5.1

Frontier

Z.ai (Zhipu AI)•Released on 2026-04-08

Z.ai's agentic coding model optimized for 8-hour autonomous work. 754B MoE with 1700 tool call capacity. Beats Opus 4.6 and GPT-5.4 on SWE-Bench Pro. MIT open-sourced on Hugging Face.

Core Specs

203K
Context Window
0K
Max Output
✓
Reasoning
✓
Open Source
Multimodal Support
text

Scenario Scores

Coding
—
Writing
—
Translation
—
Data Analysis
—
Conversation
—
Image Gen
—

User Feedback Highlights

Based on community feedback. Hover to see original reviews.

+ Staircase optimization pattern for complex tasks+ 8-hour autonomous operation− New model, limited real-world feedback+ MIT open-sourced (HuggingFace available)+ 1700 tool calls (vs ~20 for other models)+ Beats Opus 4.6 and GPT-5.4 on SWE-Bench Pro− 3x quota during peak hours (14:00-18:00 Beijing Time)− Context window (202K) smaller than Opus 4.6 (1M)− API pricing structure not fully disclosed
Sentiment:👍 65%😐 25%👎 10%

Pros & Cons

Pros

  • +8-hour autonomous operation
  • +1700 tool calls (vs ~20 for other models)
  • +Beats Opus 4.6 and GPT-5.4 on SWE-Bench Pro
  • +MIT open-sourced (HuggingFace available)
  • +Staircase optimization pattern for complex tasks

Cons

  • −3x quota during peak hours (14:00-18:00 Beijing Time)
  • −API pricing structure not fully disclosed
  • −New model, limited real-world feedback
  • −Context window (202K) smaller than Opus 4.6 (1M)

Reliability

Incidents (30d)0

Pricing

Input (per 1M tokens)$0.00
Output (per 1M tokens)$0.00
Subscription$10/month
Free trial available
Updated on 2026-03-28

Get Started

1Visit the provider's website
2Create an account
3Start using the model

Compare with Others

GLM-5.1 vs Claude Opus 4.7 →GLM-5.1 vs Claude Opus 4.6 →GLM-5.1 vs Claude Opus 4.5 →

Benchmarks

sweBenchProBeats Opus 4.6 and GPT-5.4%
terminalBench263.5%
terminalBench2Note66.5 with Claude Code harness%
cyberGym68.7%
cyberGymNoteSingle-run pass over 1507 tasks, ~20pt lead over GLM-5%
mcpAtlas71.8%
t3Bench70.6%
humanitysLastExam31%
humanitysLastExamTools52.3%
aime202695.3%
gpqaDiamond86.2%
kernelBenchL33.6x%
kernelBenchL3NoteGeometric mean speedup vs PyTorch baseline (Opus 4.6 at 4.2x)%
vectorDBBench21500%
vectorDBBenchNoteQPS, 6x better than Opus 4.6 (3547) after 655 iterations%
maxToolCalls1700%