# GLM-5 Cloud vs Qwen 3.5 vs Gemma 4 - Comprehensive Comparison

**Test Date:** April 8, 2026  
**Models Compared:**
- `qwen3.5:397b-cloud` (OpenClaw current standard)
- `gemma4:31b-cloud` (Google's latest)
- `glm-5:cloud` (NEW - Zhipu's flagship)

---

## Executive Summary

**🎯 The Verdict:** GLM-5 cloud is **powerful but SLOW** for coding agent tasks compared to Qwen 3.5 and Gemma 4.

### At a Glance

| Metric | Winner | Notes |
|--------|--------|-------|
| **Speed/Latency** | 🏆 Gemma 4 | 19.7s code generation vs GLM-5's 72.9s |
| **Code Quality** | 🏆 Qwen 3.5 | Most concise, production-ready |
| **Comprehensive Analysis** | 🏆 GLM-5 | Longest, most detailed responses |
| **Overall Coding** | 🏆 Qwen 3.5 | Best balance of speed + quality |
| **Multi-step Reasoning** | 🏆 Qwen 3.5 | Faster on complex refactoring |
| **Detailed Reviews** | 🏆 GLM-5 | Most thorough security analysis |

---

## Performance Metrics: Speed Comparison

### Total Testing Time
- **Qwen 3.5:** 256.4 seconds (4.27 min)
- **Gemma 4:** 439.9 seconds (7.33 min)
- **GLM-5:** 808.9 seconds (13.48 min) ⚠️

**GLM-5 is 3.1x slower than Qwen 3.5 overall**

### Per-Test Breakdown

| Test | Qwen 3.5 | Gemma 4 | GLM-5 | Fastest | Slowest |
|------|----------|---------|-------|---------|---------|
| 1. Code Generation | 75.1s | 19.7s 🏆 | 72.9s | Gemma (4x faster) | Qwen/GLM similar |
| 2. Bug Fixing | 13.9s 🏆 | 84.0s | 171.7s | Qwen (12.3x faster) | GLM (12.3x slower) |
| 3. Code Review | 19.0s 🏆 | 48.8s | 104.2s | Qwen (5.5x faster) | GLM (5.5x slower) |
| 4. Refactoring | 28.2s 🏆 | 25.1s | 92.0s | Gemma (3.7x faster) | GLM (3.7x slower) |
| 5. Test Writing | 49.8s 🏆 | 101.0s | 167.2s | Qwen (3.4x faster) | GLM (3.4x slower) |
| 6. Architecture | 70.4s 🏆 | 78.3s | 201.2s | Qwen (2.9x faster) | GLM (2.9x slower) |

### Key Finding
**GLM-5 is consistently 2-12x slower**, with the worst performance on bug fixing (171.7s vs Qwen's 13.9s) and architecture design (201.2s vs Qwen's 70.4s).

---

## Code Quality Analysis

### Test 1: Code Generation - `merge_sorted_streams()`

**Word Count:**
- Qwen 3.5: 132 words
- Gemma 4: 172 words
- GLM-5: 209 words ← Most verbose

**Quality Assessment:**

| Model | Approach | Quality | Notes |
|-------|----------|---------|-------|
| **Qwen 3.5** | Compact & direct | ⭐⭐⭐⭐⭐ | Perfect - minimal, working code |
| **Gemma 4** | Moderate verbosity | ⭐⭐⭐⭐ | Good, includes comments |
| **GLM-5** | Verbose with extras | ⭐⭐⭐⭐⭐ | Most robust - adds `from __future__` imports, extensive comments |

**Winner:** All three produce working code. Qwen is most concise; GLM-5 is most defensive with Python 3.10+ annotations.

---

### Test 2: Bug Fixing - ThreadSafe RateLimiter

**Analysis Quality:**
- **Qwen 3.5 (13.9s):** Identifies bugs quickly, provides fixes
- **Gemma 4 (84.0s):** More detailed explanations
- **GLM-5 (171.7s):** EXTREMELY detailed, but **takes 12x longer**

**Key Bug Identification:**
All three correctly identified:
- ✅ Missing lock in `get_remaining()` method
- ✅ Race condition in `reset()` method  
- ✅ Potential `KeyError` in `get_wait_time()`

**GLM-5 Advantage:** Added extra edge case handling for empty client IDs and better thread-safety patterns.

**Speed Issue:** GLM-5's verbose approach makes it impractical for quick bug fixes in production scenarios.

---

### Test 3: Code Review - Vulnerable REST API

**Response Lengths:**
- Qwen 3.5: 780 words (19.0s)
- Gemma 4: 752 words (48.8s)
- GLM-5: 1,105 words (104.2s) ← Most comprehensive

**Security Issues Found (all three models):**
1. ✅ **SQL Injection in search endpoint** - Identified by all
2. ✅ **SQL Injection in delete endpoint** - Identified by all
3. ✅ **Plaintext password storage** - Identified by all
4. ✅ **Missing authentication/authorization** - Identified by all
5. ✅ **Sensitive data exposure** - Identified by all (Qwen added SELECT * issue)

**Quality Differences:**

| Model | Format | Severity Labels | Code Examples | Recommendations |
|-------|--------|-----------------|---|---|
| Qwen 3.5 | Markdown with emojis | 🔴 Critical, 🟠 High | Shows vulnerable code | Clear fixes |
| Gemma 4 | Clean markdown | Listed clearly | Basic examples | Detailed mitigations |
| GLM-5 | Detailed prose | "Critical", "High" | Rich context | Extremely thorough |

**Winner for Audits:** GLM-5 provides the most comprehensive analysis, but Qwen's emoji-based formatting is more scannable for quick reviews.

---

### Test 4: Refactoring - Nested Data Processing

**Output Quality:**

| Model | Clarity | Explanation | Time |
|-------|---------|-------------|------|
| **Qwen 3.5** | ⭐⭐⭐⭐⭐ | Clear decisions | 28.2s 🏆 |
| **Gemma 4** | ⭐⭐⭐⭐ | Good explanations | 25.1s 🏆 |
| **GLM-5** | ⭐⭐⭐⭐ | Thorough notes | 92.0s ⚠️ |

**Winner:** Gemma 4 (fastest with good quality)

---

### Test 5: Test Writing - LRU Cache Jest Tests

**Output Metrics:**
- Qwen 3.5: 1,039 words (49.8s)
- Gemma 4: 750 words (101.0s)
- GLM-5: 984 words (167.2s)

**Test Coverage Comparison:**

| Aspect | Qwen 3.5 | Gemma 4 | GLM-5 |
|--------|----------|---------|-------|
| Basic operations | ✅ | ✅ | ✅ |
| Edge cases | ✅ | ✅ | ✅ |
| Concurrent access | Partial | ✅ | ✅ Rich |
| DX/Readability | ✅ Best | ✅ | Verbose |

**GLM-5 Strength:** Most thorough test cases with detailed comments, but takes 3.4x longer.

---

### Test 6: Architecture - Webhook Delivery System

**Output Sizes:**
- Qwen 3.5: 1,960 words (70.4s) 
- Gemma 4: 814 words (78.3s)
- GLM-5: 1,589 words (201.2s) ← Slowest

**Architecture Quality:**

| Component | Qwen 3.5 | Gemma 4 | GLM-5 |
|-----------|----------|---------|-------|
| **Database schema** | ✅ Complete | ✅ Good | ✅ Detailed |
| **Delivery service** | ✅ TypeScript | ✅ TS | ✅ TS + error handling |
| **Reliability patterns** | ✅ DLQ + retries | ✅ Basic | ✅ Comprehensive |
| **Implementation details** | ✅ Production-ready | Medium | ✅ Very thorough |

**Winner:** Qwen 3.5 - best value (detailed + fast)

---

## Pros & Cons Summary

### ✅ Qwen 3.5:397b-cloud

**Strengths:**
- ⚡ **FASTEST** for most coding tasks (2.9-12x faster than GLM-5)
- 📝 High-quality, production-ready code
- 🎯 Perfect balance of speed and quality
- 🧠 Excellent reasoning for refactoring and bug fixing
- 💾 Concise responses (easy to scan)

**Weaknesses:**
- Sometimes less verbose on architectural decisions
- Code generation responses slightly less detailed than GLM-5

**Best For:** 
- Fast iteration loops
- Production code generation
- Time-sensitive bug fixes
- Real-time agent tasks (OpenClaw)

**Overall Rating:** ⭐⭐⭐⭐⭐ (9.5/10)

---

### ✅ Gemma 4:31b-cloud

**Strengths:**
- ⚡ Fastest at code generation (19.7s)
- 🎯 Good balance between verbosity and speed
- 📊 Decent refactoring output (25.1s)
- 🔍 Competent code review capabilities

**Weaknesses:**
- 🐢 Slower at bug fixing (84.0s) and test writing (101.0s)
- Architecture design takes longer than Qwen

**Best For:**
- When you need the fastest code generation
- Lightweight refactoring tasks
- Medium-complexity reviews

**Overall Rating:** ⭐⭐⭐⭐ (7.5/10)

---

### ✅ GLM-5:cloud (NEW - Zhipu's Flagship)

**Strengths:**
- 📚 **MOST COMPREHENSIVE** analysis and responses
- 🔍 Best for deep security reviews (1,105 words on API review)
- 📋 Detailed architecture design with rich explanations
- 🛡️ Defensive coding practices (extra error handling)
- 🧠 Excellent at multi-step complex reasoning

**Weaknesses:**
- 🐢 **3x SLOWER** than Qwen 3.5 overall
- ⏱️ **12x slower** on bug fixing (171.7s vs 13.9s)
- 📝 Responses are too verbose for real-time agent tasks
- Not suitable for latency-sensitive applications
- Not practical for rapid iteration

**Best For:**
- One-time comprehensive code reviews
- Architectural design documents
- Security-focused analysis
- Non-time-critical documentation
- Academic or detailed technical analysis

**Best When:** You have time for detailed responses (not in production chat loop)

**Overall Rating:** ⭐⭐⭐ (6.5/10) - Great for depth, but too slow for most use cases

---

## Use Case Recommendations

### For OpenClaw Agent Framework

| Scenario | Recommended Model | Reason |
|----------|-------------------|--------|
| **Primary coding agent** | 🏆 Qwen 3.5 | Fast + quality = best for agents |
| **Quick bug fixes** | 🏆 Qwen 3.5 | 13.9s vs GLM-5's 171.7s |
| **Code generation** | 🥈 Gemma 4 | Fastest at 19.7s for initial scaffolding |
| **Deep security review** | 🥉 GLM-5 | Use offline/batch mode, not real-time |
| **Fallback (fastest)** | 🥈 Gemma 4 | When speed matters most |
| **Complex architecture** | 🏆 Qwen 3.5 | Good speed (70.4s) + comprehensive |

---

## Recommendation

### 🏆 VERDICT: Keep Qwen 3.5 as primary; skip GLM-5 for production agents

**Why?**
1. **Speed matters for agents** - User perception of 3x slower responses ruins UX
2. **Qwen 3.5 already excellent** - No quality advantage to justify 3x latency
3. **Use GLM-5 for batch analysis** - Run it offline for deep audit reports
4. **Gemma 4 still useful** - Fastest code generation (keeps as backup)

### Suggested Configuration

```json
{
  "agents": {
    "coding": {
      "primary": "ollama/qwen3.5:397b-cloud",
      "fallback": "ollama/gemma4:31b-cloud",
      "batch_analysis": "ollama/glm-5:cloud"
    }
  }
}
```

### When to Use GLM-5:cloud

✅ **DO USE:**
- Nightly security audit reports
- Archive/batch code review processes
- Detailed architectural documentation
- Complex system design documents
- Compliance/regulatory analysis

❌ **DON'T USE:**
- Real-time agent tasks
- Interactive coding sessions
- Time-sensitive bug fixes
- Production code generation loops
- User-facing features

---

## Test Files

All detailed results are saved in `./test-results/`:

**Qwen 3.5 Results:**
- `qwen3.5_397b-cloud_1_code_generation.md` (132 words, 75.1s)
- `qwen3.5_397b-cloud_2_bug_fixing.md` (357 words, 13.9s)
- `qwen3.5_397b-cloud_3_code_review.md` (780 words, 19.0s)
- `qwen3.5_397b-cloud_4_refactoring.md` (713 words, 28.2s)
- `qwen3.5_397b-cloud_5_test_writing.md` (1,039 words, 49.8s)
- `qwen3.5_397b-cloud_6_architecture.md` (1,960 words, 70.4s)

**Gemma 4 Results:**
- `gemma4_31b-cloud_1_code_generation.md` (172 words, 19.7s)
- `gemma4_31b-cloud_2_bug_fixing.md` (433 words, 84.0s)
- `gemma4_31b-cloud_3_code_review.md` (752 words, 48.8s)
- `gemma4_31b-cloud_4_refactoring.md` (583 words, 25.1s)
- `gemma4_31b-cloud_5_test_writing.md` (750 words, 101.0s)
- `gemma4_31b-cloud_6_architecture.md` (814 words, 78.3s)

**GLM-5 Results:**
- `glm-5_cloud_1_code_generation.md` (209 words, 72.9s)
- `glm-5_cloud_2_bug_fixing.md` (453 words, 171.7s)
- `glm-5_cloud_3_code_review.md` (1,105 words, 104.2s)
- `glm-5_cloud_4_refactoring.md` (876 words, 92.0s)
- `glm-5_cloud_5_test_writing.md` (984 words, 167.2s)
- `glm-5_cloud_6_architecture.md` (1,589 words, 201.2s)

---

## Conclusion

GLM-5 is impressive for **depth and comprehensiveness** but **too slow for production agent use**. 

Keep **Qwen 3.5** as your primary model—it delivers the best value for OpenClaw's agentic workflow. GLM-5 excels for non-urgent, batch-mode analysis where thoroughness beats speed.

**The future of Ollama's coding models is bright—use the right tool for the right job!** 🚀