GLM-5.1 vs Claude 4 Sonnet: Reasoning Benchmark & API Pricing Comparison

[chawkian]

Zhipu AI's GLM-5.1 is the latest iteration of China's most prominent open-weight model family. Anthropic's Claude 4 Sonnet is the mid-tier workhorse that most developers use day-to-day. Both are positioned as high-end reasoning models ??but at very different price points.

This comparison uses official benchmark data from both vendors, supplemented with practical pricing from AIWave.

---

The Contenders

Spec	GLM-5.1 (Zhipu AI)	Claude 4 Sonnet (Anthropic)
Input price	$1.40 / 1M tokens	$3.00 / 1M tokens
Output price	$4.40 / 1M tokens	$15.00 / 1M tokens
Context window	128K tokens	200K tokens
Total cost (1M mixed)	$5.80	$18.00

GLM-5.1 costs roughly 2? less on input and 3.4? less on output than Claude 4 Sonnet ??about 1/3 the total cost per million tokens.

---

Benchmark Data: Head-to-Head

Using official reported scores (vendor self-reported):

Benchmark	GLM-5.1	Claude 4 Sonnet*	?	Winner
HumanEval (code generation)	87.0%	~90.0%*	-3.0	Claude
MATH (mathematical reasoning)	85.3%	~84.0%*	+1.3	GLM-5.1
MMLU (general knowledge)	86.8%	~89.0%*	-2.2	Claude

*Claude 4 Sonnet figures are approximate based on publicly reported ranges.

Key observations:

1. The gap is small across all benchmarks ??typically 1?? percentage points
2. GLM-5.1 matches or exceeds Claude on mathematical reasoning, notable given the ~3? price difference
3. Claude maintains a slight edge on code generation and general knowledge

---

Price-Adjusted Analysis

Scenario: Processing 1M Tokens (Mixed Workload)

Model	Input Cost	Output Cost	Total Cost	HumanEval	Cost per % point
Claude 4 Sonnet	$3.00	$15.00	$18.00	~90.0%	$0.20
GLM-5.1	$1.40	$4.40	$5.80	87.0%	$0.067

GLM-5.1 delivers a 3? value advantage in cost per benchmark point.

Scenario: 100 Daily Interactions (~3K in, ~1.5K out each)

Model	Daily Cost	Monthly Cost	Annual Cost
Claude 4 Sonnet	$3.15	$94.50	$1,134
GLM-5.1	$1.08	$32.40	$388

Annual savings: ~$746 by switching to GLM-5.1.

Scenario: Batch Code Review (50 PRs/week, 5K in + 1K out each)

Model	Weekly Cost	Monthly Cost
Claude 4 Sonnet	$1.50	$6.00
GLM-5.1	$0.57	$2.28

A team of 10 developers saves $37+/month on automated code reviews alone.

---

Practical Code Reasoning Test

Problem: "Implement an LRU cache with O(1) get and put operations."

GLM-5.1 Response

from collections import OrderedDict

class LRUCache:
    def __init__(self, capacity: int):
        self.cache = OrderedDict()
        self.capacity = capacity

    def get(self, key: int) -> int:
        if key not in self.cache:
            return -1
        self.cache.move_to_end(key)
        return self.cache[key]

    def put(self, key: int, value: int) -> None:
        if key in self.cache:
            self.cache.move_to_end(key)
        self.cache[key] = value
        if len(self.cache) > self.capacity:
            self.cache.popitem(last=False)

Clean, correct, uses the standard library. Both models solve this correctly ??for standard coding tasks, the quality difference rarely matters in practice.

---

Strengths and Weaknesses

GLM-5.1 Strengths

• Mathematical reasoning ??matches or beats Claude on MATH benchmarks
• Price ??~2??.4? cheaper depending on token mix (~1/3 total cost)
• 128K context ??handles large codebases and documents
• Chinese language ??native Chinese support is superior
• Available via AIWave ??single API key, no vendor lock-in

GLM-5.1 Weaknesses

• English prose quality ??slightly more formal than Claude
• Tool use ??Claude has more mature function-calling capabilities
• Context length ??128K vs Claude's 200K

Claude 4 Sonnet Strengths

• Consistent English prose ??gold standard for natural language generation
• Tool use and agentic workflows ??excels at multi-step reasoning with tools
• 200K context ??larger window for very long documents

Claude 4 Sonnet Weaknesses

• Price ??~3? more expensive overall
• Math ??GLM-5.1 slightly outperforms on mathematical benchmarks
• Vendor lock-in ??Anthropic-specific API

---

When to Use Each Model

Choose GLM-5.1 when:

• You're doing code generation or mathematical work
• Cost is a factor (which is almost always)
• You work with Chinese-language content
• You want 128K context at a reasonable price

Choose Claude 4 Sonnet when:

• English prose quality is the primary deliverable
• You need advanced tool use / function calling
• You're building safety-critical applications
• You need 200K+ context

Best strategy: Use both. Route GLM-5.1 for code, math, and bulk tasks. Reserve Claude for final prose polishing. With AIWave's single API key, you can switch models per-request.

---

Try It Yourself

curl https://aiwave.live/v1/chat/completions \
  -H "Authorization: Bearer sk-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.1",
    "messages": [{"role": "user", "content": "Implement a binary search tree with insert, search, and delete operations in Python."}],
    "max_tokens": 2048,
    "temperature": 0.3
  }'

Sign up for AIWave ??$5 free credit on signup. See pricing for all models.

---

Conclusion

GLM-5.1 delivers ~97% of Claude's benchmark performance at ~1/3 the cost. The 3-point HumanEval gap and 2-point MMLU gap are real but negligible in day-to-day coding. You will notice the ~$746/year savings.

Discussion: Has anyone here benchmarked GLM-5.1 against Claude in production? What use cases did you find where the quality gap actually mattered?