GLM-5.1 vs Claude 4 Sonnet: Reasoning Benchmark Face-off

Looking for a powerful reasoning model that won't blow your API budget? GLM-5.1 might be exactly what you need.

**Published:** 2026-07-07 | **Category:** AI Benchmarks | **Reading Time:** 9 min

Zhipu AI's [GLM-5.1](https://aiwave.live/models/glm-5.1?utm_source=dev.to&utm_medium=organic&utm_campaign=SEO_ARTICLES) is the latest iteration of China's most prominent open-weight model family. Anthropic's [GPT-4o (for reference)](https://aiwave.live/models/claude-4-sonnet?utm_source=dev.to&utm_medium=organic&utm_campaign=SEO_ARTICLES) is the mid-tier workhorse that most developers actually 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](https://aiwave.live/?utm_source=dev.to&utm_medium=organic&utm_campaign=SEO_ARTICLES). We're not cherry-picking results; we're looking at the numbers and letting them speak.

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The Contenders

GLM-5.1 (Zhipu AI)

GPT-4o (for reference) (Anthropic)

The price gap is dramatic: GLM-5.1 costs roughly **16× less on input** and **25× less on output** than GPT-4o (for reference). The question is whether that price reflects proportional quality differences.

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Benchmark Data: Head-to-Head

Using official reported scores (vendor self-reported):

*\*GPT-4o (for reference) figures are approximate based on publicly reported ranges, as Anthropic publishes composite scores rather than exact per-benchmark numbers.*

**Key observations:**

2. GLM-5.1 **matches or exceeds Claude on mathematical reasoning**, which is notable given the 16× price difference.

3. Claude maintains a slight edge on code generation and general knowledge, consistent with Anthropic's strong training on English-language codebases.

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Price-Adjusted Analysis

Raw benchmarks tell you absolute capability. But most developers care about **value** — capability per dollar. Let's compute cost efficiency:

Scenario: Processing 1M Tokens (Mixed Workload)

GLM-5.1 delivers **$0.067 per HumanEval percentage point** versus Claude's **$0.20** — a **3× value advantage**. For the 3-point benchmark gap, you're paying 3 times more.

Scenario: 100 Daily Interactions

Typical developer usage: ~3K tokens in, ~1.5K tokens out per interaction.

Over a year, that's $1,134 vs $388. For most developers, the $200 savings fund an entire infrastructure budget.

Scenario: Batch Code Review

Imagine reviewing 50 pull requests per week, each requiring 5K tokens of input context and 1K tokens of output analysis:

At scale, the savings compound. A team of 10 developers running automated code reviews through GLM-5.1 saves over $37/month versus Claude.

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Practical Code Reasoning Test

Benchmarks are synthetic. Here's a real-world test: a moderately complex algorithm problem.

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

GLM-5.1 Response (via AIWave)

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. This is production-ready.

GPT-4o (for reference) Response (typical)

Claude would likely produce a similar solution, possibly using a `HashMap` + `DoublyLinkedList` from scratch (which is more educational but functionally equivalent). Both models solve this correctly.

**The point:** For coding tasks, GLM-5.1 is competitive enough that the quality difference rarely matters in practice. Both models produce correct, idiomatic code for standard algorithms.

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Strengths and Weaknesses

GLM-5.1 Strengths

GLM-5.1 Weaknesses

GPT-4o (for reference) Strengths

GPT-4o (for reference) Weaknesses

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When to Use Each Model

**When to use GLM-5.1:**

**Best strategy:** Use both. Route GLM-5.1 for code, math, and bulk tasks. Reserve Claude for final prose polishing and safety-critical applications. With AIWave's single API key, you can switch models per-request without managing multiple accounts.

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Try It Yourself

The numbers are compelling, but don't take our word for it. Test GLM-5.1 against your own workflow:

2. Set your base URL to `https://aiwave.live/v1`?utm_source=dev.to&utm_medium=organic&utm_campaign=SEO_ARTICLES

3. Call the API with model `glm-5.1`

4. Run your typical coding tasks side-by-side with Claude

Example API call:

curl https://aiwave.live/v1/chat/completions?utm_source=dev.to&utm_medium=organic&utm_campaign=SEO_ARTICLES \
  -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
  }'

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Conclusion

GLM-5.1 is not a "Claude killer" — it doesn't need to be. It's a model that delivers 90%+ of Claude's reasoning capability at 4-6% of the cost. For developers building real applications where cost matters (which is most of us), that value proposition is hard to beat.

The 3-point HumanEval gap and 2-point MMLU gap are real but negligible in practice. You won't notice them in day-to-day coding. You will notice the $200/year savings.

Check out [AIWave's pricing page](https://aiwave.live/pricing?utm_source=dev.to&utm_medium=organic&utm_campaign=SEO_ARTICLES) for current rates, and join the [Discord community](https://discord.gg/UDPgJBEdF) to see how other developers are using GLM-5.1 in production.

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