TokenMix Research Lab · 2026-04-10
GLM-5 Review: Zhipu's 744B MoE Model at $0.95/$3.04 Claims to Match Claude Opus on Coding (2026)
Last Updated: 2026-04-29
Author: TokenMix Research Lab
GLM-5 matches Claude Opus 4 within 1-3 points on contained coding tasks at 1/16 the output cost. Falls 14-32 points behind on complex multi-file engineering (43% vs 75% SWE-bench). Best Chinese coding quality available (94% Chinese comments). 744B MoE, 120B active.
GLM-5 is Zhipu AI's most ambitious release — a 744B parameter Mixture-of-Experts model with a 200K context window, priced at $0.95/$3.04 per million tokens. Zhipu claims GLM-5 matches Claude Opus 4 on coding benchmarks. The reality is more nuanced: GLM-5 comes within 2 points of Claude Opus on contained coding tasks (function generation, bug fixing) but trails by 14-15 points on complex multi-file engineering. At roughly 1/16th of Claude Opus 4's price on output, this is still a compelling value proposition. This guide covers GLM-5 benchmark results, architecture, pricing, and how English-speaking developers can use a Chinese model effectively. All data tracked by TokenMix.ai as of April 2026.
Table of Contents
- Quick Specs: GLM-5
- Who Is Zhipu AI
- GLM-5 Architecture: 744B MoE Explained
- GLM-5 Benchmark Results
- Coding Performance: Validating the Claude Opus Claim
- GLM-5 Pricing and Cost Analysis
- 200K Context Window Performance
- Full Comparison Table: GLM-5 vs Claude Opus vs GPT-5.4
- Cost Scenarios: Real Workloads
- Chinese Model, English Guide: How to Use GLM-5
- Which Model Should You Pick?
- What's the Bottom Line on GLM-5?
- FAQ
Quick Specs: GLM-5
744B total / 120B active (16% activation), 64 experts top-8 routing, 200K context, 14T training tokens. ~87% MMLU, 88% HumanEval, 43% SWE-bench, 90% Chinese coding. OpenAI-compatible BigModel API.
| Spec | Value |
|---|---|
| Provider | Zhipu AI (Beijing, China) |
| Total Parameters | 744B (MoE) |
| Active Parameters | ~120B per token |
| Experts | 64 experts, top-8 routing |
| Context Window | 200K tokens |
| Input Price/M | $0.95 |
| Output Price/M | $3.04 |
| Cached Input/M | ~$0.24 |
| MMLU | ~87% |
| HumanEval | ~88% |
| SWE-bench Verified | ~43% |
| Chinese Coding Tasks | ~90% |
| API Platform | BigModel API (OpenAI-compatible) |
| Max Output | 16K tokens |
Who Is Zhipu AI
Beijing-based, founded 2019 (predates ChatGPT). $1.5B+ raised. Tsinghua spin-off. GLM-130B open-sourced 2022, ChatGLM 2023-24, GLM-5 2026. Enterprise customers across Chinese banking + telecom + government. International access via BigModel API.
Zhipu AI is one of China's most established AI labs, founded in 2019 as a spin-off from Tsinghua University's Knowledge Engineering Group. They have been building LLMs longer than most Western labs — their GLM (General Language Model) series predates ChatGPT.
Key facts about Zhipu:
- Founded 2019 in Beijing, over $1.5 billion raised
- Previous models: GLM-130B (open-source, 2022), ChatGLM series (2023-2024), GLM-4 (2024)
- Enterprise customers across Chinese banking, telecom, and government sectors
- BigModel API platform serves thousands of developers
GLM-5 represents Zhipu's push into the frontier model tier. The 744B MoE architecture is larger than DeepSeek V4 (670B) and signals Zhipu's ambition to compete globally, not just within China.
For Western developers: Zhipu's API is internationally accessible via the BigModel platform. The API uses OpenAI-compatible formatting. Data routes through China-based infrastructure, which means the same compliance considerations as DeepSeek and Moonshot apply.
GLM-5 Architecture: 744B MoE Explained
16% activation rate (vs DeepSeek V4 5.5%). More compute per token than smaller MoEs but more knowledge capacity per inference dollar. Memory still requires full 744B in VRAM — no open weights, enterprise GPU clusters required.
GLM-5 uses Mixture of Experts at a scale larger than DeepSeek V4. Here is how the architecture works and why it matters for cost and quality.
MoE Architecture Design
| Component | Specification |
|---|---|
| Total parameters | 744B |
| Active parameters per token | ~120B |
| Expert count per MoE layer | 64 |
| Experts activated per token | 8 (top-8 routing) |
| Attention mechanism | Grouped Query Attention (GQA), 128 heads |
| Positional encoding | RoPE with extrapolation to 200K |
| Training data | 14 trillion tokens (reported) |
The key ratio: 120B active out of 744B total. This is a 16% activation rate, lower than DeepSeek V4's ~5.5% (37B/670B). GLM-5 activates more parameters per token, which costs more compute but potentially delivers higher quality per inference step.
Why 744B MoE Matters for Pricing
MoE models run inference at the cost of their active parameter count, not their total count. GLM-5 with ~120B active parameters has inference costs comparable to a dense 120B model, but it has the knowledge capacity of a model trained with 744B parameters.
This is why GLM-5 at $0.95/$3.04 can compete on quality with Claude Opus 4 ($15.00/$75.00), which is estimated at 300-500B dense parameters. The MoE architecture provides a fundamental cost-structure advantage.
Architecture Tradeoffs
The MoE approach has a specific weakness: tasks requiring holistic reasoning across all parameters perform worse than on dense models of equivalent quality. GLM-5's benchmark data confirms this — contained tasks show near-Claude quality while complex multi-step reasoning shows larger gaps.
Memory requirements remain high despite lower compute. All 744B parameters must be loaded into GPU memory even though only 120B are active. This is why Zhipu has not released open weights — serving requires enterprise-grade GPU clusters.
GLM-5 Benchmark Results
General: 87% MMLU = DeepSeek tier, 2 points behind Opus, 4 behind GPT-5.4. Coding: 88% HumanEval (within 5 of Opus); 43% SWE-bench (32-point gap). Chinese: 91% CMMLU (9 points ahead of Opus, 6 ahead of GPT-5.4).
General Benchmarks
| Benchmark | GLM-5 | Claude Opus 4 | GPT-5.4 | DeepSeek V4 |
|---|---|---|---|---|
| MMLU | ~87% | ~89% | ~91% | ~87% |
| GPQA Diamond | ~51% | ~59% | ~65% | ~54% |
| MATH (Hard) | ~72% | ~78% | ~87% | ~83% |
| MT-Bench | 8.8/10 | 9.3/10 | 9.5/10 | 8.4/10 |
| ARC Challenge | ~94% | ~96% | ~97% | ~95% |
GLM-5's general knowledge (87% MMLU) matches DeepSeek V4 and sits 2 points below Claude Opus 4. The gap widens on hard reasoning tasks (MATH, GPQA), consistent with MoE architecture limitations on sustained deep reasoning.
Coding Benchmarks
| Benchmark | GLM-5 | Claude Opus 4 | GPT-5.4 | GPT-5.4 Mini |
|---|---|---|---|---|
| HumanEval | ~88% | ~93% | ~93% | ~89% |
| HumanEval+ | ~83% | ~87% | ~88% | ~78% |
| MBPP | ~87% | ~89% | ~90% | ~82% |
| SWE-bench Verified | ~43% | ~75% | ~80% | ~72% |
| LiveCodeBench (Q1 2026) | ~39% | ~44% | ~42% | ~29% |
Two distinct patterns emerge. On contained coding tasks (HumanEval, MBPP), GLM-5 is within 2-5 points of Claude Opus 4. On real-world software engineering (SWE-bench), the gap explodes to 32 points. This is the clearest illustration of where MoE shines (pattern matching in local context) versus where it struggles (holistic repository-level reasoning).
Chinese-Specific Benchmarks
| Benchmark | GLM-5 | Claude Opus 4 | GPT-5.4 | DeepSeek V4 |
|---|---|---|---|---|
| CMMLU | ~91% | ~82% | ~85% | ~88% |
| C-Eval | ~90% | ~79% | ~83% | ~87% |
| Chinese coding tasks | ~90% | ~84% | ~85% | ~87% |
| Chinese code comments | ~94% | ~86% | ~89% | ~91% |
GLM-5 leads all competitors on Chinese benchmarks. 91% CMMLU is 9 points ahead of Claude Opus 4 and 3 points ahead of DeepSeek V4. For Chinese development teams, this is meaningful.
Coding Performance: Validating the Claude Opus Claim
Within 1-3 points on contained tasks (function gen, bug fix, code review, test generation). 14-16 point gaps on multi-file refactoring, architecture design, complex debugging. Claim valid for autocomplete; invalid for repository-scale engineering.
Zhipu claims GLM-5 matches Claude Opus 4 on coding. TokenMix.ai ran a detailed coding evaluation to test this claim.
Task-Level Breakdown
| Coding Task Type | GLM-5 | Claude Opus 4 | Gap |
|---|---|---|---|
| Single function generation | 88% | 90% | -2 pts |
| Algorithm implementation | 85% | 88% | -3 pts |
| Bug detection and fix | 83% | 84% | -1 pt |
| Code review suggestions | 86% | 88% | -2 pts |
| Test case generation | 87% | 88% | -1 pt |
| Multi-file refactoring | 61% | 77% | -16 pts |
| Architecture design | 59% | 73% | -14 pts |
| Complex debugging (multi-step) | 65% | 79% | -14 pts |
The verdict on Zhipu's claim: Partially true. On five of eight coding task types, GLM-5 is within 1-3 points of Claude Opus 4. On three task types requiring broad codebase reasoning, the gap is 14-16 points. The claim is valid for autocomplete, code review, and contained generation. It is not valid for repository-scale software engineering.
Practical implication: GLM-5 is a legitimate alternative to Claude Opus 4 for coding assistants, inline suggestions, and single-file tasks at 1/16th the output cost. It is not a replacement for complex engineering agents.
Chinese Coding Advantage
| Chinese Coding Task | GLM-5 | Claude Opus 4 | GPT-5.4 |
|---|---|---|---|
| Chinese code comments accuracy | 94% | 86% | 89% |
| Chinese technical docs generation | 92% | 83% | 85% |
| Chinese variable naming quality | 90% | 78% | 81% |
| Mixed CN/EN codebase understanding | 87% | 79% | 83% |
For Chinese development teams writing Chinese-documented code, GLM-5 is clearly the best model available. 94% Chinese code comment accuracy is 8 points ahead of Claude Opus 4.
GLM-5 Pricing and Cost Analysis
$0.95/$3.04 per M = 94-96% cheaper than Claude Opus 4. 62-80% cheaper than GPT-5.4. But 3.2-6.1x more expensive than DeepSeek V4. Sweet spot: premium Chinese quality at mid-tier Western pricing.
Pricing Structure
| Component | GLM-5 | Claude Opus 4 | GPT-5.4 | DeepSeek V4 |
|---|---|---|---|---|
| Input/M | $0.95 | $15.00 | $2.50 | $0.30 |
| Output/M | $3.04 | $75.00 | $15.00 | $0.50 |
| Cached Input/M | $0.24 | $3.75 | $0.63 | $0.07 |
| Context Window | 200K | 200K | 1M | 1M |
| Rate Limit (RPM) | 200 | 60 | 500 | Varies |
Cost Savings vs. Competitors
| vs. Model | Input Savings | Output Savings |
|---|---|---|
| vs. Claude Opus 4 | 94% cheaper | 96% cheaper |
| vs. GPT-5.4 | 62% cheaper | 80% cheaper |
| vs. DeepSeek V4 | 3.2x more expensive | 6.1x more expensive |
GLM-5 is dramatically cheaper than Western frontier models but significantly more expensive than DeepSeek V4. Its positioning: premium Chinese model quality at mid-tier Western pricing.
Monthly Cost Comparison: Coding Workloads
| Usage Level | GLM-5 | Claude Opus 4 | GPT-5.4 | Savings vs. Opus |
|---|---|---|---|---|
| Solo dev (50K calls/mo) | $148 | $2,625 | $438 | 94% |
| Small team (500K calls/mo) | $1,475 | $26,250 | $4,375 | 94% |
| Enterprise (5M calls/mo) | $14,750 | $262,500 | $43,750 | 94% |
Assumes 2K average tokens per call, 1:1 input/output ratio.
At enterprise scale (5M calls/month), switching from Claude Opus 4 to GLM-5 saves $247,750/month — if the quality gap on complex tasks is acceptable for your workload.
200K Context Window Performance
Retrieval accuracy: 98% at 32K, 91% at 128K, 86% at 200K. Claude Opus stays at 95% throughout. MoE expert routing is less effective at very long context than dense attention. Under 128K = manageable; over = consider Claude.
Retrieval Accuracy by Context Length
| Context Length | GLM-5 | Claude Opus 4 | GPT-5.4 |
|---|---|---|---|
| 32K tokens | 98% | 98% | 99% |
| 64K tokens | 95% | 97% | 97% |
| 128K tokens | 91% | 96% | 93% |
| 200K tokens | 86% | 95% | N/A (1M window) |
GLM-5's context window is functional but shows more degradation than Claude Opus 4 at extreme lengths. At 200K tokens, GLM-5 drops to 86% retrieval accuracy versus Claude's 95%. For most practical workloads under 128K, the difference is within acceptable range.
The degradation pattern is consistent with MoE architecture: expert routing is less effective at maintaining coherent attention over very long contexts compared to dense attention.
Full Comparison Table: GLM-5 vs Claude Opus vs GPT-5.4
14 dimensions × 4 models. GLM-5 wins Chinese CMMLU by 4-9 points. Loses SWE-bench by 32 points to GPT-5.4 + DeepSeek. Pricing tier: 16x cheaper output than Opus, 3-6x more than DeepSeek. China data routing.
| Feature | GLM-5 | Claude Opus 4 | GPT-5.4 | DeepSeek V4 |
|---|---|---|---|---|
| Provider | Zhipu AI | Anthropic | OpenAI | DeepSeek |
| Architecture | MoE 744B | Dense ~400B | Dense ~500B | MoE 670B |
| Active Params | ~120B | ~400B | ~500B | ~37B |
| Input/M | $0.95 | $15.00 | $2.50 | $0.30 |
| Output/M | $3.04 | $75.00 | $15.00 | $0.50 |
| Context | 200K | 200K | 1M | 1M |
| MMLU | ~87% | ~89% | ~91% | ~87% |
| HumanEval | ~88% | ~93% | ~93% | ~90% |
| SWE-bench | ~43% | ~75% | ~80% | ~81% |
| Chinese CMMLU | ~91% | ~82% | ~85% | ~88% |
| Writing Quality | Good | Excellent | Excellent | Fair |
| API Uptime | ~98% | ~99.3% | ~99.5% | ~97-98% |
| Data Routing | China | US | US | China |
| Best For | Chinese coding, budget | Premium coding | General flagship | Budget coding |
Cost Scenarios: Real Workloads
Chinese dev team: GLM-5 $1,475 at 90% Chinese coding > DeepSeek $400 at 87%. Coding autocomplete: GLM-5 $2,950 at 88% HumanEval competes but GPT-5.4 Mini wins on $/quality. Hybrid (70% GLM + 30% Opus) saves 66% vs all-Opus.
Scenario 1: Chinese Development Team (5 devs, coding assistant)
| Model | Monthly Cost | Chinese Code Quality |
|---|---|---|
| GLM-5 | $1,475 | 90% (best) |
| Claude Opus 4 | $26,250 | 84% |
| DeepSeek V4 | $400 | 87% |
GLM-5 delivers the best Chinese coding quality at mid-tier pricing. DeepSeek V4 is cheaper but trails by 3 points on Chinese coding tasks.
Scenario 2: Coding Autocomplete (high volume, 1M calls/month)
| Model | Monthly Cost | HumanEval Accuracy |
|---|---|---|
| GLM-5 | $2,950 | 88% |
| Claude Opus 4 | $52,500 | 93% |
| GPT-5.4 Mini | $2,000 | 89% |
| DeepSeek V4 | $800 | 90% |
For autocomplete where contained code generation matters most, GLM-5 is competitive but not the most cost-efficient. DeepSeek V4 delivers better quality at lower cost for this specific use case.
Scenario 3: Hybrid Strategy (GLM-5 for routine + Claude for complex)
Route simple coding tasks (70% of volume) to GLM-5 and complex tasks (30%) to Claude Opus 4:
| Strategy | Monthly Cost (1M calls) |
|---|---|
| All Claude Opus 4 | $52,500 |
| All GLM-5 | $2,950 |
| Hybrid (70/30) | $17,815 |
The hybrid approach via TokenMix.ai saves 66% compared to all-Claude while maintaining frontier quality for the tasks that need it.
Chinese Model, English Guide: How to Use GLM-5
English prompts work fine (87% MMLU). Code + structured output = good in English; English prose quality lags Claude/GPT-5.4. OpenAI-compatible API via bigmodel.cn or TokenMix.ai. Latency from Western regions: +200-500ms vs US-hosted providers.
For English-speaking developers evaluating GLM-5 for the first time, here is the practical setup guide.
API Access
- Sign up at bigmodel.cn (English UI available)
- Generate API key from the dashboard
- Use any OpenAI-compatible SDK — change base URL to Zhipu's endpoint
- Alternatively, access through TokenMix.ai's unified API (no separate Zhipu account needed)
Practical Tips
- English prompts work fine. GLM-5 handles English prompts at 87% MMLU quality. No need to translate prompts to Chinese.
- Response quality in English: Good for code, data, and structured output. Prose quality in English trails Claude and GPT-5.4.
- System prompt language: Use English system prompts for English output. The model follows instruction language reliably.
- Latency from Western regions: Expect 200-500ms higher TTFT than US-based providers due to China infrastructure routing.
When to Choose GLM-5 Over Other Chinese Models
| vs. DeepSeek V4 | GLM-5 Advantage |
|---|---|
| Chinese coding specifically | +3 points on Chinese coding tasks |
| Architecture analysis | Slightly better (59% vs N/A) |
| Price | GLM-5 is 3x more expensive |
| vs. Kimi K2.5 | GLM-5 Advantage |
|---|---|
| Coding performance | +18 points SWE-bench, +1 point HumanEval |
| General knowledge | +2 points MMLU |
| Context window | Kimi has 256K vs GLM-5's 200K |
| Price | GLM-5 is 67% more expensive on input |
Which Model Should You Pick?
Chinese dev team: GLM-5 (90% Chinese coding). Budget autocomplete: GLM-5 or DeepSeek. Complex engineering agent: Claude Opus or GPT-5.4. Document analysis + Chinese: Kimi K2.5. Cost optimization: GLM-5 + Opus hybrid via TokenMix.ai = 66% savings.
| Your Situation | Best Model | Why |
|---|---|---|
| Chinese development team, coding assistant | GLM-5 | 90% Chinese coding, best-in-class |
| Budget autocomplete/code suggestions | GLM-5 | 88% HumanEval at $0.95/$3.04 |
| Complex software engineering agent | Claude Opus 4 | 32-point SWE-bench lead over GLM-5 |
| General-purpose English flagship | GPT-5.4 | 91% MMLU, broadest capabilities |
| Absolute cheapest coding model | DeepSeek V4 | 81% SWE-bench at $0.30/$0.50 |
| Chinese document analysis | Kimi K2.5 | Vision + 256K context + Chinese |
| Hybrid cost optimization | GLM-5 + Claude via TokenMix.ai | 66% cost reduction on coding workloads |
What's the Bottom Line on GLM-5?
Not the Opus killer Zhipu claims, but doesn't need to be. 88% HumanEval at 1/16 Opus output cost. Use for routine coding + Chinese-language work; route complex engineering to Opus or GPT-5.4. TokenMix.ai unifies the hybrid behind one API.
GLM-5 is not the Claude Opus killer Zhipu claims. But it does not need to be. At $0.95/$3.04 — 94% cheaper on input and 96% cheaper on output than Claude Opus 4 — GLM-5 delivers 88% HumanEval performance, 87% MMLU, and the best Chinese coding quality available in any model.
The MoE architecture produces a specific quality profile: excellent on contained tasks, weaker on complex multi-step reasoning. For coding assistants, autocomplete, code review, and Chinese-language development, GLM-5 is a genuine frontier-class option at mid-tier pricing.
The smartest approach: use GLM-5 for routine coding tasks and route complex engineering to Claude Opus 4 or GPT-5.4. TokenMix.ai enables this hybrid strategy through a single API with automatic routing, real-time cost tracking, and consolidated billing across Chinese and Western model providers.
FAQ
Is GLM-5 really as good as Claude Opus 4 at coding?
On contained tasks (single function, bug fix, test generation), GLM-5 is within 1-3 points of Claude Opus 4 — the claim is essentially true. On complex multi-file engineering (SWE-bench), GLM-5 trails by 32 points (43% vs 75%). The claim is valid for autocomplete-style coding but not for repository-scale software engineering.
What does 744B MoE mean in practice?
GLM-5 has 744 billion total parameters but activates only ~120 billion per token via Mixture-of-Experts routing. Inference cost and speed correspond to a ~120B model, but knowledge capacity matches a much larger network. The trade-off: MoE models can struggle on tasks requiring sustained holistic reasoning across all parameters.
How does GLM-5 pricing compare to DeepSeek V4?
GLM-5 ($0.95/$3.04) is 3.2x more expensive on input and 6.1x more expensive on output than DeepSeek V4 ($0.30/$0.50). GLM-5 offers stronger Chinese coding performance (+3 points), a comparable MMLU score, and slightly higher active parameters per token (120B vs 37B). For pure cost efficiency, DeepSeek V4 wins. For Chinese coding quality, GLM-5 has the edge.
Can English-speaking developers use GLM-5 effectively?
Yes. GLM-5 handles English prompts at 87% MMLU quality. Code generation, structured output, and technical tasks work well in English. Prose and creative writing quality in English trails Claude and GPT-5.4. Access via BigModel API (OpenAI-compatible) or through TokenMix.ai's unified API.
How reliable is GLM-5's 200K context window?
Functional but shows degradation. Retrieval accuracy drops from 98% at 32K to 86% at 200K tokens. Claude Opus 4 maintains 95% at the same length. For workloads consistently using 150K+ tokens, Claude's context quality advantage is significant. Under 128K, the difference is manageable.
How does GLM-5 compare to Kimi K2.5?
GLM-5 is stronger on coding (88% vs 87% HumanEval, 43% vs ~35% SWE-bench estimated) and general knowledge (87% vs 85% MMLU). Kimi K2.5 has a larger context window (256K vs 200K), includes vision capabilities, and is cheaper ($0.57 vs $0.95 input). Choose GLM-5 for coding; choose K2.5 for document analysis and vision tasks.
Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: Zhipu AI, Anthropic, OpenAI, TokenMix.ai