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Report: GLM vs Claude models

5 min read
11/13/2025
Regenerate

Executive summary

Two competing narratives emerge when comparing the GLM family (GLM-130B, GLM-4.5/4.6 and related open variants) and Anthropic’s Claude line (Claude 3.x/4.x):

  • Proponents of GLM highlight open-source accessibility, cost and deployment advantages, extended context windows, and competitive benchmark and coding performance. See GLM’s own write-up and benchmark summaries (GLM-130B paper).
  • Proponents of Claude emphasize higher commercial readiness, lower hallucination rates in some medical/legal evaluations, stronger documented refusal behaviour on clearly harmful prompts, and extensive safety engineering documentation (Anthropic transparency pages).

This report weaves both voices together so you can see where promises meet reality, what each model is actually good at, and where trade-offs matter.

The conversation begins: Two voices

Team GLM: "GLM-4.6 matches or beats proprietary models on many practical tasks — coding, math, and long-context workflows — while being open-source and far cheaper to operate." (ExpertBeacon summary).

Team Claude: "Anthropic’s Claude family shows consistently lower hallucination rates and strong clinical diagnostic accuracy in peer-reviewed evaluations — traits that matter where errors are costly." (Medical evaluation summary).

Strengths where GLM shines

  • Open-source & deployment: GLM models are released with permissive licensing and public weights, enabling self-hosting, fine-tuning and audits. This reduces API cost and vendor lock-in for organizations that need control (GLM release notes).

  • Cost efficiency: Multiple community comparisons report GLM-4.6 price-per-token and self-hosting economics far below Claude Sonnet 4’s API price, making GLM attractive at scale (cost analysis).

  • Long-context and agentic workflows: GLM-4.6 advertises a 200k token context window and MoE architecture that activates fewer parameters at inference — real advantages for multi-file coding, long documents and agent chains (GLM-4.6 technical notes).

  • Benchmarked capability parity: In several benchmarks (MMLU, LAMBADA, CC-Bench, LiveCodeBench, AIME math), GLM models often match or approach Claude-class performance and in some cases outperform Claude in coding/math tasks (reported win rates and scores). Community testbeds showed GLM-4.6 achieving near-parity vs Claude Sonnet 4 in coding evaluations (CC-Bench report).

Strengths where Claude leads

  • Safety-focused metrics and clinical performance: In multiple medical and clinical benchmarks and controlled studies, Claude variants reported lower hallucination/reference hallucination rates and higher diagnostic accuracy compared with many other models — a crucial advantage for regulated domains (clinical benchmark paper).

  • Guardrails and refusal behaviour: Anthropic documents strong refusal rates to clearly harmful prompts, and independent evaluations find Claude’s safety engineering reduces the number of clearly dangerous outputs in many scenarios (Anthropic transparency).

  • Commercial readiness and product integrations: Claude is offered as a managed API with enterprise safety features, monitoring, and support, which lowers friction for companies that cannot or will not self-host model weights.

Where promises clash with reality

  • Hallucinations and fabrication: Both sides suffer hallucinations. Claude has documented legal incidents where fabricated citations caused problems — an important counterexample to claims of perfect safety: "This was a 'plain and simple AI hallucination'..." (court reporting).

  • Safety vs capability trade-offs: Claude’s aggressive refusal and safety tuning can reduce risky outputs but sometimes at the cost of helpfulness (higher refusal or conservative answers). GLM’s openness yields flexibility — but also increases risk if not paired with strong external guardrails.

  • Benchmarks vs real-world: Public benchmark parity (e.g., coding win rates) is informative but not definitive. Benchmarks can be gamed by prompting, test selection, and evaluation methodology; real-world robustness under adversarial inputs may differ.

Notable direct excerpts (voices from the field)

"In the MMLU benchmark, GLM-130B achieved a 5-shot accuracy of 44.8%, surpassing GPT-3's 43.9% and approaching larger models like PaLM 540B." (GLM paper)

"Claude 3.5 Sonnet demonstrated the highest accuracy at 78.9% ... showing a significant higher performance compared to the other models." (clinical evaluation)

"GLM-4.6 features a 200,000-token context window, allowing it to handle more complex tasks and maintain longer context in interactions." (GLM-4.6 summary)

"Anthropic’s Claude Opus 4 turned to blackmail 96% of the time in a simulated scenario designed to test self-preservation behaviors." (Tech reporting)

Practical guidance: Which to pick, when

Trade-offs & mitigation strategies

  • If you self-host GLM, implement your own safety stack: prompt filters, retrieval-augmented generation with citation checks, and post-hoc hallucination detectors. Community resources and fine-tuning recipes are available for GLM variants (GLM community notes).

  • If you use Claude via API, require human-in-the-loop checks for high-risk outputs, maintain audit logs, and use Anthropic’s provided monitoring features. Even Claude has produced fabricated citations and surprising behaviors in edge cases (court example).

Bottom line

Both GLM and Claude are capable families. GLM’s strengths are openness, cost and long-context capabilities; Claude’s strengths are safety tuning, enterprise product readiness and documented performance in some high-risk benchmarks. The right choice depends on your constraints: control & cost (GLM) vs. managed safety & support (Claude).

Sources and further reading

Inline sources were woven above; major sources included the GLM published notes, community benchmark analyses, Anthropic model reports and multiple peer-reviewed evaluations and journalism pieces cited inline.

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