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DeepSeek: R1 vs Z.ai: GLM 5: Coding Performance with 10 Evaluators

In our latest benchmark for Coding Performance with 10 Evaluators, we compare DeepSeek: R1 against Z.ai: GLM 5 to determine which model leads in real-world development tasks.

DeepSeek: R1

2.9

preference score

vs

Z.ai: GLM 5

7.1

preference score

Judges ranked the responses in this Run against each other; the rank is mapped onto a 0–10 scale. It shows which response was preferred, not how good either one is — and it is not a percentage, a pass rate, or a check that the output was correct.

Sample size for this comparison was not recorded. Treat it as directional.

Evidence clarification: this article predates recorded sample provenance. Treat its conclusions as claims about the displayed examples; they do not establish general model superiority, verified correctness, or production suitability.

Key Findings

Overall PerformanceZ.ai: GLM 5

Z.ai: GLM 5 secured the top rank with an overall score of 7.11.

Cost EfficiencyZ.ai: GLM 5

Z.ai: GLM 5 proved significantly cheaper, costing roughly 65% less than DeepSeek: R1.

Accuracy & ComplianceZ.ai: GLM 5

Evaluators consistently rated Z.ai: GLM 5 higher in both accuracy and instruction following.

Specifications

SpecDeepSeek: R1Z.ai: GLM 5
Providerdeepseekz-ai
Context Length64K205K
Input Price (per 1M tokens)$0.70$0.60
Output Price (per 1M tokens)$2.50$1.92
Max Output Tokens16,000128,000
Tierstandardstandard

Our Verdict

Z.ai: GLM 5 is the clear winner for coding-related tasks, outperforming DeepSeek: R1 in both accuracy and cost efficiency. While DeepSeek: R1 generated more extensive output tokens, it failed to match the quality and precision required by our 10 evaluators. For projects prioritizing high-quality code generation at a lower operational cost, Z.ai: GLM 5 is the recommended choice.

Overview

As the demand for AI-assisted software development grows, choosing the right model for your codebase is critical. In this evaluation, we compare DeepSeek: R1 vs Z.ai: GLM 5 within the context of Coding Performance with 10 Evaluators. PeerLM’s comparative analysis focuses on how these models handle complex coding prompts, measuring their ability to provide accurate, instruction-compliant solutions.

Benchmark Results

Our comparative evaluation, conducted by 10 independent evaluators, reveals a significant performance gap between the two models. Z.ai: GLM 5 emerges as the clear leader in this specific coding suite.

ModelRankOverall ScoreAccuracyInstruction Following
Z.ai: GLM 517.117.117.11
DeepSeek: R122.892.892.89

Criteria Breakdown

The evaluation centered on two primary pillars: Accuracy and Instruction Following. In coding tasks, these metrics are vital for ensuring that the generated code is not only syntactically correct but also adheres strictly to the provided architectural requirements. Z.ai: GLM 5 demonstrated superior alignment with evaluator expectations, achieving a score of 7.11 compared to DeepSeek: R1's score of 2.89.

Cost & Latency

Beyond raw performance, cost efficiency is a major factor for teams scaling AI-integrated development environments. The table below highlights the economic footprint of each model during the benchmark run.

ModelTotal Cost (USD)Avg Completion TokensCost per Output Token
Z.ai: GLM 50.0096239760.002465
DeepSeek: R10.0277192,7120.002556

Z.ai: GLM 5 is significantly more cost-effective, with a total cost of $0.009623 for the test set, compared to the $0.027719 incurred by DeepSeek: R1. Furthermore, DeepSeek: R1 generated significantly longer completions on average, which may account for its higher total operational cost in this coding suite.

Use Cases

  • Z.ai: GLM 5: Best suited for enterprise-grade coding assistants where high accuracy and low cost-per-request are non-negotiable requirements.
  • DeepSeek: R1: May be better suited for experimental tasks or scenarios where longer, more verbose reasoning paths are preferred over concise code generation.

Verdict

When analyzing DeepSeek: R1 vs Z.ai: GLM 5 for coding tasks, Z.ai: GLM 5 provides a more efficient and accurate experience. With a higher overall score and a lower cost profile, it stands out as the top performer for developers requiring reliable code generation.

Backed by real data

View the Full Evaluation Report

See every response, score, and evaluator judgment behind this comparison. All data from PeerLM's blind evaluation pipeline.

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Methodology

Evaluated using PeerLM's blind evaluation pipeline with 4 responses per model across 2 criteria.