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StepFun: Step 3.5 Flash vs Z.ai: GLM 5: Coding Performance with 10 Evaluators

We analyze the coding capabilities of StepFun: Step 3.5 Flash vs Z.ai: GLM 5 through a rigorous assessment by 10 independent evaluators.

StepFun: Step 3.5 Flash

4.2

/ 10

vs

Z.ai: GLM 5

5.8

/ 10

Key Findings

Overall PerformanceZ.ai: GLM 5

Z.ai: GLM 5 achieved the top rank with an overall score of 5.79.

Instruction FollowingZ.ai: GLM 5

Demonstrated superior capability in following complex coding requirements compared to Step 3.5 Flash.

Cost EfficiencyStepFun: Step 3.5 Flash

Provides a significantly lower cost per output token at $0.000304.

Specifications

SpecStepFun: Step 3.5 FlashZ.ai: GLM 5
Providerstepfunz-ai
Context Length256K80K
Input Price (per 1M tokens)$0.10$0.72
Output Price (per 1M tokens)$0.30$2.30
Max Output Tokens256,000131,072
Tierstandardstandard

Our Verdict

Z.ai: GLM 5 is the superior model for high-stakes coding performance, outperforming StepFun: Step 3.5 Flash in both accuracy and instruction following. While StepFun: Step 3.5 Flash offers a more cost-effective solution, Z.ai: GLM 5 justifies its higher cost through consistently higher quality outputs in the 10-evaluator coding suite.

Overview

In the rapidly evolving landscape of large language models, choosing the right architecture for software development tasks is critical. This report provides a detailed comparison of StepFun: Step 3.5 Flash vs Z.ai: GLM 5, specifically focusing on their Coding Performance with 10 Evaluators. By utilizing PeerLM's comparative evaluation methodology, we move beyond static benchmarks to understand how these models perform in real-world coding scenarios as perceived by expert human reviewers.

Benchmark Results

The evaluation involved a head-to-head ranking process where 10 evaluators assessed the models based on accuracy and instruction following. Z.ai: GLM 5 emerged as the top-performing model in this specific suite.

ModelOverall ScoreAccuracyInstruction Following
Z.ai: GLM 55.795.795.79
StepFun: Step 3.5 Flash4.214.214.21

Criteria Breakdown

The evaluation focused on two core pillars essential for coding assistants: Accuracy and Instruction Following. In coding, accuracy represents the model's ability to generate bug-free, functional code, while instruction following measures its adherence to complex architectural requirements and constraints provided in the prompts. Z.ai: GLM 5 demonstrated a superior grasp of these requirements, achieving a score spread of 1.58 over its competitor.

Cost & Latency

Understanding the economic impact of model selection is vital for scaling development environments. Below is a breakdown of the costs associated with the evaluation run.

  • Z.ai: GLM 5: Total cost of $0.009623 with a cost per output token of $0.002465.
  • StepFun: Step 3.5 Flash: Total cost of $0.007501 with a cost per output token of $0.000304.

While StepFun: Step 3.5 Flash offers a more budget-friendly cost per output token, Z.ai: GLM 5 provides a higher tier of performance that may justify the premium for mission-critical coding tasks.

Use Cases

Z.ai: GLM 5 is ideally suited for complex software engineering tasks, such as refactoring legacy codebases, generating boilerplate for enterprise applications, and solving complex algorithmic challenges where precision is paramount. StepFun: Step 3.5 Flash serves as an excellent candidate for high-volume, lightweight coding tasks, rapid prototyping, and scenarios where cost-efficiency is prioritized over maximum reasoning depth.

Verdict

The comparative analysis between StepFun: Step 3.5 Flash vs Z.ai: GLM 5 highlights the trade-offs between specialized coding performance and operational expenditure. For developers requiring the highest standard of code quality and instruction adherence, Z.ai: GLM 5 is the clear choice. However, for organizations looking to optimize their token spend without sacrificing baseline utility, StepFun: Step 3.5 Flash remains a highly competitive and efficient alternative.

Backed by real data

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Methodology

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