4.5 Haiku V/S GPT-5 Mini & GLM-4.6 : Which is the best SMALL & CHEAP CODING MODEL?

Key Concepts

• Mid-tier Coding Models: AI models designed for everyday development tasks, balancing speed, cost, and performance, as opposed to high-end, more expensive frontier models.
• Tool Calling: The ability of an AI model to interact with external tools or functions, a critical feature for practical application development.
• Concurrency Safeguards: Mechanisms implemented to manage simultaneous access to shared resources, preventing data corruption or race conditions.
• Lease-Based Locking: A concurrency control technique where a resource is "leased" for a specific duration or until a condition is met, preventing multiple processes from accessing it simultaneously.
• Exponential Backoff: A strategy used in retry mechanisms where the delay between retries increases exponentially, reducing the load on a system during periods of high demand or failure.
• Transactions (Database): A sequence of operations performed as a single logical unit of work, ensuring that either all operations are completed successfully or none are.
• API Pricing: The cost associated with using an AI model's application programming interface, typically based on input and output tokens.
• Caching System: A mechanism that stores frequently accessed data in a temporary location for faster retrieval, potentially reducing costs.
• Prompt Engineering: The process of designing and refining input prompts to elicit the desired output from an AI model.
• Production Ready: Code or systems that are robust, reliable, and suitable for deployment in a live operational environment.

Model Comparison: Haiku 4.5 vs. GLM 4.6 vs. GPT5 Mini

This summary details a comparison of three mid-tier coding AI models: Claude Haiku 4.5, GLM 4.6, and GPT5 Mini, focusing on their performance in coding tasks, specifically building a job queue system in TypeScript with SQLite. The evaluation considers speed, cost, code quality, and tool calling capabilities.

Test Setup and Methodology

The comparison was conducted using Kilo Code's "ask mode" for prompt generation and "code mode" for execution. The prompt was to build a job queue system in TypeScript with SQLite, supporting delayed execution and persistence, with an example demonstration. This prompt was chosen to test asynchronous logic, persistence, and concurrency, areas where smaller models often struggle. The same prompt was used for all three models without any model-specific tuning or prompt engineering.

Pricing and Cost Analysis

• API Pricing: Haiku 4.5 was noted as slightly pricier on output tokens, GLM 4.6 was in the middle, and GPT5 Mini was the cheapest.
• Caching System: GPT5 Mini features a caching system that reportedly makes it up to 90% cheaper for reads, a significant advantage for long coding sessions or applications with persistent context.
• Total Cost: Despite cheaper per-token rates, some models can be more expensive overall due to verbosity. GLM 4.6, though cheap on paper, ended up being the most expensive due to its verbose output.

Performance Metrics and Results

The models were evaluated on speed, cost, code quality, and tool calling.

• GPT5 Mini:

  • Time: 6 minutes
  • Cost: $0.05
  • Key Strengths: Understood SQLite's concurrency limitations and implemented a robust lease-based locking system (timestamp-based, locked until column). Utilized transactions and exponential backoff. Focused on correctness and safety over flashy features. Recovered from minor tool call hiccups on retry. Considered the most production-ready.
  • Key Weaknesses: Minor tool call hiccups, though recoverable.

• Claude Haiku 4.5:

  • Time: 3 minutes
  • Cost: $0.08
  • Key Strengths: Fastest execution time. Added extra features like stats and job clearing, demonstrating polish and developer-friendliness. Zero tool calling failures. Top-tier file edit precision.
  • Key Weaknesses: Lacked concurrency control, locking, transactions, and safeguards. No production safety features. Occasionally got stuck in a loop or repeated itself.

• GLM 4.6:

  • Time: 4 minutes
  • Cost: $0.14 (most expensive overall)
  • Key Strengths: Excellent code structure, creating multiple files, a typed system, enums, and priority queues. Ambitious and architecturally sound.
  • Key Weaknesses: Reasoning mode broke tool calling, requiring it to be disabled for execution. Tracked active jobs in memory, making it vulnerable to data loss upon application crashes. Hand-rolled a UUID function instead of using a standard one, suggesting an attempt to "show off." Not production safe due to in-memory job tracking.

Detailed Model-by-Model Analysis

GPT5 Mini: Correctness and Safety

GPT5 Mini's primary focus was on correctness and safety. It successfully implemented a sophisticated lease-based locking mechanism to handle SQLite's concurrency limitations, a testament to its understanding of real-world engineering challenges. The use of transactions and exponential backoff further solidified its production-ready status. While it experienced minor tool call issues, these were resolvable through retries.

GLM 4.6: Structure and Ambition

GLM 4.6 excelled in code structure, generating a well-organized, multi-file project with advanced features. However, its ambition led to practical issues, such as the need to disable its reasoning mode to enable tool calling and its vulnerability to data loss due to in-memory job tracking. The model's tendency to over-engineer, like hand-rolling a UUID function, was also noted.

Claude Haiku 4.5: Speed and Polish

Haiku 4.5 was the fastest model, delivering a functional solution with added polish like statistics and job clearing features. Its developer-friendly approach and perfect tool calling record were highlighted. However, it critically lacked any concurrency safeguards, making it unsuitable for production environments where data integrity is paramount. The occasional tendency to loop or repeat itself was also a drawback.

Tool Calling Comparison

• GLM 4.6: Failed in reasoning mode.
• Haiku 4.5: Perfect, with zero failures.
• GPT5 Mini: Experienced minor hiccups but recovered after retries.

Concurrency Handling Comparison

• GPT5 Mini: Implemented robust lease locking, ensuring data safety.
• GLM 4.6: Used in-memory tracking, which is prone to data loss on crashes.
• Haiku 4.5: Ignored concurrency issues entirely.

Analogies for Model Thinking

The article uses analogies to describe the core approach of each model:

• GPT5 Mini: A systems engineer, prioritizing robustness and reliability.
• GLM 4.6: A software architect, focusing on structure and design.
• Claude Haiku 4.5: A UX designer, emphasizing speed and user experience.

Conclusion and Takeaways

The primary takeaway is that the "best" model depends on the specific use case:

• For correctness and production deployment: GPT5 Mini is the recommended choice due to its safety features and robust handling of concurrency.
• For code structure exploration: GLM 4.6 is interesting to observe but carries significant risks for production use.
• For rapid prototyping and demos: Haiku 4.5 is highly effective due to its speed and feature richness, provided production safety is not a primary concern.

The article emphasizes the value of testing actual code generation and tool calling capabilities rather than relying on theoretical claims. The author expresses a desire for future comparisons to include more complex scenarios like API layers or front-end development to further highlight trade-offs.