Overview
In the rapidly evolving landscape of LLMs, choosing the right model for software development tasks is critical. This analysis presents a head-to-head comparison between Anthropic: Claude Haiku 4.5 and DeepSeek: DeepSeek V3.2, focusing specifically on their Coding Performance with 10 Evaluators. By utilizing PeerLM's comparative evaluation methodology, we determine which model provides superior accuracy and instruction adherence when handling complex code-related prompts.
Benchmark Results
Our evaluation across 10 independent judges highlights a distinct leader in coding tasks. The following table summarizes the performance and cost metrics for both models based on the benchmark run.
| Model | Rank | Overall Score | Avg Prompt Tokens | Avg Completion Tokens |
|---|---|---|---|---|
| DeepSeek: DeepSeek V3.2 | 1 | 8.11 | 216 | 146 |
| Anthropic: Claude Haiku 4.5 | 2 | 1.89 | 237 | 197 |
Criteria Breakdown
The evaluation focused on two primary pillars of coding utility: Accuracy and Instruction Following. Because our methodology relies on comparative ranking rather than static rubric scoring, these aggregated scores reflect how the 10 evaluators perceived the models' output quality relative to one another. DeepSeek: DeepSeek V3.2 consistently outperformed the competition, securing an overall score of 8.11, while Anthropic: Claude Haiku 4.5 trailed with a score of 1.89.
Cost & Latency
Efficiency is a secondary but vital factor for developers integrating LLMs into IDE extensions or CI/CD pipelines. The cost comparison reveals a significant disparity between the two models:
- DeepSeek: DeepSeek V3.2: Total cost of $0.000447 with a cost per output token of $0.000764.
- Anthropic: Claude Haiku 4.5: Total cost of $0.004878 with a cost per output token of $0.006206.
DeepSeek: DeepSeek V3.2 demonstrates superior cost-efficiency, offering a more economical solution for high-volume coding tasks without compromising on the quality of the generated code.
Use Cases
Based on the Coding Performance with 10 Evaluators results, DeepSeek: DeepSeek V3.2 is highly recommended for developers seeking a balance of high-fidelity code generation and cost-effectiveness. Anthropic: Claude Haiku 4.5 may still find utility in specialized environments where specific model-native features are required, though it currently lags behind in raw coding benchmarks against this competitor.
Verdict
Our comparative evaluation clearly identifies DeepSeek: DeepSeek V3.2 as the stronger performer for coding tasks. It dominates the leaderboard with a superior score and significantly lower operational costs compared to Anthropic: Claude Haiku 4.5.