DeepSeek vs GLM Coding Plan
DeepSeek and GLM are two of the most capable coding models available through claude-multi. Here is how they compare in practice for code generation, reasoning, and cost.
| DeepSeek | GLM Coding Plan | |
|---|---|---|
| Type | DeepSeek | GLM Coding Plan |
| Website | deepseek.com | bigmodel.cn |
DeepSeek
Pros
- Excellent at complex multi-step reasoning
- Strong performance on benchmarks and real-world coding tasks
- Good at following detailed instructions
- Large context window supports big codebases
- Competitive pricing for the quality
- Well-documented API
Cons
- Slower than GLM on straightforward tasks
- Can overthink simple problems
- Pricing is higher than GLM for bulk work
- API availability has had intermittent issues
GLM Coding Plan
Pros
- Fast response times for code generation
- Lower cost per token than most alternatives
- Good at standard coding tasks: refactoring, tests, boilerplate
- Chinese AI ecosystem with local support
- Clean API with straightforward integration
- Strong multilingual support including Chinese
Cons
- Weaker at complex multi-step reasoning compared to DeepSeek R1
- Less proven track record on Western coding benchmarks
- Documentation primarily in Chinese
- Smaller community outside China
- May struggle with highly nuanced architecture decisions
When to choose DeepSeek
You need strong reasoning for complex tasks: architecture decisions, tricky bugs, multi-file refactors. DeepSeek R1 thinks harder and gets better results on hard problems.
When to choose GLM Coding Plan
You need fast, cheap code generation at volume. Writing tests, generating boilerplate, standard refactoring. GLM gets it done quickly without overthinking.
Verdict
DeepSeek R1 is stronger at complex reasoning and multi-step planning. GLM Coding Plan is faster and cheaper for straightforward code generation. Pick DeepSeek for hard problems, GLM for volume.
Reasoning depth
DeepSeek R1 is a reasoning model. It thinks through problems step by step before writing code. This makes it better at architecture decisions, tricky bugs, and multi-step refactors. It takes longer but produces better results on hard problems.
GLM Coding Plan is more direct. You give it a prompt, it writes code. It’s fast and efficient. For straightforward tasks, it gets the job done with less overhead.
Speed and cost
GLM is faster and cheaper. If you are generating a lot of standard code, writing tests, or doing simple refactoring, GLM will save you time and money.
DeepSeek costs more per token and takes longer to respond. But for a hard bug or a complex refactor, the extra reasoning time pays off.
Code quality
For standard coding tasks, both produce good output. The gap shows on complex tasks. DeepSeek R1 handles nuanced requirements better, catches edge cases, and produces more complete solutions.
GLM is reliable for the common cases. Write a function, generate tests, fix a linter error. It does these well and quickly.
Practical setup with claude-multi
You can run both through claude-multi at the same time. Use DeepSeek for the hard architecture tasks and GLM for the volume work. This is the setup that gets the best of both.