GoogleGemini 3.5 FlashVSZ.ai (Zhipu AI)GLM-5.1
Our Take
We recommend GLM-5.1 for a 1.6x API cost saving and superior coding capability, or Gemini 3.5 Flash if your workflow requires peak reasoning. While GLM-5.1 is more cost-effective, Gemini 3.5 Flash holds a clear reasoning advantage. Choose GLM-5.1 for code generation, or Gemini 3.5 Flash for complex logical reasoning.
▶WHY?
Benchmark Calculations & Evidence:
Coding Benchmarks: Both models were evaluated on the SWE-bench Pro benchmark. Gemini 3.5 Flash scored 55.1%, while GLM-5.1 scored 58.4%.
Reasoning Benchmarks: Both models were evaluated on the GPQA Diamond benchmark. Gemini 3.5 Flash scored 92.2%, while GLM-5.1 scored 86.2%.
Cost Efficiency: GLM-5.1 pricing ($1.4/M input, $4.4/M output) is 1.6x cheaper than Gemini 3.5 Flash ($1.5/M input, $9/M output).
Was this recommendation helpful?
Benchmarks & Scores
Coding (swe-bench-pro)
55.1%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+6.0%)
92.2%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$3.38Input: $1.50 | Output: $9.00Context WindowLarger
1.05M tokensBenchmarks & Scores
Coding (swe-bench-pro)Winner (+3.3%)
58.4%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)
86.2%graduate-level science QA
Cost & Context
Cost (per 1M tokens)1.6x cheaper
$2.15Input: $1.40 | Output: $4.40Context Window
202.75k tokensFrequently Asked Questions about Gemini 3.5 Flash vs GLM-5.1
GLM-5.1 is cheaper than Gemini 3.5 Flash. GLM-5.1 has a blended cost of $2.15/1M tokens, which is about 1.6x cheaper than Gemini 3.5 Flash at $3.38/1M tokens.
GLM-5.1 is better for coding tasks on this benchmark. It scores 58.4% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to Gemini 3.5 Flash which scores 55.1%.
Related Matchups
Explore similar comparisons for Gemini 3.5 Flash and GLM-5.1.
Do you want to find a model for your constraints?
Use our interactive model finder to filter LLMs by reasoning capability, coding performance, cost, and context length.