GoogleGemma 4 31BVSMoonshot AI (Kimi)Kimi K2.7 Code
Our Take
We recommend Gemma 4 31B for practical local execution on standard developer hardware, or Kimi K2.7 Code if you need peak reasoning and have the VRAM (or a cloud API) to host it. While Kimi K2.7 Code offers frontier capability, its massive parameter size makes it extremely difficult to host locally. Choose Gemma 4 31B for standard local setups, or Kimi K2.7 Code for peak reasoning.
▶WHY?
Benchmark Calculations & Evidence:
Model Size: Kimi K2.7 Code is a massive 1000B parameter model requiring heavy GPU infrastructure, while Gemma 4 31B is a 30.7B parameter model.
Was this recommendation helpful?
Benchmarks & Scores
Coding (live-code-bench)
80%scripting single-file apps or clearly defined functions
Reasoning (gpqa-diamond)
84.3%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
Self HostLocal execution (zero API fees)Context Window
262.14k tokensBenchmarks & Scores
Coding (swe-bench-pro)
58.6%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+5.7%)
90%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$1.71Input: $0.95 | Output: $4.00Context Window
262.14k tokensFrequently Asked Questions about Gemma 4 31B vs Kimi K2.7 Code
For coding tasks, Gemma 4 31B scores 80% on live-code-bench (scripting single-file apps or clearly defined functions), while Kimi K2.7 Code scores 58.6% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning).
Related Matchups
Explore similar comparisons for Gemma 4 31B and Kimi K2.7 Code.
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.