OPEN CODING LLMS COMPARED: GLM 4.7 VS DEEPSEEK 3.2 VS MINIMAX M2.1 VS KIMI K2
A recent video compares four coding-focused LLMs (GLM 4.7, DeepSeek 3.2, MiniMax M2.1, Kimi K2) across programming tasks. The takeaway is that performance varie...
A recent video compares four coding-focused LLMs (GLM 4.7, DeepSeek 3.2, MiniMax M2.1, Kimi K2) across programming tasks. The takeaway is that performance varies by task and setup, so teams should benchmark against their own workloads (repo-level codegen, SQL, tests, bug-fixing) before choosing a default.
Picking the right open model can cut costs and enable on-prem while maintaining code quality.
Task fit (e.g., SQL generation vs. multi-file refactors) impacts developer throughput more than headline scores.
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Run a lightweight eval harness on your repos covering ETL/ELT scaffolding, SQL generation/optimization, schema migrations, and unit-test creation/fix rate.
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Measure latency, context handling on large repos, tool/RAG integration, and regression stability across model versions.
Legacy codebase integration strategies...
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Pilot behind a feature flag in IDE and CI, compare diffs and test pass rates against your current assistant before switching defaults.
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Abstract through an OpenAI-compatible gateway to swap models without rewriting prompts or SDK calls.
Fresh architecture paradigms...
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Adopt a model-agnostic client, define evals and golden tasks on day 0, and store prompts as versioned assets in Git.
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Design for repo-level context (RAG/embeddings) and enforce guardrails with structured outputs and policy checks.