Best open-source models for post-training

Selecting an open-source model for post-training depends primarily on use case, tool compatibility, benchmark performance, and costs driven by active parameters, total parameters, and KV-cache size. Reinforcement learning is particularly sensitive to generation costs because it requires repeated model rollouts that are evaluated and used as reward signals to adjust behavior, while supervised fine-tuning and preference optimization offer other specialization approaches. DeepSeek-V4-Flash is positioned as an economical option for long-context text tasks due to its 13B active parameters and compressed KV cache, while GLM-5.2 targets more expensive workloads with asynchronous RL tooling. Kimi K2.6 offers multimodal and agentic capabilities, and K2.7 Code builds on it with stronger coding performance and lower reasoning-token usage for coding agents. NVIDIA’s Nemotron-3-Super-120B emphasizes efficient long-context reasoning, a hybrid architecture with limited KV-cache growth, native FP4 support, and compatibility with established NVIDIA tooling. The Qwen3 family is presented as a broadly supported default across sizes and price ranges, suitable for applications from lightweight classification and retrieval to larger training workloads.

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