Best Open Source Models For Post Training

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Best open-source models for post-training

Compare top open-source models for post-training. Learn what drives cost and which model fits use case.

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Chloe Florit

Last updated September 2, 2026

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TL;DR Choosing an open-source model for post-training comes down to cost and use case. Cost is driven by active parameters, total parameters, and KV cache size. We grouped the best models into five cost tiers, from ultra-expensive (Kimi K3) to ultra-cheap (Qwen3.6-35B-A3B), and explained which to pick for text, vision, and coding-agent work. DeepSeek-V4-Flash is great for long-context, cost-sensitive tasks; GLM-5.2 provides fast, asynchronous RL; Kimi K2.6 and K2.7 Code fine-tune the most stably, with K2.7 best for coding agents; Nemotron-3-Super-120B is efficient to fine-tune with native FP4; and Qwen3 is the safe default with the widest size and library support.

During post-training, an open-source model can be specialized for your use case with methods like supervised fine-tuning, preference optimization, and reinforcement learning (RL). RL is one of the most common methods. It works as a feedback loop: the model generates responses (rollouts), an evaluator (human, AI, or algorithmic check) scores them, that feedback is turned into a reward, and the model is adjusted so high-scoring behavior becomes more likely and low-scoring behavior less likely. The cycle repeats until the model reliably produces the results you want. In this post, we break down what drives post-training cost, then rank the best open models by cost tier so you can choose the right model for your budget and use case. What determines the cost to train and serve? Most frontier models use a Mixture of Experts (MoE) architecture. A Mixture-of-Experts model routes each token through a small subset of its 'experts,' so you get the reasoning capacity of a massive model at a lower compute cost. With MoE, only a fraction of parameters (active parameters) are used to generate each token. Kimi K2.6, for example, has 1 trillion total parameters but only activates 32B per token. (Some models, like the smaller Qwen3 variants, are still dense: every parameter is used to generate a token.) The split between total and active parameters is why two models of the same size can have different post-training and inference costs. Active parameters drive compute cost Every token the model generates requires math proportional to its active parameters. Each active weight gets used in the calculation (to turn input text into the next word), so more active parameters means more floating point operations (FLOPs) per generated token. Since RL post-training generates many rollout tokens, active parameter count is the biggest driver of post-training cost. Total parameters limit speed During serving, weights must be streamed from GPU memory to the compute cores for every token generation step, and the size of that transfer can be a bottleneck for inference speed. You might expect an MoE to only stream its active experts, but in practice models serve many requests at once, and different tokens route to different experts. Across a batch, nearly all the weights move through GPU memory for each token generation. This is why total parameters determine how fast inference can run. KV cache slows generation as context grows As the model processes a sequence, it caches a key and value for every token it has seen and it typically re-reads that entire cache each time it generates a new token. The cache grows with context length. KV cache size per token is how much memory the model needs to store the keys and values of a token. A large KV cache limits inference speed the same way total parameters do: the cache has to be streamed from GPU memory to the compute cores for every new token, alongside the weights. KV Cache:  Keys help the model figure out which words to pay attention to, and values determine what information gets added to a word's meaning based on which other words are relevant. Keys and values are collectively cached as the “KV cache”. Each layer generates a unique KV pair per token. Models, ranked by cost to serve

Below, we’ll dive into the models most commonly used for post-training and what makes each one a strong choice. DeepSeek-V4-Flash (Best text model in cheap tier) Architecture: 284B total parameters, 13B activated per token (MoE). DeepSeek V4’s hybrid attention (CSA + HCA) improves efficiency by compressing the KV cache to ~4.8 KB per token in FP8/FP4 serving.

Why it’s cheap to post-train:

The compressed KV cache makes it cheap to fine-tune very long training examples like long documents or long agent trajectories.

13B active parameters and a light KV cache make large RL (Reinforcement Learning) runs cost less.

Post-training use cases: long-context, cost-sensitive tasks.

GLM-5.2 (BF16) (Best text model in expensive tier) Architecture: It uses a MoE architecture with 256 experts, routing just 8 experts per token. GLM’s sparse attention selects a subset of the keys to use in the attention algorithm rather than using them all. This helps reduce the KV cache traffic per token and the cost of long context windows. Why it’s good for post-training: GLM 5.2’s slime (an open-source RL training framework) provides strong, fast RL support. It is asynchronous, which means the different stages of the RL loop (generating rollouts, scoring them, and updating the model) can run concurrently instead of waiting for each other. GPUs spend less time idle and RL runs finish faster thanks to model-specific tooling.

Kimi K2.6 (Best vision model in expensive tier) Architecture: Moonshot AI's 1-trillion-parameter MoE model activates 32B per token. Kimi K2.6 has multimodal support via MoonViT (a 400M visual encoder) and can take text, images, and video as input. (Note: 1T total parameters makes the model expensive to post-train and serve.) Why it’s good for post-training: Good tool calling: handles agentic tool use well, including coding tools

Strong baseline coding ability: less effort is needed to improve coding performance during post-training.

Good vision: can read images and video, not just text.

Post-training use cases: fine-tune/specialize agent behavior. Best suited as the main orchestrating agent, not a lightweight sub-agent.

Kimi K2.7 Code (Best for coding agents in expensive tier)...