Most LLMs hide their intermediate reasoning; this model intentionally exposes it. That makes it easier to audit multi-step math, complex debugging, and agentic plans because the model emits its chain-of-thought before the final answer, letting you inspect, validate, or programmatically parse intermediate steps.
Key Capabilities
- Explicit, machine-readable reasoning traces: emits reasoning inside
<think>...</think>blocks so downstream tooling or humans can review and extract intermediate steps rather than infer them from the final reply—useful for verifiable workflows and debugging. - Long-context reasoning: supports a 131,072-token context with a sliding-window attention strategy, so it can hold extensive documents, codebases, or multi-turn agent traces in memory without frequent truncation.
- Mixture-of-Experts (MoE) with sparse activation: 64 experts with 8 active per token to increase capacity while keeping the base parameter count moderate—helps handle complex reasoning patterns and specialized subskills.
- Training & alignment choices: produced via supervised fine-tuning followed by RL with verifiable rewards (RLVR) on a mix that emphasizes long-form math and reasoning, prioritizing traceable correctness over terse answers.
Who it's for and tradeoffs
Great fit if you need auditable multi-step outputs (researchers validating reasoning, engineers debugging long traces, or toolchains that parse intermediate steps). It’s also useful when working with very long contexts or when you want explicit internal reasoning to feed downstream validators.
Look elsewhere if you require minimal-latency, compact answers without reasoning traces (there are Instruct-style checkpoints in the same family optimized for lower latency), or if your deployment environment cannot support MoE or very large context windows—those features increase inference complexity and resource needs.
Where it sits
Compared to short-context instruct models, this variant trades latency and serving complexity for inspectability and stronger multi-step math/logic performance. Its evaluation numbers on internal benchmarks show strong reasoning/math accuracy but a higher infrastructure cost due to sparse expert routing and long-context attention.
