Mellum-4b-base
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Mellum-4b-sft-all
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查看详情 ↗Mellum2.1-12B-A2.5B-Thinking
JetBrains> [!Note] > Mellum2.1 is a thinking model. Use it for complex agentic tasks, such as working in a repository, running commands, and calling tools, and for hard non-agentic problems in coding, math, and reasoning. Mellum2.1 is the next version of Mellum2 Thinking. The architecture is unchanged (a 12B mixture-of-experts model with 2.5B active parameters), and almost all of the work for this version went into post-training, primarily reinforcement learning (RL): As a result, Mellum2.1 handles agentic tasks much better than Mellum2. After millions of sandboxed runs in real environments during training, it explores a codebase, edits files, and checks its own changes.
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查看详情 ↗Mellum2.1-12B-A2.5B-Thinking-GGUF
JetBrainsThis repository contains **GGUF** builds of `JetBrains/Mellum2.1-12B-A2.5B-Thinking` in every quantization we publish, ready to run with `llama.cpp`, Ollama, LM Studio, and other GGUF-compatible runtimes. Each variant is one file; download the one you need. The repository also ships the model's **multi-token prediction (MTP) head** for speculative decoding in llama.cpp; see Speculative decoding with the MTP head. Mellum2.1 Thinking is a Mixture-of-Experts reasoning model (64 experts, 8 activated per token, 131,072-token context) that emits its chain of thought inside ` ... ` blocks before the final answer. For the full model description, evaluation results, and architecture details, see the original model card: **JetBrains/Mellum2.1-12B-A2.5B-Thinking**. KL divergence and top-token agreement are measured against the BF16 logits on Wikitext-2 (`n_ctx=512`); lower KLD / higher agreement means closer to the unquantized model.
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查看详情 ↗Mellum2-12B-A2.5B-Base
JetBrains> [!Note] > Use this checkpoint as the starting point for your own fine-tuning, alignment, or domain adaptation on top of the long-context base. For instruction-following or reasoning tasks out of the box, use Instruct or Thinking instead. The model uses a Mixture-of-Experts architecture with 64 experts and activates 8 experts per token. It uses a combination of sliding-window and full attention layers, with a context length of 131,072 tokens. This is the long-context base, produced from `Mellum2-12B-A2.5B-Base-Pretrain` by a layer-selective YaRN extension stage that re-maps RoPE frequencies on the global-attention layers only. It is the shared starting point for the released Instruct and Thinking variants.
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查看详情 ↗Mellum2-12B-A2.5B-Base-Pretrain
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查看详情 ↗Mellum2-12B-A2.5B-Instruct
JetBrains> [!Note] > Use this model when you want direct, low-latency answers without an explicit chain of thought — interactive chat, code assistance, tool use, and instruction following. If you need explicit reasoning before the answer (complex debugging, planning, multi-step agentic flows), use Thinking instead. The model uses a Mixture-of-Experts architecture with 64 experts and activates 8 experts per token. It uses a combination of sliding-window and full attention layers, with a context length of 131,072 tokens. It is produced from `Mellum2-12B-A2.5B-Base` by supervised fine-tuning followed by reinforcement learning with verifiable rewards (RLVR) on math, executable coding, tool use, instruction following, reasoning, and knowledge tasks. Mellum2 Instruct answers directly, without an externalized chain of thought.
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查看详情 ↗This repository contains a **GGUF Q4_K_M** quantization of `JetBrains/Mellum2-12B-A2.5B-Instruct`, ready to run with `llama.cpp`, Ollama, LM Studio, and other GGUF-compatible runtimes. **This quantization (Q4_K_M):** 4-bit k-quant (medium). Strong quality/size trade-off (KLD ~0.106, 87% top-token agreement) — a good default. Mellum 2 Instruct is a Mixture-of-Experts assistant model (64 experts, 8 activated per token, 131,072-token context) that answers directly, without an externalized chain of thought. For the full model description, evaluation results, and architecture details, see the original model card: **JetBrains/Mellum2-12B-A2.5B-Instruct**.
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查看详情 ↗Mellum2-12B-A2.5B-Instruct-GGUF-Q8_0
JetBrainsThis repository contains a **GGUF Q8_0** quantization of `JetBrains/Mellum2-12B-A2.5B-Instruct`, ready to run with `llama.cpp`, Ollama, LM Studio, and other GGUF-compatible runtimes. **This quantization (Q8_0):** 8-bit. Effectively lossless — KL divergence from BF16 is ~0.016 and it keeps BF16's top token 95% of the time. Mellum 2 Instruct is a Mixture-of-Experts assistant model (64 experts, 8 activated per token, 131,072-token context) that answers directly, without an externalized chain of thought. For the full model description, evaluation results, and architecture details, see the original model card: **JetBrains/Mellum2-12B-A2.5B-Instruct**.
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查看详情 ↗Mellum2-12B-A2.5B-Instruct-SFT
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查看详情 ↗Mellum2-12B-A2.5B-Thinking
JetBrains> [!Note] > Use this model when you want explicit chain-of-thought before the final answer — complex debugging, multi-step planning, agentic workflows, and math- or reasoning-heavy tasks. For direct, low-latency answers without reasoning traces, use Instruct instead. The model uses a Mixture-of-Experts architecture with 64 experts and activates 8 experts per token. It uses a combination of sliding-window and full attention layers, with a context length of 131,072 tokens. It is produced from `Mellum2-12B-A2.5B-Base` by supervised fine-tuning (loss computed only on the final assistant turn) followed by reinforcement learning with verifiable rewards (RLVR) on a harder data mix that includes a long-form math subset. The model emits its reasoning inside ` ... ` blocks before the final answer.
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查看详情 ↗Mellum2-12B-A2.5B-Thinking-SFT
JetBrains> [!Note] > Research artifact: the SFT-only intermediate checkpoint of the Thinking training pipeline. Use it to study or build on the reasoning SFT stage in isolation, run post-training experiments (preference tuning, RLVR, etc.), or compare against the final RL-tuned variant. For production-quality reasoning use Thinking instead. Mellum2 Thinking (SFT) is the supervised-fine-tuning-only intermediate checkpoint of the Mellum2 Thinking training pipeline, released by JetBrains as a research artifact. The model uses a Mixture-of-Experts architecture with 64 experts and activates 8 experts per token. It uses a combination of sliding-window and full attention layers, with a context length of 131,072 tokens.
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查看详情 ↗Qwen3.8-3.6-27B-blend
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查看详情 ↗Qwen3.8-3.6-27B-blend-GGUF
JetBrains**Unofficial JetBrains derivative.** This model was prepared by JetBrains using Qwen models developed by Alibaba Cloud. This release is not affiliated with, sponsored by, or endorsed by Alibaba Cloud or Alibaba Group. *Tasks completed versus output tokens on JetBrains’ 100-task internal coding benchmark. Reasoning settings differ between configurations; see the article for methodology and context.* Read Making Local AI Smarter and Faster for the Junie Local release story, coding evaluations, token-efficiency results, and MTP runtime experiments.
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查看详情 ↗Qwen3.8-3.6-27B-blend-MLX-4bit
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查看详情 ↗Qwen3.8-3.6-27B-blend-MTP-MLX-4bit
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。