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Model Library

Explore open models you can run and customize on Modal.

Language & vision models

Z.ai

GLM 5.3

A natively multimodal model for coding, agents, and long-context workloads.

Language

753B MoE 1M context

Z.ai

GLM 5.3 Flash

A natively multimodal model for coding, agents, and long-context workloads.

Vision + language

320B MoE 1M context

Qwen

Qwen3.8-Max

The open-weights, text-only Max-class model for coding and long-horizon agentic work.

Language

2.4T MoE Up to 1M context

Moonshot AI

Kimi K3

Open frontier intelligence for deep reasoning and knowledge work.

Vision + language

2.8T MoE 1M context

Thinking Machines

Inkling NVFP4

An open-weights multimodal model with controllable reasoning across text, images, and audio.

Multimodal

975B MoE 1M context

DeepSeek

DeepSeek V4.1 Flash

A strong MoE model with a 1M-token context, native visual understanding, and an 3.9× smaller KV cache than V4 Flash.

Vision + language

552B MoE 1M context

OpenAI

GPT-OSS 120B

OpenAI's open-weight reasoning model with configurable reasoning effort and native tool use, sized to fit on a single GPU.

Language

117B MoE 128K context

IFM

K2 Horizon 0.9B

IFM's smallest K2 Horizon model: a 0.9B dense reasoning model with 128K context, selectable reasoning effort, tool calling, open training checkpoints, and Apache 2.0 weights.

Language

0.9B dense 128K context

DeepSeek

DeepSeek V4 Pro

DeepSeek's official V4 Pro release: a 1.6T MoE model with 49B active parameters, 1M-token context, built-in DSpark speculative decoding, and MIT weights.

Language

1.6T MoE · 49B active 1M context

Z.ai

GLM 5.3

Z.ai's GLM-5.3 flagship coding model in Modal's NVFP4 quantization: a 753B sparse-attention MoE with 40B active parameters, 1M context, and three reasoning effort levels.

Language

753B MoE · 40B active 1M context

Meta

Muse Glimmer 30B NVFP4

RadixArk's NVFP4/MXFP8 quantization of Meta's Muse Glimmer 30B agentic model: text-only, 128K context, controllable reasoning, and tool calling.

Language

30B dense 128K context

Meta

Muse Glimmer 30B BF16

Meta's open agentic model for local hardware: a dense 30B model distilled from Muse Spark, with 128K context, controllable reasoning, and Apache 2.0 weights.

Language

30B dense 128K context

NVIDIA

Nemotron 3.5 Lightning 30B A3B NVFP4

NVIDIA's fast open agent workhorse: a hybrid Mamba-2 MoE with 30B total and 3B active parameters, 1M-token context, built-in speculative decoding, and OpenMDW-1.1 weights.

Language

30B MoE · 3B active 1M context

Pipecat

PhoneLLM Alpha 1

Pipecat's open-weights LLM for voice agents: a 30B Mamba-Transformer MoE (3.5B active) fine-tuned from Nemotron 3 Nano for low-latency tool calling, 256K context, BSD 2-Clause.

Language

30B MoE · 3.5B active 256K context

Qwen

Qwen3.8 27B

Qwen's compact dense Qwen3.8 model: a 27B model with switchable thinking, 256K context, and Apache 2.0 weights.

Language

27B dense 256K context

Qwen

Qwen3.8 Flash Next FP8

Qwen's open preview of the Qwen4 architecture: a 125B MoE with 6B active, Qwen Sparse Attention, 51B n-gram embeddings, and 256K context, in FP8.

Language

125B MoE · 6B active 256K context

Qwen

Qwen3.8 2.4T A95B NVFP4

Qwen's open Qwen-Max-class flagship: a text-only 2.4T-parameter MoE with 95B active per token, always-on reasoning, and 256K context, quantized to NVFP4 by Modal.

Language

2.4T MoE · 95B active 256K context

DeepSeek

DeepSeek V4 Flash

DeepSeek's official V4 Flash release: a 284B MoE model with 13B active parameters, 1M-token context, built-in DSpark speculative decoding, and MIT weights.

Language

284B MoE · 13B active 1M context

Tencent

Hy3 GPTQ Int4

Tencent's Hy3 in AngelSlim's GPTQ Int4 quantization: a 299B Mixture-of-Experts model with 21B active parameters, 256K context, selectable reasoning effort, and Apache 2.0 weights.

Language

299B MoE · 21B active 256K context

Z.ai

GLM 5.2 NVFP4

Z.ai's GLM-5.2 flagship in NVIDIA's NVFP4 quantization: a 753B sparse-attention MoE with 40B active parameters, 1M context, and MIT weights for Blackwell GPUs.

Language

753B MoE · 40B active 1M context

Z.ai

GLM 5.2 FP8

Z.ai's official FP8 checkpoint of GLM-5.2: a 753B sparse-attention MoE with 40B active parameters, a solid 1M context, configurable effort levels, and MIT weights.

Language

753B MoE · 40B active 1M context

NVIDIA

NVIDIA Nemotron 3 Ultra 550B A55B NVFP4

NVIDIA's frontier-scale open reasoning model: a 550B latent MoE with 55B active parameters, up to 1M-token context, NVFP4 weights, and OpenMDW-1.1 licensing.

Language

550B MoE · 55B active 1M context

Moonshot AI

Kimi K2.6 NVFP4

Moonshot AI's Kimi K2.6 in NVIDIA's NVFP4 quantization: a 1T-parameter MoE agentic model with 32B active per token, 256K context, and thinking mode.

Language

1T MoE · 32B active 256K context

DeepSeek

DeepSeek V4 Pro Preview

DeepSeek's flagship V4 preview: a 1.6T Mixture-of-Experts model with 49B active parameters, hybrid compressed sparse attention, 1M-token context, and MIT weights.

Language

1.6T MoE · 49B active 1M context

DeepSeek

DeepSeek V4 Flash Preview

DeepSeek's efficient V4 preview: a 284B Mixture-of-Experts model with 13B active parameters, hybrid compressed sparse attention, 1M-token context, and MIT weights.

Language

284B MoE · 13B active 1M context

Google

Gemma 4 31B IT

Google DeepMind's largest open Gemma 4 model: a dense 31B vision-language model with configurable thinking, 256K context, and Apache 2.0 weights.

Vision + language

31B dense 256K context

Google

Gemma 4 26B A4B IT

Google DeepMind's Mixture-of-Experts Gemma 4 model: 25.2B total parameters with 3.8B active per token, text and image input, 256K context, and Apache 2.0 weights.

Vision + language

25.2B MoE · 3.8B active 256K context

Google

Gemma 4 E4B IT

Google DeepMind's on-device Gemma 4 model: an 8B (4.5B effective) vision-language model with text and image input, 128K context, and Apache 2.0 weights.

Vision + language

8B dense 128K context

Google

Gemma 4 E2B IT

Google DeepMind's smallest Gemma 4 model: a 5.1B (2.3B effective) on-device vision-language model with text and image input, 128K context, and Apache 2.0 weights.

Vision + language

5.1B dense 128K context

Qwen

Qwen3.6 35B A3B

Qwen's first open-weight Qwen3.6 model: a 35B MoE vision-language model with 3B active parameters, default-on thinking, 262K context, and Apache 2.0 weights.

Vision + language

35B MoE · 3B active 262K context

Qwen

Qwen3.6 35B A3B FP8

Qwen's FP8 build of its 35B MoE vision-language model: 3B active parameters, default-on thinking, 262K context, quantized block-wise to FP8 under Apache 2.0.

Vision + language

35B MoE · 3B active 262K context

Qwen

Qwen3.6 27B

Qwen's flagship-level dense coder: a 27B vision-language model with default-on thinking, 262K native context, and Apache 2.0 weights.

Vision + language

27B dense 262K context

Qwen

Qwen3.6 27B FP8

Qwen's FP8 build of its 27B dense coder: same vision-language model with default-on thinking and 262K context, quantized block-wise to FP8 under Apache 2.0.

Vision + language

27B dense 262K context

NVIDIA

NVIDIA Nemotron 3 Super 120B A12B NVFP4

NVIDIA's hybrid Mamba-Transformer MoE for agentic reasoning: 120B total and 12B active parameters, up to 1M-token context, native NVFP4 pretraining, and multi-token prediction.

Language

120B MoE · 12B active 1M context

Qwen

Qwen3.5 9B

Qwen's 9B Qwen3.5 model: a dense vision-language model with a hybrid Gated DeltaNet architecture, thinking on by default, 256K context extensible to 1M, and Apache 2.0 weights.

Vision + language

9B dense 256K context

Qwen

Qwen3.5 4B

Qwen's 4B Qwen3.5 model: a dense vision-language model with a hybrid Gated DeltaNet architecture, thinking on by default, 256K context extensible to 1M, and Apache 2.0 weights.

Vision + language

4B dense 256K context

Qwen

Qwen3.5 2B

Qwen's compact Qwen3.5 model: a dense 2B vision-language model with a hybrid Gated DeltaNet architecture, 256K context, and Apache 2.0 weights.

Vision + language

2B dense 256K context

Qwen

Qwen3.5 0.8B

Qwen's smallest Qwen3.5 model: a dense 0.8B vision-language model with a hybrid Gated DeltaNet architecture, 256K context, and Apache 2.0 weights.

Vision + language

0.8B dense 256K context

Qwen

Qwen3.5 397B A17B FP8

Qwen's flagship Qwen3.5 model in FP8: a 397B Mixture-of-Experts vision-language model with 17B active parameters, hybrid linear attention, 256K context, and Apache 2.0 weights.

Vision + language

397B MoE · 17B active 256K context

Qwen

Qwen3.5 122B A10B FP8

Qwen's mid-size Mixture-of-Experts Qwen3.5 model in FP8: 122B total with 10B active parameters, native text and image input, 256K context, and Apache 2.0 weights.

Vision + language

122B MoE · 10B active 256K context

Qwen

Qwen3.5 35B A3B FP8

Qwen's compact Mixture-of-Experts Qwen3.5 model in FP8: 35B total with 3B active parameters, native text and image input, 256K context, and Apache 2.0 weights.

Vision + language

35B MoE · 3B active 256K context

Qwen

Qwen3.5 27B FP8

Qwen's largest dense Qwen3.5 model in FP8: a 27B vision-language model with hybrid Gated DeltaNet attention, thinking on by default, 256K context, and Apache 2.0 weights.

Vision + language

27B dense 256K context

Z.ai

GLM 4.7

Z.ai's GLM-4.7 agentic coding model: a 358B Mixture-of-Experts with 32B active parameters, interleaved and preserved thinking, 200K context, and MIT weights.

Language

358B MoE · 32B active 200K context

Qwen

Qwen3 Embedding 8B

Qwen's largest Qwen3 Embedding model: an 8B dense text embedder with 32K context, up to 4096-dim MRL vectors, 100+ languages, and Apache 2.0 weights.

Language

8B dense 32K context

Qwen

Qwen3 Embedding 0.6B

Qwen's smallest Qwen3 Embedding model: a 0.6B dense text embedder with 32K context, up to 1024-dim MRL vectors, 100+ languages, and Apache 2.0 weights.

Language

0.6B dense 32K context

Google

Gemma 3 1B IT

Google DeepMind's compact Gemma 3 model: a 1B-parameter text-only instruction-tuned LLM with 32K context, 140+ language pretraining, and open weights under the Gemma Terms of Use.

Language

1B dense 32K context

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