- Developers & Engineers: Integrate high-quality reasoning and coding LLMs without MoE complexity in your apps.
- Researchers & Students: Explore distilled chain-of-thought behaviors in smaller models with open access.
- Enterprises & Startups: Deploy efficient yet capable reasoning models in production systems.
- Open-Source Advocates: Utilize, adapt, and redistribute under permissive MIT licensing.
- Benchmarkers: Compare across sizes and capacities in math, coding, and reasoning challenges.
How to Use DeepSeek R1 Distill Qwen-32B?
- Load the Model: Available via Hugging Face (`deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`) with MIT license.
- Deploy Locally or in Cloud: Use vLLM (`serve deepseek-ai/...`), SGLang, Ollama, or Hugging Face tools.
- Use Chain-of-Thought Prompts: Include step-by-step directives and use temperature 0.5–0.7 to maximize quality.
- Provide Up to 32K Tokens: Supports long contexts for coding, math, and complex reasoning.
- Quantize as Needed: Choose Q80 / Q4K formats (e.g. GGUF) to fit hardware constraints.
- Superior to o1-mini: Outperforms OpenAI’s dense 32B model across key reasoning benchmarks.
- Context Window Scalability: Built on Qwen 2.5 base with up to 32K tokens support.
- Efficient Deployment: Works well on consumer and server GPUs; quantized variants support lower-VRAM setups.
- Open-Source & Permissive: MIT licensed with derivations from Apache 2.0 base—no restrictive licensing.
- Top-tier dense reasoning performance
- Open-source with flexible licensing
- Deployable via popular frameworks
- Supports quantization for hardware efficiency
- Large context size supports complex tasks
- Lacks multimodal or MoE-level context window
- Some users report code-generation edge cases vs specialized coder models.
- GGUF variants may exhibit loading issues on certain platforms.
- Smaller distilled variants (14B and below) show noticeable drop-off in quality.
Custom
Custom
Proud of the love you're getting? Show off your AI Toolbook reviews—then invite more fans to share the love and build your credibility.
Add an AI Toolbook badge to your site—an easy way to drive followers, showcase updates, and collect reviews. It's like a mini 24/7 billboard for your AI.
Reviews
Rating Distribution
Average score
Popular Mention
FAQs
Similar AI Tools
OpenAI - GPT 4.1
GPT-4.1 is OpenAI’s newest multimodal large language model, designed to deliver highly capable, efficient, and intelligent performance across a broad range of tasks. It builds on the foundation of GPT-4 and GPT-4 Turbo, offering enhanced reasoning, greater factual accuracy, and smoother integration with tools like code interpreters, retrieval systems, and image understanding. With native support for a 128K token context window, function calling, and robust tool usage, GPT-4.1 brings AI closer to behaving like a reliable, adaptive assistant—ready to work, build, and collaborate across tasks with speed and precision.
OpenAI - GPT 4.1
GPT-4.1 is OpenAI’s newest multimodal large language model, designed to deliver highly capable, efficient, and intelligent performance across a broad range of tasks. It builds on the foundation of GPT-4 and GPT-4 Turbo, offering enhanced reasoning, greater factual accuracy, and smoother integration with tools like code interpreters, retrieval systems, and image understanding. With native support for a 128K token context window, function calling, and robust tool usage, GPT-4.1 brings AI closer to behaving like a reliable, adaptive assistant—ready to work, build, and collaborate across tasks with speed and precision.
OpenAI - GPT 4.1
GPT-4.1 is OpenAI’s newest multimodal large language model, designed to deliver highly capable, efficient, and intelligent performance across a broad range of tasks. It builds on the foundation of GPT-4 and GPT-4 Turbo, offering enhanced reasoning, greater factual accuracy, and smoother integration with tools like code interpreters, retrieval systems, and image understanding. With native support for a 128K token context window, function calling, and robust tool usage, GPT-4.1 brings AI closer to behaving like a reliable, adaptive assistant—ready to work, build, and collaborate across tasks with speed and precision.
Gemini 2.5 Flash
Gemini 2.5 Flash is Google DeepMind’s cost-efficient, low-latency hybrid-reasoning model. Designed for large-scale, real-time tasks that require thinking—like classification, translation, conversational AI, and agent behaviors—it supports text, image, audio, and video input, and offers developer control over its reasoning depth. It balances high speed with strong multimodal intelligence.
Gemini 2.5 Flash
Gemini 2.5 Flash is Google DeepMind’s cost-efficient, low-latency hybrid-reasoning model. Designed for large-scale, real-time tasks that require thinking—like classification, translation, conversational AI, and agent behaviors—it supports text, image, audio, and video input, and offers developer control over its reasoning depth. It balances high speed with strong multimodal intelligence.
Gemini 2.5 Flash
Gemini 2.5 Flash is Google DeepMind’s cost-efficient, low-latency hybrid-reasoning model. Designed for large-scale, real-time tasks that require thinking—like classification, translation, conversational AI, and agent behaviors—it supports text, image, audio, and video input, and offers developer control over its reasoning depth. It balances high speed with strong multimodal intelligence.
Gemini 2.0 Flash‑Lite is Google DeepMind’s most cost-efficient, low-latency variant of the Gemini 2.0 Flash model, now publicly available in preview. It delivers fast, multimodal reasoning across text, image, audio, and video inputs, supports native tool use, and processes up to a 1 million token context window—all while keeping latency and cost exceptionally low .
Gemini 2.0 Flash‑Lite is Google DeepMind’s most cost-efficient, low-latency variant of the Gemini 2.0 Flash model, now publicly available in preview. It delivers fast, multimodal reasoning across text, image, audio, and video inputs, supports native tool use, and processes up to a 1 million token context window—all while keeping latency and cost exceptionally low .
Gemini 2.0 Flash‑Lite is Google DeepMind’s most cost-efficient, low-latency variant of the Gemini 2.0 Flash model, now publicly available in preview. It delivers fast, multimodal reasoning across text, image, audio, and video inputs, supports native tool use, and processes up to a 1 million token context window—all while keeping latency and cost exceptionally low .
Gemini 1.5 Pro
Gemini 1.5 Pro is Google DeepMind’s mid-size multimodal model, using a mixture-of-experts (MoE) architecture to deliver high performance with lower compute. It supports text, images, audio, video, and code, and features an experimental context window up to 1 million tokens—the longest among widely available models. It excels in long-document reasoning, multimodal understanding, and in-context learning.
Gemini 1.5 Pro
Gemini 1.5 Pro is Google DeepMind’s mid-size multimodal model, using a mixture-of-experts (MoE) architecture to deliver high performance with lower compute. It supports text, images, audio, video, and code, and features an experimental context window up to 1 million tokens—the longest among widely available models. It excels in long-document reasoning, multimodal understanding, and in-context learning.
Gemini 1.5 Pro
Gemini 1.5 Pro is Google DeepMind’s mid-size multimodal model, using a mixture-of-experts (MoE) architecture to deliver high performance with lower compute. It supports text, images, audio, video, and code, and features an experimental context window up to 1 million tokens—the longest among widely available models. It excels in long-document reasoning, multimodal understanding, and in-context learning.
Meta Llama 4
Meta Llama 4 is the latest generation of Meta’s large language model series. It features a mixture-of-experts (MoE) architecture, making it both highly efficient and powerful. Llama 4 is natively multimodal—supporting text and image inputs—and offers three key variants: Scout (17B active parameters, 10 M token context), Maverick (17B active, 1 M token context), and Behemoth (288B active, 2 T total parameters; still in development). Designed for long-context reasoning, multilingual understanding, and open-weight availability (with license restrictions), Llama 4 excels in benchmarks and versatility.
Meta Llama 4
Meta Llama 4 is the latest generation of Meta’s large language model series. It features a mixture-of-experts (MoE) architecture, making it both highly efficient and powerful. Llama 4 is natively multimodal—supporting text and image inputs—and offers three key variants: Scout (17B active parameters, 10 M token context), Maverick (17B active, 1 M token context), and Behemoth (288B active, 2 T total parameters; still in development). Designed for long-context reasoning, multilingual understanding, and open-weight availability (with license restrictions), Llama 4 excels in benchmarks and versatility.
Meta Llama 4
Meta Llama 4 is the latest generation of Meta’s large language model series. It features a mixture-of-experts (MoE) architecture, making it both highly efficient and powerful. Llama 4 is natively multimodal—supporting text and image inputs—and offers three key variants: Scout (17B active parameters, 10 M token context), Maverick (17B active, 1 M token context), and Behemoth (288B active, 2 T total parameters; still in development). Designed for long-context reasoning, multilingual understanding, and open-weight availability (with license restrictions), Llama 4 excels in benchmarks and versatility.
Meta Llama 3
Meta Llama 3 is Meta’s third-generation open-weight large language model family, released in April 2024 and enhanced in July 2024 with the 3.1 update. It spans three sizes—8B, 70B, and 405B parameters—each offering a 128K‑token context window. Llama 3 excels at reasoning, code generation, multilingual text, and instruction-following, and introduces multimodal vision (image understanding) capabilities in its 3.2 series. Robust safety mechanisms like Llama Guard 3, Code Shield, and CyberSec Eval 2 ensure responsible output.
Meta Llama 3
Meta Llama 3 is Meta’s third-generation open-weight large language model family, released in April 2024 and enhanced in July 2024 with the 3.1 update. It spans three sizes—8B, 70B, and 405B parameters—each offering a 128K‑token context window. Llama 3 excels at reasoning, code generation, multilingual text, and instruction-following, and introduces multimodal vision (image understanding) capabilities in its 3.2 series. Robust safety mechanisms like Llama Guard 3, Code Shield, and CyberSec Eval 2 ensure responsible output.
Meta Llama 3
Meta Llama 3 is Meta’s third-generation open-weight large language model family, released in April 2024 and enhanced in July 2024 with the 3.1 update. It spans three sizes—8B, 70B, and 405B parameters—each offering a 128K‑token context window. Llama 3 excels at reasoning, code generation, multilingual text, and instruction-following, and introduces multimodal vision (image understanding) capabilities in its 3.2 series. Robust safety mechanisms like Llama Guard 3, Code Shield, and CyberSec Eval 2 ensure responsible output.
Janus-Pro-7B
anus Pro 7B is DeepSeek’s flagship open-source multimodal AI model, unifying vision understanding and text-to-image generation within a single transformer architecture. Built on DeepSeek‑LLM‑7B, it uses a decoupled visual encoding approach paired with SigLIP‑L and VQ tokenizer, delivering superior visual fidelity, prompt alignment, and stability across tasks—benchmarked ahead of OpenAI’s DALL‑E 3 and Stable Diffusion variants.
Janus-Pro-7B
anus Pro 7B is DeepSeek’s flagship open-source multimodal AI model, unifying vision understanding and text-to-image generation within a single transformer architecture. Built on DeepSeek‑LLM‑7B, it uses a decoupled visual encoding approach paired with SigLIP‑L and VQ tokenizer, delivering superior visual fidelity, prompt alignment, and stability across tasks—benchmarked ahead of OpenAI’s DALL‑E 3 and Stable Diffusion variants.
Janus-Pro-7B
anus Pro 7B is DeepSeek’s flagship open-source multimodal AI model, unifying vision understanding and text-to-image generation within a single transformer architecture. Built on DeepSeek‑LLM‑7B, it uses a decoupled visual encoding approach paired with SigLIP‑L and VQ tokenizer, delivering superior visual fidelity, prompt alignment, and stability across tasks—benchmarked ahead of OpenAI’s DALL‑E 3 and Stable Diffusion variants.
Claude 3 Opus
Claude 3 Opus is Anthropic’s flagship Claude 3 model, released March 4, 2024. It offers top-tier performance for deep reasoning, complex code, advanced math, and multimodal understanding—including charts and documents—supported by a 200K‑token context window (extendable to 1 million in select enterprise cases). It consistently outperforms GPT‑4 and Gemini Ultra on benchmark tests like MMLU, HumanEval, HellaSwag, and more.
Claude 3 Opus
Claude 3 Opus is Anthropic’s flagship Claude 3 model, released March 4, 2024. It offers top-tier performance for deep reasoning, complex code, advanced math, and multimodal understanding—including charts and documents—supported by a 200K‑token context window (extendable to 1 million in select enterprise cases). It consistently outperforms GPT‑4 and Gemini Ultra on benchmark tests like MMLU, HumanEval, HellaSwag, and more.
Claude 3 Opus
Claude 3 Opus is Anthropic’s flagship Claude 3 model, released March 4, 2024. It offers top-tier performance for deep reasoning, complex code, advanced math, and multimodal understanding—including charts and documents—supported by a 200K‑token context window (extendable to 1 million in select enterprise cases). It consistently outperforms GPT‑4 and Gemini Ultra on benchmark tests like MMLU, HumanEval, HellaSwag, and more.
Meta Llama 4 Scout
Llama 4 Scout is Meta’s compact and high-performance entry in the Llama 4 family, released April 5, 2025. Built on a mixture-of-experts (MoE) architecture with 17B active parameters (109B total) and a staggering 10‑million-token context window, it delivers top-tier speed and long-context reasoning while fitting on a single Nvidia H100 GPU. It outperforms models like Google's Gemma 3, Gemini 2.0 Flash‑Lite, and Mistral 3.1 across benchmarks.
Meta Llama 4 Scout
Llama 4 Scout is Meta’s compact and high-performance entry in the Llama 4 family, released April 5, 2025. Built on a mixture-of-experts (MoE) architecture with 17B active parameters (109B total) and a staggering 10‑million-token context window, it delivers top-tier speed and long-context reasoning while fitting on a single Nvidia H100 GPU. It outperforms models like Google's Gemma 3, Gemini 2.0 Flash‑Lite, and Mistral 3.1 across benchmarks.
Meta Llama 4 Scout
Llama 4 Scout is Meta’s compact and high-performance entry in the Llama 4 family, released April 5, 2025. Built on a mixture-of-experts (MoE) architecture with 17B active parameters (109B total) and a staggering 10‑million-token context window, it delivers top-tier speed and long-context reasoning while fitting on a single Nvidia H100 GPU. It outperforms models like Google's Gemma 3, Gemini 2.0 Flash‑Lite, and Mistral 3.1 across benchmarks.
Meta Llama 3.2
Llama 3.2 is Meta’s multimodal and lightweight update to its Llama 3 line, released on September 25, 2024. The family includes 1B and 3B text-only models optimized for edge devices, as well as 11B and 90B Vision models capable of image understanding. It offers a 128K-token context window, Grouped-Query Attention for efficient inference, and opens up on-device, private AI with strong multilingual (e.g. Hindi, Spanish) support.
Meta Llama 3.2
Llama 3.2 is Meta’s multimodal and lightweight update to its Llama 3 line, released on September 25, 2024. The family includes 1B and 3B text-only models optimized for edge devices, as well as 11B and 90B Vision models capable of image understanding. It offers a 128K-token context window, Grouped-Query Attention for efficient inference, and opens up on-device, private AI with strong multilingual (e.g. Hindi, Spanish) support.
Meta Llama 3.2
Llama 3.2 is Meta’s multimodal and lightweight update to its Llama 3 line, released on September 25, 2024. The family includes 1B and 3B text-only models optimized for edge devices, as well as 11B and 90B Vision models capable of image understanding. It offers a 128K-token context window, Grouped-Query Attention for efficient inference, and opens up on-device, private AI with strong multilingual (e.g. Hindi, Spanish) support.
Mistral Magistral
Magistral is Mistral AI’s first dedicated reasoning model, released on June 10, 2025, available in two versions: open-source 24 B Magistral Small and enterprise-grade Magistral Medium. It’s built to provide transparent, multilingual, domain-specific chain-of-thought reasoning, excelling in step-by-step logic tasks like math, finance, legal, and engineering.
Mistral Magistral
Magistral is Mistral AI’s first dedicated reasoning model, released on June 10, 2025, available in two versions: open-source 24 B Magistral Small and enterprise-grade Magistral Medium. It’s built to provide transparent, multilingual, domain-specific chain-of-thought reasoning, excelling in step-by-step logic tasks like math, finance, legal, and engineering.
Mistral Magistral
Magistral is Mistral AI’s first dedicated reasoning model, released on June 10, 2025, available in two versions: open-source 24 B Magistral Small and enterprise-grade Magistral Medium. It’s built to provide transparent, multilingual, domain-specific chain-of-thought reasoning, excelling in step-by-step logic tasks like math, finance, legal, and engineering.
Mistral Nemotron
Mistral Nemotron is a preview large language model, jointly developed by Mistral AI and NVIDIA, released on June 11, 2025. Optimized by NVIDIA for inference using TensorRT-LLM and vLLM, it supports a massive 128K-token context window and is built for agentic workflows—excelling in instruction-following, function calling, and code generation—while delivering state-of-the-art performance across reasoning, math, coding, and multilingual benchmarks.
Mistral Nemotron
Mistral Nemotron is a preview large language model, jointly developed by Mistral AI and NVIDIA, released on June 11, 2025. Optimized by NVIDIA for inference using TensorRT-LLM and vLLM, it supports a massive 128K-token context window and is built for agentic workflows—excelling in instruction-following, function calling, and code generation—while delivering state-of-the-art performance across reasoning, math, coding, and multilingual benchmarks.
Mistral Nemotron
Mistral Nemotron is a preview large language model, jointly developed by Mistral AI and NVIDIA, released on June 11, 2025. Optimized by NVIDIA for inference using TensorRT-LLM and vLLM, it supports a massive 128K-token context window and is built for agentic workflows—excelling in instruction-following, function calling, and code generation—while delivering state-of-the-art performance across reasoning, math, coding, and multilingual benchmarks.
Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
If you have any suggestions or questions, email us at hello@aitoolbook.ai