Open Router Models and its Strengths - Updated 13th November 2025

Allowance models, their strengths and the two most relevant tags of each.

We're happy to have you here! This is the document where we detail the strenghs of Models, LLMs, and guide you on when to use them. Don't forget to bookmark this link: https://documentation.triplo.ai/faq/open-router-models-and-its-strengths.

Below is a simple, one‐by‐one overview of each model. Each section includes a brief description, notes on what the model excels at, and suggestions for who might find it most useful.

AI21 (Jamba Large 1.7)

Long context & efficiency: Jamba Large 1.7 offers a 256K token context window and leverages a hybrid MoE (SSM-Transformer) architecture, providing high throughput and memory efficiency.
Grounded, accurate outputs: It improves grounding (fully accurate answers based on the provided context) and instruction-following (“steerability”) compared to earlier versions.
Use case focus: Well-suited for tasks needing lots of context and precise answers (e.g. finance, research), with state-of-the-art knowledge and fast inference in long-form tasks.

AionLabs (Aion-1.0, Aion-1.0-Mini)

Aion-1.0: A 131K token MoE model built on DeepSeek-R1 with Tree-of-Thought and MoE enhancements. It is Aion’s top-tier reasoning model, excelling at complex reasoning and coding tasks.
Aion-1.0-Mini (32B): A distilled version of DeepSeek-R1 with the same 131K context, optimized for math, logic, and coding. It outperforms comparable models (e.g. Qwen-32B) on reasoning benchmarks.
Features: Both models emphasize deep, step-by-step reasoning and handle very long inputs, making them ideal for logic puzzles, theorem proving, and multi-step code tasks.

Amazon (Nova Micro 1.0)

Low latency/cost: Nova Micro 1.0 is the smallest Nova model, designed for ultra-fast, low-cost inference with a 128K token context.
Text tasks strength: Excels at standard NLP tasks (summarization, translation, classification, chat, brainstorming) with quick responses.
Basic reasoning: Handles simple math and code tasks, but its main selling point is speed and efficiency rather than deep reasoning.

Anthropic (Claude series)

Claude Sonnet 4.5: Anthropic’s latest flagship. Built for complex, long-context tasks, it excels at coding, planning, and problem-solving. It leverages advanced RL training and scales inference (“thinking mode”) for in-depth reasoning.
Claude 3.7 Sonnet (200k): A hybrid “reasoning” model that can think in depth or answer quickly on demand. It introduces an extended “” mode for up to 128K tokens of internal chain-of-thought, boosting performance on math, science, and coding problems.
Claude Haiku 4.5: A smaller, efficient model focusing on coding speed. It achieves near-4★ coding performance at ~3x lower cost and 2x faster than Sonnet 4, making it ideal for real-time chatbots and rapid prototyping.
Alignment & Safety: Sonnet 4.5 and Haiku 4.5 are Anthropic’s most aligned models, reducing refusals and harmful outputs significantly.

Cohere (Command R7B, Dec 2024)

Command R7B (7B): The smallest and fastest Cohere R-family model, with 128K context. It’s state-of-the-art for its size on diverse tasks.
Efficiency focus: Optimized for speed and low cost, making it well-suited for high-volume applications and retrieval-augmented generation.
Strong reasoning & tools: Despite its size, it supports advanced reasoning and tool use. It is excellent for agentic applications (multi-step tasks, tool use, RAG) and maintains high performance on planning and synthesis tasks.

DeepSeek (Prover V2, V3.1, V3.2-Exp)

DeepSeek Prover V2: A theorem-proving expert model (Lean 4) in 7B and 671B (8-expert) versions. It achieves SOTA results on formal math benchmarks and supports an enormous 163K token context for long proofs.
DeepSeek V3.1-Terminus: An “agent-era” model with dual modes (“think” vs “non-think”). It supports 128K context in both modes and shows big gains in tool use and multi-step reasoning (e.g. coding and search tasks).
DeepSeek V3.2-Exp: An experimental successor to V3.1 that introduces “DeepSeek Sparse Attention” for more efficient long-context processing. It runs ~50% faster on long inputs while matching V3.1’s performance.
Strengths: The DeepSeek line emphasizes consistency, chain-of-thought, and cutting-edge reasoning. It’s open-source and supports advanced tools and agentic workflows (e.g. strict function calling and multi-step “Tree-of-Thought” reasoning).

Goliath (120B 6k)

Goliath 120B: A combined model merging two Llama-2 70B variants (Xwin & Euryale) via model merging. It has 120B total parameters and a 6K context.
Blend of strengths: Designed to capture complementary abilities of its two parents. While its niche is still emerging, it inherits strong roleplay and creative writing skills from both models.
Use cases: Likely a general-purpose large model, offering improved depth and breadth over a single 70B model due to the merged expertise.

Google (Gemini 2.5 Flash & Pro)

Gemini 2.5 Pro: Google’s new high-end model. Multi-modal with a 1M token context. Excels at reasoning and coding – top of leaderboards on many benchmarks.
Gemini 2.5 Flash: A lightweight sibling with the same 1M context, optimized for throughput and low latency. It’s the price-performance leader, ideal for large-scale, real-time applications requiring “thinking” abilities.
Features: Both support multi-modal input (text, images, soon audio/video) and advanced “thinking” modes (chain-of-thought). Flash is tuned for speed at scale; Pro is tuned for absolute capability.

OpenAI (GPT-4o 128K, GPT-OSS 120B)

GPT-4o (128K): Also known as GPT-4 Omni. It’s OpenAI’s latest cost-efficient small model. At 128K context, it surpasses GPT-3.5 on reasoning and code tasks.
Features: Supports text and vision (with future audio/video planned). Very strong at math, coding, and multimodal reasoning relative to prior small models.
GPT-OSS 120B: OpenAI’s first open-weight model (120B total). It matches GPT-4o-mini on reasoning tasks while running efficiently with sparse MoE. It’s Apache 2.0 licensed and optimized for tool use and chain-of-thought.
Strengths: GPT-OSS-120B is a fully open equivalent of GPT-4o-mini, strong on reasoning, instruction-following, and safety. GPT-4o with 128K context is top-of-class for tasks needing long context or low-cost inference.

Inception (Mercury)

Mercury: The world’s first commercial diffusion-based LLM (dLLM). Its generation is parallel (coarse-to-fine), making it up to 10× faster than comparable autoregressive models.
Focus on speed: Mercury Coder models achieve thousands of tokens/sec on GPUs, excelling at coding and latency-sensitive tasks (e.g. IDE autocomplete, agent reasoning).
Latest update (Nov 2025): Shows big gains in reasoning, coding, and math benchmarks, while retaining ultra-fast inference.
Use cases: Particularly strong in coding, delivering rapid, high-quality code suggestions.

Inflection (Inflection 3 Productivity)

Inflection 3 Productivity: Instruction-following model, optimized for precise outputs (especially JSON or structured data).
Features: Good at adhering strictly to guidelines and instructions. Has access to recent news.
Use cases: General-purpose assistant focusing on accuracy over creativity.

Mistral AI (Large 2411, Codestral 2508, Devstral Medium, etc.)

Mistral Large 2411: A 123B dense model with top-tier reasoning and coding. Supports a 128K token context, excellent math abilities, and built-in function-calling.
Codestral 2508: Specialized code-focused model tuned for software development tasks.
Devstral Medium: Enterprise-grade text model for software engineering, balancing performance with efficiency.
Mistral 7B Instruct: A small instruction-tuned model for quick tasks on edge devices.
Mistral Medium 3.1: Mid-scale “multimodal” model strong across tasks.
Mistral Nemo: Multi-lingual open 12B model known for solid general performance.
General strengths: The Mistral lineup trades size vs cost efficiently, prioritizing multilinguality and function calling.

Mistral AI (Mixtral 8x22B Instruct)

Mixtral 8x22B Instruct: A sparse MoE open model (8 experts of 22B, 39B active). Designed for high efficiency while outperforming all other open models on reasoning, math, and coding.
Capabilities: Native function calling, very strong in math/coding, multilingual, with a 64K context.
Use cases: Best-in-class open model for developers needing advanced reasoning and code generation without proprietary compute costs.

MoonshotAI (Kimi series)

Kimi K2: A high-end open MoE model built for general reasoning and chat.
Kimi K2 Thinking: The flagship agentic reasoning model with 256K context. Optimized for sustained chain-of-thought and tool use.
Kimi 2 Linear 48B Instruct: A 48B model with efficient “linear attention,” handling 1M token inputs and achieving up to 6× faster decoding on very long contexts.
Use cases: Kimi K2 Thinking excels at deep reasoning and long-form problem solving; Kimi Linear targets ultra-long context tasks.

NeverSleep (Lumimaid v0.2 8B)

Lumimaid v0.2 8B: A small model (finetuned on Llama 3.1) tailored for conversation, with improved data quality.
Strengths: Fast inference, solid conversational performance.
Use cases: Chat-focused applications with lightweight compute needs.

Noromaid (20B, 8K context)

Noromaid 20B: Open-source 20B model designed to run on consumer hardware.
Strengths: Maintains coherence and handles complex queries effectively.
Use cases: General-purpose assistant for developers and enthusiasts.

Nous Research (Hermes 3 & 4)

Hermes 3 (405B): Large hybrid (think-enabled) reasoning model built on Llama-3.1-405B.
Hermes 4 (405B): Updated model with significantly more data, enhanced creativity, and reduced refusals.
Features: Neutral alignment, strong math/coding abilities, and toggleable reasoning.
Use cases: Researcher/enthusiast use where open-ended, uncensored intelligence is desired.

Perplexity (Sonar series)

Sonar Search/Pro: Optimized for factual retrieval and summarization. Grounded responses with citations.
Sonar Reasoning: Real-time reasoning model for multi-step problem solving.
Sonar Reasoning Pro: Enhanced version leveraging a DeepSeek-R1 backbone for higher accuracy.
Sonar Deep Research: Expert-level model for exhaustive web research and synthesis.
Use cases: Deep, factual, and up-to-date research and reasoning tasks.

Qwen (Qwen3 series)

Qwen3-235B-A22B: Sparse MoE flagship (235B total, 22B active) with 128K context, offering top-tier coding, math, and reasoning.
Hybrid modes: “Thinking” (high-accuracy) and “Non-Thinking” (fast) modes balance depth and speed.
Qwen3-30B-A3B: Smaller MoE optimized for efficiency.
Qwen3-32B: Dense 32B model strong for general reasoning.
Qwen-Max: Larger proprietary version.
Use cases: Scalable, multilingual (119+ languages), suited for both Chinese and global applications.

SorcererLM (8x22B)

SorcererLM 8x22B: A LoRA-tuned variant of WizardLM-2 using an 8-expert MoE.
Focus: Roleplay and storytelling with expressive, personality-rich generation.
Use cases: Creative writing and conversational agents.

Unslopnemo 12B

UnslopNemo 12B: A retrained 12B Llama-2 model with cleaned data.
Strengths: Expressive, safe conversational style.
Use cases: Chatbots and lightweight research tools.

xAI (Grok 4, Grok Code Fast 1)

Grok 4: Flagship reasoning model integrating real-time web and tool use. Multi-modal and highly capable.
Grok Code Fast 1: Smaller, optimized coding model for fast agentic programming tasks.
Use cases: Grok 4 for general reasoning and chat; Grok Code Fast 1 for high-speed coding workflows.

Z.AI (GLM 4.5, 4.5 Air)

GLM-4.5: 355B MoE model (32B active) with 128K context, excelling at reasoning, coding, and tool use.
GLM-4.5-Air: Lighter 106B MoE (12B active), same architecture, cost-efficient with near-equivalent power.
Capabilities: Dual execution modes (“Thinking” and “Non-Thinking”). Outstanding for code generation and agentic applications.
Use cases: Advanced coding assistants and AI agents needing balance between reasoning depth and efficiency.

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