Magnum v4 72B ranks higher at 25/100 vs Qwen: Qwen3 30B A3B Instruct 2507 at 22/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Qwen: Qwen3 30B A3B Instruct 2507 | Magnum v4 72B |
|---|---|---|
| Type | Model | Model |
| UnfragileRank | 22/100 | 25/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Paid |
| Starting Price | $9.00e-8 per prompt token | $3.00e-6 per prompt token |
| Capabilities | 6 decomposed | 8 decomposed |
| Times Matched | 0 | 0 |
A 30.5B-parameter mixture-of-experts (MoE) architecture that activates only 3.3B parameters per inference token, enabling efficient instruction-following through gated expert routing. The model uses a sparse gating mechanism to dynamically select which expert sub-networks process each token, reducing computational overhead while maintaining instruction comprehension across diverse task types. This architecture allows the model to specialize different experts for different instruction domains (reasoning, coding, creative writing) while keeping inference latency competitive with smaller dense models.
Unique: Uses a gated mixture-of-experts architecture with 3.3B active parameters per token (11% sparsity) rather than dense 30B activation, achieving dense-model knowledge breadth with sparse-model inference efficiency. The A3B variant specifically optimizes the expert routing and load balancing for instruction-following tasks.
vs alternatives: More cost-efficient than dense 30B models (Llama 3 30B, Mistral Large) for instruction-following while maintaining comparable quality; faster inference than full-parameter MoE models like Mixtral 8x22B due to lower active parameter count.
The model is trained on multilingual instruction-following data, enabling it to understand and respond to instructions in multiple languages (including English, Chinese, Spanish, French, German, Japanese, and others) with consistent quality. The architecture uses shared token embeddings and expert routing across languages, allowing the model to leverage cross-lingual knowledge transfer while maintaining language-specific instruction semantics. This capability enables single-model deployment for global applications without language-specific fine-tuning.
Unique: Trained on balanced multilingual instruction-following datasets with explicit optimization for non-English languages, particularly Chinese. Uses shared expert routing across languages rather than language-specific expert branches, enabling efficient cross-lingual knowledge transfer while maintaining per-language instruction semantics.
vs alternatives: More balanced multilingual performance than GPT-4 or Claude (which prioritize English) while maintaining instruction-following quality comparable to English-optimized models; more cost-effective than deploying separate language-specific models.
The model operates in non-thinking mode, meaning it generates responses directly without intermediate reasoning steps or chain-of-thought scaffolding. This design choice prioritizes inference latency and token efficiency over explicit reasoning transparency, making it suitable for real-time applications where response speed is critical. The architecture skips the overhead of generating visible reasoning traces, reducing time-to-first-token and total response latency by 20-40% compared to thinking-mode variants.
Unique: Explicitly designed for non-thinking inference mode, eliminating the computational overhead of generating intermediate reasoning steps. This is an architectural choice at training time, not a runtime parameter, meaning the model is optimized end-to-end for direct response generation rather than reasoning transparency.
vs alternatives: Significantly faster inference latency than thinking-mode variants (O1, O3) while maintaining instruction-following quality; more cost-effective for high-volume applications where reasoning traces are not required.
The model is fine-tuned on diverse instruction-following datasets covering a wide range of task types (summarization, question-answering, creative writing, coding, analysis, etc.), enabling it to generalize to novel instructions and task types not explicitly seen during training. The fine-tuning process uses instruction templates and task diversity to build robust instruction-following capabilities that transfer across domains. This enables the model to handle ad-hoc user requests and follow complex, multi-part instructions with high accuracy.
Unique: Fine-tuned on a diverse, balanced instruction-following dataset spanning 50+ task types and domains, with explicit optimization for task generalization and transfer learning. The training process uses instruction templates and task diversity to build robust instruction-following capabilities that generalize to novel task types.
vs alternatives: More consistent instruction-following quality across diverse task types than base models; comparable to GPT-4 and Claude for general-purpose instruction-following while offering better cost-efficiency through sparse activation.
The model maintains context across multiple turns of conversation, enabling it to track conversation history, reference previous statements, and generate coherent multi-turn dialogues. The architecture uses standard transformer attention mechanisms to process the full conversation history (up to the context window limit), allowing the model to understand references, maintain consistency, and build on previous exchanges. This capability enables natural, flowing conversations where the model can clarify ambiguities, correct previous statements, and maintain conversational state.
Unique: Uses standard transformer attention over full conversation history within the context window, with no explicit memory augmentation or retrieval mechanisms. The model relies on attention weights to identify and prioritize relevant context from conversation history, enabling natural context-aware responses.
vs alternatives: Simpler and more efficient than retrieval-augmented dialogue systems while maintaining natural multi-turn conversation quality; comparable to GPT-4 and Claude for multi-turn dialogue while offering better cost-efficiency.
The model can generate, analyze, and modify code based on natural language instructions, leveraging its instruction-following capabilities to understand code-related requests. It processes code snippets as input, understands code semantics through its training on code datasets, and generates syntactically correct code in multiple programming languages. The model can perform tasks like code completion, refactoring, bug fixing, and explanation based on natural language instructions, without requiring language-specific prompting or special code-handling mechanisms.
Unique: Leverages instruction-following fine-tuning to handle code tasks through natural language instructions rather than special code-handling mechanisms. The model treats code as text and uses its instruction-following capabilities to understand code-related requests, enabling flexible code generation and analysis without language-specific prompting.
vs alternatives: More flexible than specialized code models (Codex) for instruction-based code modification and analysis; comparable to GPT-4 for code generation while offering better cost-efficiency through sparse activation.
Generates natural language responses mimicking Claude 3 Sonnet/Opus writing style through fine-tuning on Qwen2.5 72B base model. Uses instruction-tuned architecture to follow complex multi-step prompts while maintaining coherent, well-structured prose with appropriate tone and formality levels. The model learns stylistic patterns from Claude outputs during fine-tuning rather than using retrieval or prompt engineering alone.
Unique: Fine-tuned specifically on Claude 3 Sonnet/Opus output patterns rather than generic instruction-tuning, creating a style-matched alternative that preserves Anthropic's prose characteristics while running on Qwen2.5's 72B architecture
vs alternatives: Offers Claude-quality writing at lower cost than Anthropic's API and with more deployment flexibility than proprietary models, though with less transparency about training methodology than fully open-source alternatives like Llama
Maintains coherent multi-turn dialogue through transformer-based attention mechanisms that track conversation history and speaker context. The instruction-tuned architecture processes entire conversation threads as input, allowing the model to reference previous exchanges, maintain consistent character/tone, and resolve pronouns and references across turns without explicit memory structures.
Unique: Inherits Qwen2.5's instruction-tuning approach to conversation, which explicitly trains on multi-turn formats with clear role markers, enabling better context resolution than models trained primarily on single-turn examples
vs alternatives: Simpler integration than systems requiring external memory stores (RAG, vector DBs) since context is handled natively, but less sophisticated than models with explicit memory architectures or retrieval-augmented approaches for very long conversations
Magnum v4 72B scores higher at 25/100 vs Qwen: Qwen3 30B A3B Instruct 2507 at 22/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Generates code snippets and technical explanations by applying instruction-tuned patterns learned from fine-tuning on Claude outputs. The model understands code context from natural language descriptions, can generate multiple programming languages, and provides explanations alongside code. Implementation relies on transformer attention over code tokens and learned associations between natural language intent and code patterns.
Unique: Fine-tuned on Claude's code generation outputs, capturing Anthropic's approach to code explanation and safety considerations (e.g., error handling suggestions) rather than pure code-to-code translation
vs alternatives: Provides better code explanations and safety context than specialized code models like CodeLlama, but likely slower and less specialized than models fine-tuned specifically on code-only datasets
Applies learned chain-of-thought reasoning patterns from Claude fine-tuning to break down complex problems into steps. The model generates intermediate reasoning steps before final answers, using transformer attention to track logical dependencies across reasoning chains. This is achieved through instruction-tuning on examples where Claude explicitly shows reasoning work.
Unique: Inherits Claude's explicit chain-of-thought training approach, which emphasizes showing reasoning work as part of the output rather than reasoning internally, making reasoning patterns visible and auditable
vs alternatives: More transparent reasoning than models without explicit chain-of-thought training, but less specialized than models fine-tuned specifically on mathematical reasoning datasets or formal logic
Condenses long-form text into summaries while preserving key information, using attention mechanisms to identify salient content and instruction-tuned patterns for summary formatting. The model learns from Claude's summarization style, which emphasizes clarity and hierarchical organization of information. Works by attending to important tokens and generating compressed representations.
Unique: Fine-tuned on Claude's summarization outputs, which emphasize hierarchical structure and clear topic organization rather than extractive summarization, producing more readable abstracts
vs alternatives: Better prose quality and readability than extractive summarization tools, but less specialized than models fine-tuned specifically on summarization tasks or using dedicated abstractive architectures
Executes complex, multi-part instructions by parsing task structure and maintaining execution context across steps. The instruction-tuned architecture learns to identify task boundaries, handle conditional logic (if-then patterns), and sequence operations correctly. Implementation relies on transformer attention to track task state and learned patterns from Claude's instruction-following training.
Unique: Trained on Claude's instruction-following patterns, which emphasize explicit acknowledgment of task structure and step-by-step execution reporting, making task progress transparent
vs alternatives: More reliable instruction-following than base models without instruction-tuning, but less specialized than models with explicit task planning architectures or reinforcement learning from human feedback on instruction compliance
Answers questions by understanding context, identifying relevant information, and generating coherent responses. Uses transformer attention to locate answer-relevant tokens and instruction-tuned patterns to format responses appropriately. The model learns from Claude's question-answering style, which emphasizes accuracy, nuance, and acknowledgment of uncertainty.
Unique: Fine-tuned on Claude's QA outputs, which emphasize acknowledging uncertainty, providing nuanced answers, and explaining reasoning rather than simple factual retrieval
vs alternatives: Better answer quality and nuance than retrieval-based QA systems, but without external knowledge bases or web search, limited to training data knowledge unlike RAG-augmented systems
Generates creative text including stories, essays, marketing copy, and other original content by learning stylistic patterns from Claude's creative outputs. The model uses transformer attention to maintain narrative coherence, character consistency, and thematic development across generated text. Fine-tuning captures Claude's approach to balancing creativity with clarity.
Unique: Fine-tuned on Claude's creative outputs, which balance imaginative storytelling with clarity and coherence, producing more readable creative content than models trained purely on internet text
vs alternatives: Better prose quality and narrative coherence than base language models, but less specialized than models fine-tuned specifically on creative writing datasets or with explicit story structure training