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| Pricing | Free | Free |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Routes each input token through exactly 2 of 8 expert networks per transformer layer using a learned router network, activating only 12.9B of 46.7B total parameters per forward pass. The router makes independent routing decisions per token per layer, with expert outputs combined additively. This sparse activation pattern enables inference throughput equivalent to a 12.9B dense model while maintaining GPT-3.5-level performance across benchmarks.
Unique: Uses token-level routing to 2-of-8 experts per layer with simultaneous expert and router training, achieving 27.6% parameter utilization while maintaining dense-model performance. Differs from dense models (which activate all parameters) and from other MoE designs by using learned routing per token rather than sequence-level or document-level routing.
vs alternatives: Achieves 6x faster inference than Llama 2 70B with equivalent performance by activating only 12.9B parameters per token, whereas dense models must activate all parameters regardless of task complexity.
Generates coherent, contextually-aware text across general-purpose language tasks by applying transformer decoder architecture with 32K token context window. The model was trained on open web data and achieves performance parity with GPT-3.5 on standard benchmarks (MMLU, HellaSwag, TruthfulQA, Winogrande, GSM8K, MATH, HumanEval) while maintaining lower computational cost through sparse routing. Supports both base and instruction-tuned variants, with the Instruct variant fine-tuned via supervised fine-tuning (SFT) and Direct Preference Optimization (DPO).
Unique: Achieves GPT-3.5-level performance on standard benchmarks (MMLU, HellaSwag, TruthfulQA, Winogrande, GSM8K, MATH, HumanEval) while using sparse mixture-of-experts routing to reduce inference cost. Unlike dense models of equivalent capability, Mixtral activates only 27.6% of parameters per token, enabling faster inference without performance degradation.
vs alternatives: Matches GPT-3.5 performance on standard benchmarks while being 6x faster than Llama 2 70B and fully open-source under Apache 2.0, making it the best cost-performance option for self-hosted GPT-3.5-equivalent inference at the time of release.
Evaluated across standard language model benchmarks including MMLU (knowledge), HellaSwag (common sense reasoning), TruthfulQA (factuality), Winogrande (coreference resolution), GSM8K (math), MATH (advanced math), and HumanEval (code generation). Results demonstrate performance parity with GPT-3.5 on most benchmarks, with specific scores provided for MT-Bench (8.30 for Instruct variant). Benchmark evaluation enables quantitative comparison with other models and verification of capability claims.
Unique: Evaluated across 7+ standard benchmarks (MMLU, HellaSwag, TruthfulQA, Winogrande, GSM8K, MATH, HumanEval) with documented MT-Bench score of 8.30 for Instruct variant. Provides quantitative performance comparison enabling verification of GPT-3.5-level capability claims.
vs alternatives: Demonstrates GPT-3.5-level performance on standard benchmarks while being 6x faster than Llama 2 70B and fully open-source, providing quantitative evidence of capability parity with commercial models at lower inference cost.
Base model (non-Instruct variant) has no built-in safety guardrails and will follow any instruction without refusal or content filtering. Safety behavior is not enforced through training or architecture; instead, the model relies on explicit prompting or preference optimization (as in the Instruct variant) to learn refusal behavior. This design choice prioritizes capability and flexibility over safety by default, requiring users to implement safety measures explicitly.
Unique: Base model has no built-in safety guardrails and will follow any instruction without refusal, prioritizing capability and flexibility over safety by default. Differs from Instruct variant which has learned safety behavior through DPO, and from commercial models with built-in content filtering.
vs alternatives: Provides unconstrained base model for research and fine-tuning without safety-induced refusals, whereas commercial models (GPT-3.5, Claude) have built-in safety guardrails that may interfere with capability assessment or domain-specific applications.
Generates and completes code across multiple programming languages by applying transformer decoder architecture trained on code-inclusive datasets. The model demonstrates strong performance on HumanEval benchmark and supports code generation for tasks ranging from single-function completion to multi-file refactoring. Instruction-tuned variant (Mixtral 8x7B Instruct) provides improved code understanding and explanation capabilities through supervised fine-tuning and preference optimization.
Unique: Explicitly documented as having 'strong performance' on code generation tasks with HumanEval benchmark results, achieved through training on code-inclusive datasets and instruction-tuning via SFT + DPO. Sparse routing architecture enables code generation at 6x faster inference speed than dense 70B models.
vs alternatives: Provides open-source code generation with GPT-3.5-level performance and 6x faster inference than Llama 2 70B, enabling self-hosted code completion without reliance on proprietary APIs or external services.
Generates coherent text in English, French, German, Spanish, and Italian through transformer decoder architecture trained on multilingual open web data. The model maintains language-specific performance across supported languages while using the same sparse routing mechanism as English generation. Multilingual performance is documented with benchmark results for each language, though specific scores are not detailed in available documentation.
Unique: Supports 5 European languages (English, French, German, Spanish, Italian) with documented multilingual benchmarks, trained on language-inclusive open web data. Achieves multilingual performance through unified sparse routing architecture rather than language-specific expert routing.
vs alternatives: Provides multilingual support across 5 languages with GPT-3.5-level performance in a single open-source model, eliminating the need to maintain separate language-specific instances or rely on proprietary multilingual APIs.
Follows natural language instructions and engages in multi-turn conversation through the Mixtral 8x7B Instruct variant, which is fine-tuned via supervised fine-tuning (SFT) and Direct Preference Optimization (DPO). The instruction-tuned variant achieves MT-Bench score of 8.30, positioning it as the best open-source model on this benchmark at release. The model learns to refuse harmful requests and provide helpful, harmless, and honest responses through preference optimization, though safety guardrails are not guaranteed without explicit prompting.
Unique: Fine-tuned via supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to achieve MT-Bench score of 8.30, claimed as best open-source model at release. Combines instruction-following with preference-learned safety behavior, though safety is not guaranteed without explicit prompting.
vs alternatives: Achieves MT-Bench score of 8.30 (best open-source at release) with 6x faster inference than Llama 2 70B, providing instruction-following quality comparable to GPT-3.5 while maintaining open-source licensing and self-hosting capability.
Enables efficient inference through integration with vLLM framework and Megablocks CUDA kernels, which are specifically optimized for sparse mixture-of-experts computation. The sparse activation pattern (2 of 8 experts per token) is implemented via custom CUDA kernels that avoid computing inactive expert parameters, reducing memory bandwidth and compute requirements. Inference throughput is equivalent to a 12.9B dense model despite 46.7B total parameters, achieving 6x speedup over Llama 2 70B while maintaining equivalent performance.
Unique: Integrates with vLLM and Megablocks CUDA kernels specifically optimized for sparse mixture-of-experts computation, enabling inference throughput equivalent to 12.9B dense model while maintaining 46.7B parameter capacity. Custom CUDA kernels avoid computing inactive expert parameters, reducing memory bandwidth and compute requirements.
vs alternatives: Achieves 6x faster inference than Llama 2 70B through Megablocks CUDA kernel optimization of sparse routing, whereas dense models must compute all parameters regardless of task complexity, making Mixtral significantly more efficient for production inference.
+4 more capabilities
Mistral Large processes up to 128,000 tokens in a single context window, enabling analysis of entire codebases, long documents, or multi-turn conversations without context truncation. The architecture uses optimized attention mechanisms (likely grouped-query attention based on Mistral's prior work) to maintain computational efficiency while supporting this extended context, allowing developers to maintain coherent reasoning across large information volumes without manual chunking or sliding-window strategies.
Unique: 128K context window with grouped-query attention optimization enables full-codebase and full-document analysis without external retrieval, differentiating from GPT-4's 128K (which uses standard attention) through computational efficiency gains that reduce latency penalty
vs alternatives: Larger than Claude 3.5 Sonnet's 200K context but more cost-efficient per token than GPT-4o's extended context for most enterprise use cases due to optimized attention architecture
Mistral Large implements function calling through a schema-based interface where developers define tool signatures in JSON Schema format, and the model outputs structured function calls that can be directly dispatched to registered handlers. The implementation uses constrained decoding to ensure valid JSON output matching the provided schema, preventing malformed function calls and enabling reliable tool orchestration without post-processing validation.
Unique: Uses constrained decoding with JSON Schema validation to guarantee valid function calls without post-processing, whereas competitors like GPT-4 rely on post-hoc validation of model output, reducing error rates and enabling direct dispatch
vs alternatives: More reliable than Claude's tool_use format for complex multi-step workflows because constrained decoding prevents malformed calls, and simpler to integrate than OpenAI's function calling which requires additional validation layers
Mistral Large scores higher at 77/100 vs Mixtral 8x7B at 58/100.
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Mistral Large can be deployed on-premises or in private cloud environments, enabling organizations to maintain data sovereignty and avoid sending sensitive information to external APIs. Self-hosted deployments support custom fine-tuning on proprietary datasets, enabling domain-specific optimization without sharing training data with Mistral. Deployment uses standard container formats (Docker) and supports multiple hardware backends (NVIDIA GPUs, AMD ROCm, Intel Gaudi).
Unique: Supports full self-hosted deployment with custom fine-tuning on proprietary data, enabling organizations to maintain complete control over model behavior and data, whereas most competitors restrict fine-tuning to managed services
vs alternatives: More flexible than OpenAI's fine-tuning (which is API-only) and more cost-effective than Claude for high-volume on-premises deployments due to lower licensing costs
Mistral Large achieves performance competitive with GPT-4o and Claude 3.5 Sonnet on major reasoning benchmarks including MMLU (84.0%), HumanEval, and MATH, indicating comparable capability for complex reasoning, code generation, and mathematical problem-solving. This performance is achieved with a 123B parameter model, making it more efficient than larger competitors in terms of inference cost and latency.
Unique: Achieves GPT-4o and Claude 3.5 Sonnet-level performance on major benchmarks with a 123B parameter model, enabling competitive reasoning capability at lower inference cost due to smaller model size and optimized architecture
vs alternatives: More cost-efficient than GPT-4o and Claude 3.5 Sonnet for equivalent reasoning performance, making it ideal for cost-sensitive applications where benchmark-level performance is sufficient
Mistral Large exposes temperature and top-p (nucleus sampling) parameters to control the randomness and diversity of generated outputs. Temperature scales the logit distribution (higher = more random), while top-p limits sampling to the smallest set of tokens with cumulative probability ≥ p. These parameters enable tuning the model's behavior from deterministic (temperature=0) to highly creative (temperature=2.0), allowing builders to balance consistency and diversity for different use cases.
Unique: Exposes temperature and top-p parameters with standard semantics, enabling fine-grained control over output diversity and consistency without model retraining
vs alternatives: Standard parameter set comparable to GPT-4o and Claude, with no unique advantages but consistent behavior across models
Mistral Large can be constrained to output only valid JSON matching a provided schema, using constrained decoding to enforce structural validity at generation time rather than post-processing. This ensures every generated token respects the schema constraints, preventing partial or malformed JSON and enabling reliable downstream parsing without error handling for invalid output.
Unique: Enforces schema compliance at token generation time using constrained decoding, guaranteeing valid JSON output without post-processing, whereas most competitors (including GPT-4) generate JSON then validate, allowing invalid output to be produced
vs alternatives: More efficient than Claude's JSON mode because validation happens during generation rather than after, eliminating retry loops for invalid output and reducing latency for structured extraction tasks
Mistral Large is trained on multilingual data and maintains reasoning capability across 10+ languages including English, French, Spanish, German, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, and Arabic. The model uses a shared embedding space and unified transformer architecture rather than language-specific branches, enabling cross-lingual transfer and reasoning without language-specific fine-tuning.
Unique: Unified transformer architecture with shared embeddings across 10+ languages enables consistent reasoning quality and cross-lingual transfer, whereas competitors often use separate language-specific models or language adapters that add latency
vs alternatives: More efficient than running separate language models for each language, and maintains better cross-lingual reasoning than GPT-4o which uses separate tokenizers per language
Mistral Large uses a distinct system prompt format optimized for instruction following, where system instructions are formatted as structured directives that the model interprets with higher fidelity than standard text prompts. The architecture includes special tokens and attention patterns that prioritize system instructions over user input, enabling more reliable behavior control and reducing prompt injection vulnerabilities.
Unique: Dedicated system prompt format with special tokens and attention masking prioritizes instructions over user input, reducing prompt injection risk and improving instruction adherence vs standard chat templates used by competitors
vs alternatives: More robust instruction following than GPT-4o's system message format because special tokenization prevents user input from overriding system directives, and simpler than Claude's system prompt which requires careful phrasing to avoid conflicts
+5 more capabilities