| # Methodology |
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| ## LLM Model Comparison for Enterprise Use Cases (2026) |
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| Published by [Salt Technologies AI](https://www.salttechno.ai) | Version: Q1 2026 v2 | Last updated: February 18, 2026 |
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| ## Data Categories |
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| This dataset combines three categories of independently sourced and verified data. |
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| ### 1. Specifications and Pricing |
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| - Sourced directly from **official provider documentation** and API pricing pages as of February 2026 |
| - Pricing reflects **pay-as-you-go API rates** in USD |
| - Volume discounts, committed-use pricing, and prompt caching discounts are excluded from the primary cost fields (noted in `pricingNote` where applicable) |
| - Open-source model pricing reflects **median costs across major inference providers**: Together AI, Groq, Fireworks AI, and DeepInfra |
| - Context window sizes, parameter counts, training cutoffs, and feature flags reflect documented specifications |
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| ### 2. Benchmark Scores |
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| Four industry-standard benchmarks are included: |
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| | Benchmark | What It Measures | Scale | Source | |
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| | **MMLU** | Massive Multitask Language Understanding | 0-100 | Original papers, provider reports | |
| | **HumanEval** | Code generation accuracy | 0-100 | Original papers, provider reports | |
| | **MATH** | Mathematical problem solving | 0-100 | Original papers, provider reports | |
| | **MT-Bench** | Multi-turn instruction following | 0-10 | LMSYS Chatbot Arena | |
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| **Score sourcing priority:** |
| 1. Official provider-reported scores from technical reports |
| 2. Verified third-party evaluations (LMSYS Chatbot Arena, Stanford HELM, Artificial Analysis) |
| 3. Reproduced results from peer-reviewed evaluations |
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| **Null values** indicate the provider has not published a verified score for that benchmark. We do not estimate or interpolate missing scores. |
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| **Important note:** As the industry transitions to newer evaluation suites (MMLU Pro, SWE-bench, GPQA Diamond), traditional benchmark comparisons across model generations may reflect different evaluation conditions. |
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| ### 3. Latency and Throughput |
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| - **Time-to-first-token (TTFT)** and **throughput (tokens per second)** are measured using standardized prompts: |
| - Input: 500 tokens |
| - Output: 200 tokens |
| - Measured against each provider's **production API endpoint from US-East regions** |
| - Results represent the **median of 100 sequential requests** during off-peak hours |
| - Self-hosted and inference-provider latency varies by hardware and provider; ranges reflect typical deployments on **H100/H200 GPUs** |
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| ### 4. API Feature Flags |
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| Feature flags (`functionCalling`, `jsonMode`, `streaming`, `fineTuning`, `enterpriseReady`) reflect **documented GA (Generally Available) features** as of the dataset date. Beta or preview features are excluded. |
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| ## Models Included (16) |
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| | Provider | Models | Count | |
| |---|---|---| |
| | OpenAI | GPT-4.1, GPT-4.1 mini, o3, o4-mini | 4 | |
| | Anthropic | Claude Sonnet 4.5, Claude Haiku 4.5, Claude Opus 4.5 | 3 | |
| | Google | Gemini 2.5 Pro, Gemini 2.5 Flash | 2 | |
| | Meta | Llama 4 Scout, Llama 4 Maverick | 2 | |
| | DeepSeek | DeepSeek V3, DeepSeek R1 | 2 | |
| | Mistral AI | Mistral Large 3, Mistral Small 3.2 | 2 | |
| | Cohere | Command A | 1 | |
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| ### Selection Criteria |
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| Models are included if they meet at least two of: |
| - Available via production API with published pricing |
| - Widely used in enterprise deployments (based on Salt Technologies AI project data) |
| - Top-5 performance on at least one included benchmark |
| - Open-source with significant community adoption |
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| ### Excluded Models |
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| - Research-only models without production APIs |
| - Models deprecated before the dataset date |
| - Provider-specific fine-tuned variants |
| - Models with fewer than 7B parameters (outside enterprise scope) |
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| ## Pricing Methodology |
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| | Model Type | Pricing Basis | |
| |---|---| |
| | Commercial API (OpenAI, Anthropic, Google, Cohere) | Official API pricing page, pay-as-you-go tier | |
| | Open source via inference providers (Llama 4, DeepSeek, Mistral) | Median price across Together AI, Groq, Fireworks AI, DeepInfra | |
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| Costs are per 1 million tokens. To estimate monthly costs: |
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| ``` |
| Monthly cost = (conversations/month) × (avg tokens/conversation) × (cost per 1M tokens / 1,000,000) |
| ``` |
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| **Example:** 10,000 conversations/month at 2,000 tokens each with GPT-4.1: |
| - Input: 10,000 × 1,000 × ($2.00 / 1,000,000) = $20.00 |
| - Output: 10,000 × 1,000 × ($8.00 / 1,000,000) = $80.00 |
| - Total: ~$100/month |
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| ## Limitations |
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| - Benchmark scores may not reflect real-world production performance for specific use cases |
| - Pricing changes frequently; verify current rates on provider pricing pages |
| - Latency measurements are point-in-time and vary by region, load, and prompt characteristics |
| - Parameter counts for closed-source models are undisclosed and estimated ranges are not included |
| - This dataset covers text-focused capabilities; specialized multimodal benchmarks (image, video, audio) are not included |
| - Enterprise readiness assessment is based on documented features, not independent audit |
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| ## Update Frequency |
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| Updated **quarterly** to reflect new model releases, pricing changes, and benchmark updates. Major mid-quarter model launches may trigger an interim update (indicated by version suffix, e.g., "Q1 2026 v2"). |
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| ## Contact |
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| For questions about methodology, data corrections, or partnership inquiries: |
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| - **Website:** [www.salttechno.ai](https://www.salttechno.ai) |
| - **Dataset page:** [www.salttechno.ai/datasets/llm-model-comparison-2026](https://www.salttechno.ai/datasets/llm-model-comparison-2026/) |
| - **Contact:** [www.salttechno.ai/contact](https://www.salttechno.ai/contact/) |
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