Hugging Face's logo

Datasets:
salttechno
/
LLM-Model-Comparison-2026

Modalities:
Tabular
Text
Formats:
csv
Size:
< 1K
Libraries:
Datasets
pandas
Polars
Dataset card Data Studio Files
xet
 Community
1
LLM-Model-Comparison-2026 / METHODOLOGY.md
nileshg's picture
nileshg
Upload all files
906ae5e verified
preview code
|
Raw
History Blame Contribute Delete
5.56 kB

Methodology

LLM Model Comparison for Enterprise Use Cases (2026)

Published by Salt Technologies AI | Version: Q1 2026 v2 | Last updated: February 18, 2026

Data Categories

This dataset combines three categories of independently sourced and verified data.

1. Specifications and Pricing

  • 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

2. Benchmark Scores

Four industry-standard benchmarks are included:

Benchmark What It Measures Scale Source
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

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

Null values indicate the provider has not published a verified score for that benchmark. We do not estimate or interpolate missing scores.

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.

3. Latency and Throughput

  • 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

4. API Feature Flags

Feature flags (functionCalling, jsonMode, streaming, fineTuning, enterpriseReady) reflect documented GA (Generally Available) features as of the dataset date. Beta or preview features are excluded.

Models Included (16)

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

Selection Criteria

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

Excluded Models

  • 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)

Pricing Methodology

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

Costs are per 1 million tokens. To estimate monthly costs:

Monthly cost = (conversations/month) × (avg tokens/conversation) × (cost per 1M tokens / 1,000,000)

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

Limitations

  • 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

Update Frequency

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").

Contact

For questions about methodology, data corrections, or partnership inquiries: