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LLM Model Comparison 2026

Which LLM should you use for enterprise AI in 2026? This open dataset compares 16 large language models from 7 providers across 22 fields: pricing, benchmark scores, context windows, latency, API features, and recommended use cases.

Published and maintained by Salt Technologies AI, the AI engineering division of Salt Technologies (14+ years, 800+ projects delivered).

License: CC BY 4.0 Dataset Version Last Updated DOI

Quick Links

What's Inside

Pricing Comparison (per 1M tokens)

Model Provider Input Output Context Open Source
GPT-4.1 OpenAI $2.00 $8.00 1M No
GPT-4.1 mini OpenAI $0.40 $1.60 1M No
o4-mini OpenAI $1.10 $4.40 200K No
o3 OpenAI $2.00 $8.00 200K No
Claude Sonnet 4.5 Anthropic $3.00 $15.00 200K No
Claude Haiku 4.5 Anthropic $1.00 $5.00 200K No
Claude Opus 4.5 Anthropic $5.00 $25.00 200K No
Gemini 2.5 Pro Google $1.25 $10.00 1M No
Gemini 2.5 Flash Google $0.30 $2.50 1M No
Llama 4 Scout Meta $0.11 $0.34 10M Yes
Llama 4 Maverick Meta $0.20 $0.60 10M Yes
DeepSeek V3 DeepSeek $0.25 $1.10 128K Yes
DeepSeek R1 DeepSeek $0.55 $2.19 128K Yes
Mistral Large 3 Mistral AI $0.50 $1.50 256K Yes
Mistral Small 3.2 Mistral AI $0.06 $0.18 128K Yes
Command A Cohere $2.50 $10.00 256K No

Benchmark Scores

Model MMLU HumanEval MATH MT-Bench
DeepSeek R1 90.8 85.3 97.3 -
Claude Opus 4.5 89.5 91.0 76.0 9.3
Claude Sonnet 4.5 89.0 93.0 78.5 9.2
DeepSeek V3 88.5 82.6 90.2 8.8
o3 87.5 95.2 96.7 -
o4-mini 83.2 93.4 96.7 -
Gemini 2.5 Pro 87.2 84.0 78.0 9.0
GPT-4.1 86.5 90.2 80.4 9.2

Data Files 

data/
  llm-model-comparison-2026.csv    # 16 records, 22 fields
  llm-model-comparison-2026.json   # Same data with schema + metadata

Schema (22 fields)

Field Type Description
model string Model name
provider string Company that created/offers the model
parametersBillions string Parameter count in billions, or "Undisclosed"
contextWindow string Maximum context window (tokens)
trainingCutoff string Training data cutoff date
inputCostPer1M number (USD) Cost per 1M input tokens
outputCostPer1M number (USD) Cost per 1M output tokens
pricingNote string Additional pricing context
openSource boolean Model weights publicly available
multimodal boolean Supports image/video/audio input
functionCalling boolean Supports structured tool calling
jsonMode boolean Guaranteed JSON output
streaming boolean Streaming token output
fineTuning boolean Fine-tuning support
enterpriseReady boolean Enterprise SLAs, SOC2, support
mmluScore number|null MMLU score (0-100)
humanEvalScore number|null HumanEval code gen score (0-100)
mathScore number|null MATH score (0-100)
mtBenchScore number|null MT-Bench score (0-10)
latencyTTFTMs string Time-to-first-token latency
throughputTPS string Tokens per second range
bestFor string Recommended use cases

Providers Covered (7)

  • OpenAI (GPT-4.1, GPT-4.1 mini, o3, o4-mini)
  • Anthropic (Claude Sonnet 4.5, Claude Haiku 4.5, Claude Opus 4.5)
  • Google (Gemini 2.5 Pro, Gemini 2.5 Flash)
  • Meta (Llama 4 Scout, Llama 4 Maverick)
  • DeepSeek (DeepSeek V3, DeepSeek R1)
  • Mistral AI (Mistral Large 3, Mistral Small 3.2)
  • Cohere (Command A)

Usage Examples

Python (pandas) 

import pandas as pd

df = pd.read_csv("data/llm-model-comparison-2026.csv")

# Cheapest models by output cost
print(df.sort_values("output_cost_per_1m_usd")[["model", "provider", "input_cost_per_1m_usd", "output_cost_per_1m_usd"]].head(5))

# Open-source models only
open_source = df[df["open_source"] == True]
print(open_source[["model", "provider", "mmlu_score", "input_cost_per_1m_usd"]])

# Models with 1M+ context window
big_context = df[df["context_window"].isin(["1M", "10M"])]
print(big_context[["model", "context_window", "input_cost_per_1m_usd"]])

JavaScript / Node.js 

import data from "./data/llm-model-comparison-2026.json" assert { type: "json" };

// Find cheapest model with function calling
const withTools = data.records
  .filter(r => r.functionCalling)
  .sort((a, b) => a.inputCostPer1M - b.inputCostPer1M);
console.log(`Cheapest with tools: ${withTools[0].model} ($${withTools[0].inputCostPer1M}/1M)`);

// Compare benchmark scores
data.records
  .filter(r => r.mmluScore !== null)
  .sort((a, b) => b.mmluScore - a.mmluScore)
  .forEach(r => console.log(`${r.model}: MMLU ${r.mmluScore}`));

R 

library(readr)

df <- read_csv("data/llm-model-comparison-2026.csv")

# Cost per million tokens by provider
aggregate(cbind(input_cost_per_1m_usd, output_cost_per_1m_usd) ~ provider, data = df, FUN = mean)

# Highest benchmark scores
df[order(-df$mmlu_score), c("model", "mmlu_score", "humaneval_score", "math_score")]

Methodology

This dataset combines three categories of data:

  1. Specifications and pricing: Sourced from official provider documentation and API pricing pages as of February 2026. Pricing reflects pay-as-you-go rates in USD. Open-source model pricing reflects median costs across inference providers (Together AI, Groq, Fireworks AI, DeepInfra).

  2. Benchmark scores: MMLU, HumanEval, MATH, and MT-Bench scores from original model papers, provider technical reports, or verified third-party evaluations (LMSYS Chatbot Arena, Stanford HELM, Artificial Analysis). Null values indicate no verified score published.

  3. Latency and throughput: TTFT and tokens-per-second measured with standardized prompts (500-token input, 200-token output) against production API endpoints from US-East. Median of 100 sequential requests during off-peak hours.

See METHODOLOGY.md for full details.

Update Schedule

This comparison is updated quarterly to reflect new model releases, pricing changes, and benchmark updates. The current version is Q1 2026 v2, last updated February 18, 2026.

See CHANGELOG.md for version history.

Citation

If you use this dataset in your research, article, or product, please cite:

Salt Technologies AI. (2026). LLM Model Comparison for Enterprise Use Cases (2026) [Dataset].
https://www.salttechno.ai/datasets/llm-model-comparison-2026/

BibTeX:

@dataset{salttechnologiesai_2026_llm_comparison,
  title     = {LLM Model Comparison for Enterprise Use Cases (2026)},
  author    = {{Salt Technologies AI}},
  year      = {2026},
  publisher = {Salt Technologies AI},
  url       = {https://www.salttechno.ai/datasets/llm-model-comparison-2026/},
  license   = {CC BY 4.0}
}

A CITATION.cff file is included for automated citation tools.

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

You are free to:

  • Share — copy and redistribute the data in any medium or format
  • Adapt — remix, transform, and build upon the data for any purpose, including commercial

As long as you:

  • Give attribution — credit Salt Technologies AI and link to the dataset page

About the Publisher

Salt Technologies AI is the AI engineering division of Salt Technologies, a software development company with 14+ years of experience, 800+ projects delivered, and a team of 100+ engineers. Rated 4.9 on Clutch.

We build AI chatbots, RAG systems, AI agents, and workflow automation for SaaS, healthcare, fintech, and e-commerce companies.