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).

Quick Links

• Interactive dataset page: salttechno.ai/datasets/llm-model-comparison-2026
• Download JSON: llm-model-comparison-2026.json
• Download CSV: llm-model-comparison-2026.csv

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.

• Website: Custom AI Development Company
• Datasets: www.salttechno.ai/datasets
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• Contact: www.salttechno.ai/contact