# 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](https://www.salttechno.ai), the AI engineering division of [Salt Technologies](https://www.salttechno.com) (14+ years, 800+ projects delivered).

[![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Dataset Version](https://img.shields.io/badge/Version-Q1_2026_v2-blue.svg)](https://www.salttechno.ai/datasets/llm-model-comparison-2026/)
[![Last Updated](https://img.shields.io/badge/Updated-Feb_2026-green.svg)](https://www.salttechno.ai/datasets/llm-model-comparison-2026/)
[![DOI](https://zenodo.org/badge/1161678949.svg)](https://doi.org/10.5281/zenodo.18697956)

## Quick Links

- **Interactive dataset page:** [salttechno.ai/datasets/llm-model-comparison-2026](https://www.salttechno.ai/datasets/llm-model-comparison-2026/)
- **Download JSON:** [llm-model-comparison-2026.json](https://www.salttechno.ai/datasets/llm-model-comparison-2026.json)
- **Download CSV:** [llm-model-comparison-2026.csv](https://www.salttechno.ai/datasets/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)

```python
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

```javascript
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

```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](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](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:

```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)](https://creativecommons.org/licenses/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](https://www.salttechno.ai/datasets/llm-model-comparison-2026/)

## About the Publisher

**[Salt Technologies AI](https://www.salttechno.ai)** is the AI engineering division of [Salt Technologies](https://www.salttechno.com), 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](https://www.salttechno.ai)
- Datasets: [www.salttechno.ai/datasets](https://www.salttechno.ai/datasets/)
- Service: [AI Readyness Audit](https://www.salttechno.ai/services/ai-readiness-audit/)
- Service: [AI Chatbot Development](https://www.salttechno.ai/services/ai-chatbot-development/)
- Contact: [www.salttechno.ai/contact](https://www.salttechno.ai/contact/)