Salt Technologies AI AI

LLM Model Comparison 2026

16 models, 7 providers, 22 fields. Compare GPT-4.1, Claude 4.5, Gemini 2.5, Llama 4, DeepSeek, and more: pricing, benchmark scores, latency, context windows, and API features.

Download CSV View Methodology
Dataset Overview

Records

16

Fields

22

Format

CSV, JSON

License

CC BY 4.0

Version

Q1 2026 v2

Updated

2026-02-18

Publisher

Salt Technologies AI

Source: Salt Technologies AI

CSV JSON

Key Highlights

Quick reference for the leading LLMs across cost, benchmarks, and capabilities in Q1 2026.

Cheapest Production

Mistral Small 3.2

$0.06 / 1M input tokens

Open source, 24B params

Highest MMLU

DeepSeek R1

90.8 MMLU score

Open source (MIT), 97.3 MATH

Best Reasoning

o3

95.2 HumanEval, 96.7 MATH

80% cheaper since launch

Largest Context

Llama 4 Scout

10 million tokens

Open source, multimodal

Pricing per Million Tokens

Input and output cost comparison across all 16 models. Sorted by input cost, lowest first. All figures in USD.

Input cost
Output cost
per 1M tokens (USD) · scale capped at $15
Mistral Small 3.2 OSS
$0.06 / $0.18
Llama 4 Scout OSS
$0.11 / $0.34
Llama 4 Maverick OSS
$0.20 / $0.60
DeepSeek V3 OSS
$0.25 / $1.10
Gemini 2.5 Flash
$0.30 / $2.50
GPT-4.1 mini
$0.40 / $1.60
Mistral Large 3 OSS
$0.50 / $1.50
DeepSeek R1 OSS
$0.55 / $2.19
Claude Haiku 4.5
$1.00 / $5.00
o4-mini
$1.10 / $4.40
Gemini 2.5 Pro
$1.25 / $10.00
GPT-4.1
$2.00 / $8.00
o3
$2.00 / $8.00
Command A
$2.50 / $10.00
Claude Sonnet 4.5
$3.00 / $15.00
Claude Opus 4.5
$5.00 / $25.00
▸ $25
$0 $15+

Full Model Comparison: Pricing and Specifications

All 16 models with pricing, context windows, parameters, and provider details. Scroll horizontally on mobile.

LLM pricing and specifications as of Q1 2026
Model Provider Parameters Context Input $/1M Output $/1M Multimodal Open Source Training Cutoff
GPT-4.1 OpenAI Undisclosed 1M $2.00 $8.00 Yes No Jun 2024
GPT-4.1 mini OpenAI Undisclosed 1M $0.40 $1.60 Yes No Jun 2024
o4-mini OpenAI Undisclosed 200K $1.10 $4.40 Yes No Jun 2024
o3 OpenAI Undisclosed 200K $2.00 $8.00 Yes No Jun 2024
Claude Sonnet 4.5 Anthropic Undisclosed 200K $3.00 $15.00 Yes No Apr 2025
Claude Haiku 4.5 Anthropic Undisclosed 200K $1.00 $5.00 Yes No Apr 2025
Claude Opus 4.5 Anthropic Undisclosed 200K $5.00 $25.00 Yes No Apr 2025
Gemini 2.5 Pro Google Undisclosed 1M $1.25 $10.00 Yes No Jan 2025
Gemini 2.5 Flash Google Undisclosed 1M $0.30 $2.50 Yes No Jan 2025
Llama 4 Scout Meta 17B active (16 experts) 10M $0.11 $0.34 Yes Yes Dec 2024
Llama 4 Maverick Meta 17B active (128 experts) 10M $0.20 $0.60 Yes Yes Dec 2024
DeepSeek V3 DeepSeek 671 (37B active) 128K $0.25 $1.10 No Yes Dec 2024
DeepSeek R1 DeepSeek 671 (37B active) 128K $0.55 $2.19 No Yes Dec 2024
Mistral Large 3 Mistral AI 675 (41B active) 256K $0.50 $1.50 Yes Yes Jun 2025
Mistral Small 3.2 Mistral AI 24 128K $0.06 $0.18 No Yes Mar 2025
Command A Cohere Undisclosed 256K $2.50 $10.00 No No Mar 2024

Open-source model pricing reflects typical costs via inference providers (Together AI, Groq, Fireworks). Self-hosted costs vary by infrastructure.

Benchmark Scores

Performance scores across four industry-standard benchmarks. Higher is better. Null indicates no verified score published.

LLM benchmark scores and latency as of Q1 2026
Model Provider MMLU HumanEval MATH MT-Bench TTFT Throughput (TPS) Best For
GPT-4.1 OpenAI 86.5 90.2 80.4 9.2 ~400ms 80-190 General-purpose enterprise AI, long-context tasks, tool use, code generation
GPT-4.1 mini OpenAI 83.5 87.5 72.0 8.8 ~200ms 120-180 High-volume chatbots, classification, summarization, cost-sensitive production workloads
o4-mini OpenAI 83.2 93.4 96.7 — ~2-10s 30-60 Complex reasoning, math, coding, visual tasks, cost-efficient reasoning workloads
o3 OpenAI 87.5 95.2 96.7 — ~3-15s 20-50 Hardest reasoning tasks, agentic workflows, science, mission-critical accuracy
Claude Sonnet 4.5 Anthropic 89.0 93.0 78.5 9.2 ~400ms 70-90 Complex reasoning, long-document analysis, code review, nuanced conversation
Claude Haiku 4.5 Anthropic 80.0 89.5 72.0 8.6 ~200ms 120-150 Fast customer support, multi-agent systems, real-time classification, high-throughput tasks
Claude Opus 4.5 Anthropic 89.5 91.0 76.0 9.3 ~600ms 40-60 Mission-critical accuracy, nuanced analysis, complex writing, regulated industries
Gemini 2.5 Pro Google 87.2 84.0 78.0 9.0 ~500ms 60-80 Long-context RAG, document processing, video/audio analysis, agentic applications
Gemini 2.5 Flash Google 83.6 82.0 73.1 8.6 ~150ms 150-200 Cost-efficient production workloads, large context tasks, multimodal processing
Llama 4 Scout Meta 79.6 82.0 70.5 8.3 ~200-600ms 100-600 Massive context (10M tokens), multimodal, on-premises deployment, cost optimization
Llama 4 Maverick Meta 85.5 88.0 78.5 8.7 ~300-1000ms 50-560 Best open-source all-around performance, data sovereignty, custom fine-tuning
DeepSeek V3 DeepSeek 88.5 82.6 90.2 8.8 ~300-1000ms 50-100 Cost-efficient reasoning, math-heavy tasks, code generation, open-source GPT-4 alternative
DeepSeek R1 DeepSeek 90.8 85.3 97.3 — ~2-15s 20-50 Advanced reasoning, mathematical proofs, scientific analysis, research tasks
Mistral Large 3 Mistral AI 85.5 90.2 83.5 8.5 ~350ms 60-80 European data residency, multilingual enterprise, coding, open-source frontier model
Mistral Small 3.2 Mistral AI 72.2 75.0 60.0 8.1 ~100ms 150-200 Ultra-low-cost classification, routing, edge deployment, cost-efficient European workloads
Command A Cohere 71.2 68.0 53.0 8.2 ~280ms 60-80 Enterprise RAG, grounded generation with citations, multilingual search, agentic workflows

MMLU: Massive Multitask Language Understanding (0-100)

HumanEval: Code generation accuracy (0-100)

MATH: Mathematical problem solving (0-100)

MT-Bench: Multi-turn instruction following (0-10)

Version: Q1 2026 v2 · Last updated: 2026-02-18 · License: CC BY 4.0

API Features and Capabilities

Feature support across all 16 models. Critical for production integration decisions.

LLM API feature support as of Q1 2026
Model Function Calling JSON Mode Streaming Fine-Tuning Enterprise Ready
GPT-4.1 Yes Yes Yes Yes Yes
GPT-4.1 mini Yes Yes Yes Yes Yes
o4-mini Yes Yes Yes No Yes
o3 Yes Yes Yes No Yes
Claude Sonnet 4.5 Yes Yes Yes No Yes
Claude Haiku 4.5 Yes Yes Yes No Yes
Claude Opus 4.5 Yes Yes Yes No Yes
Gemini 2.5 Pro Yes Yes Yes Yes Yes
Gemini 2.5 Flash Yes Yes Yes No Yes
Llama 4 Scout Yes Yes Yes Yes No
Llama 4 Maverick Yes Yes Yes Yes No
DeepSeek V3 Yes Yes Yes Yes No
DeepSeek R1 No No Yes No No
Mistral Large 3 Yes Yes Yes Yes Yes
Mistral Small 3.2 Yes Yes Yes Yes Yes
Command A Yes Yes Yes Yes Yes

LLM Selection Guide by Use Case

Which model to use depends on your use case, budget, and requirements.

Customer Support Chatbot

High volume, fast response, cost-sensitive

GPT-4.1 mini Gemini 2.5 Flash Claude Haiku 4.5

RAG Knowledge Base

Long-context retrieval, accuracy critical

Claude Sonnet 4.5 GPT-4.1 Command A

AI Agent with Tool Use

Multi-step reasoning, function calling, reliability

GPT-4.1 Claude Sonnet 4.5 o4-mini

Complex Reasoning and Math

Scientific analysis, proofs, multi-step problem solving

o3 DeepSeek R1 o4-mini

Document Processing at Scale

High volume, large documents, budget-constrained

Gemini 2.5 Flash Llama 4 Scout DeepSeek V3

On-Premises / Data Sovereignty

No data leaves your infrastructure

Llama 4 Maverick DeepSeek V3 Mistral Large 3

Data Dictionary

Schema documentation for all 22 fields in this dataset.

Data dictionary for LLM Model Comparison 2026
Field Type Description Example
model string Official model name as listed by the provider. GPT-4o
provider string Company that develops and/or hosts the model. 7 providers. OpenAI
parametersBillions string Model parameter count in billions. "Undisclosed" for closed-source models. 70
contextWindow string Maximum supported input context in tokens. 128K
trainingCutoff string Date of training data cutoff as reported by the provider. Oct 2023
inputCostPer1M number (USD) Cost per 1 million input tokens in USD (pay-as-you-go). 2.50
outputCostPer1M number (USD) Cost per 1 million output tokens in USD (pay-as-you-go). 10.00
pricingNote string Additional context about pricing model or billing structure. Pay-as-you-go API
openSource boolean Whether the model weights are publicly available for download. true
multimodal boolean Whether the model accepts image, audio, or video input. true
functionCalling boolean Whether the model supports structured function/tool calling. true
jsonMode boolean Whether the model supports guaranteed JSON output formatting. true
streaming boolean Whether the API supports streaming token-by-token responses. true
fineTuning boolean Whether the provider offers a fine-tuning API for this model. true
enterpriseReady boolean Whether the provider offers enterprise SLAs, SOC2, BAAs, and dedicated support. true
mmluScore number | null MMLU benchmark score (0-100). Tests broad knowledge and reasoning. 88.7
humanEvalScore number | null HumanEval benchmark score (0-100). Tests code generation accuracy. 90.2
mathScore number | null MATH benchmark score (0-100). Tests mathematical problem solving. 76.6
mtBenchScore number | null MT-Bench score (0-10). Tests multi-turn instruction following quality. 9.3
latencyTTFTMs string Time to first token in milliseconds. Median of 100 requests from US-East. ~300ms
throughputTPS string Output throughput in tokens per second (TPS). 80-100
bestFor string Recommended enterprise use cases based on production experience. General-purpose enterprise AI

Methodology

How this comparison data was collected, verified, and validated.

This dataset combines three categories of 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. Open-source model pricing reflects median costs across major inference providers (Together AI, Groq, Fireworks AI, DeepInfra). (2) Benchmark scores: MMLU, HumanEval, MATH, and MT-Bench scores are taken from the original model papers, provider-published technical reports, or verified third-party evaluations (LMSYS Chatbot Arena, Stanford HELM, Artificial Analysis). Where multiple evaluations exist, we use the official provider-reported score. Null values indicate that the provider has not published a verified score for that benchmark. Note: as the industry transitions to newer evaluation suites (MMLU Pro, SWE-bench, GPQA), 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 (500-token input, 200-token output) against each provider's production API endpoint from US-East regions. Measurements represent the median of 100 sequential requests during off-peak hours. Self-hosted and inference-provider latency varies by hardware and provider; ranges shown reflect typical deployments on H100/H200 GPUs. API feature flags (function calling, JSON mode, streaming, fine-tuning) reflect documented GA features as of the dataset date.

Cite This Dataset

This dataset is published under the CC BY 4.0 license. Use the citations below to attribute the data in your research, reports, or content.

APA

Salt Technologies AI. (2026). LLM Model Comparison for Enterprise Use Cases (2026) (Version Q1 2026 v2) [Dataset]. https://www.salttechno.ai/datasets/llm-model-comparison-2026/

BibTeX

@misc{salttechnoai_llm_model_comparison_for_enterprise_use_cases_2026__2026,
  title     = {LLM Model Comparison for Enterprise Use Cases (2026)},
  author    = {Salt Technologies AI},
  year      = {2026},
  version   = {Q1 2026 v2},
  url       = {https://www.salttechno.ai/datasets/llm-model-comparison-2026/},
  note      = {Licensed under CC BY 4.0}
}

Mirrors and Alternate Downloads

This dataset is available on multiple platforms. Use whichever format or platform fits your workflow.

Hugging Face

Browse, preview, and load directly into Python with datasets library

Kaggle

Download CSV, explore in notebooks, and fork for your own analysis

GitHub

Source repository with CSV, JSON, and version history via Git

Zenodo

Archived with DOI for academic citation and long-term preservation

Figshare

Citable research data repository with DOI and versioned file hosting

The canonical source for this dataset is www.salttechno.ai. Mirror platforms may have a slight delay in reflecting the latest updates.

Version History

This dataset is updated quarterly. All previous versions are documented below.

Version history for LLM Model Comparison 2026
Version Date Changes
Q1 2026 v2 2026-02-18 Major update: replaced all models with latest generations. Added GPT-4.1/4.1 mini (1M context), o3/o4-mini, Claude 4.5 family, Gemini 2.5 Pro/Flash, Llama 4 Scout/Maverick (10M context), DeepSeek V3, Mistral Large 3 (open source), and Command A. Removed deprecated GPT-4o, Claude 3.5, Gemini 2.0/1.5, Llama 3.x, and older Mistral/Cohere models.
Q1 2026 2026-02-15 Initial release with 16 LLM models across 7 providers and 22 fields including benchmark scores, latency metrics, and API feature flags.

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Frequently Asked Questions

Which LLM is best for enterprise chatbots in 2026?
For enterprise chatbots, GPT-4.1 and Claude Sonnet 4.5 are the top choices. GPT-4.1 offers a 1M token context window with function calling, JSON mode, and fine-tuning support at $2.00/$8.00 per million tokens. Claude Sonnet 4.5 excels at nuanced conversation and complex reasoning at $3.00/$15.00. For cost-sensitive deployments, GPT-4.1 mini ($0.40/$1.60), Gemini 2.5 Flash ($0.30/$2.50), or Claude Haiku 4.5 ($1.00/$5.00) provide strong performance at 5x to 10x lower cost.
What is the cheapest LLM for production use in 2026?
Mistral Small 3.2 is the cheapest production LLM at $0.06/$0.18 per million tokens. Llama 4 Scout is next at $0.11/$0.34 via inference providers with a massive 10M token context window. Gemini 2.5 Flash offers the best value among major commercial APIs at $0.30/$2.50 with a 1M context window. DeepSeek V3 provides frontier-level reasoning at $0.25/$1.10 as open source.
Which LLM has the highest benchmark scores?
As of Q1 2026, DeepSeek R1 has the highest MMLU score at 90.8. On MATH benchmarks, DeepSeek R1 leads at 97.3, followed by o4-mini at 96.7. For code generation (HumanEval), o3 leads at 95.2, followed by o4-mini at 93.4 and Claude Sonnet 4.5 at 93.0. For instruction following (MT-Bench), Claude Opus 4.5 leads at 9.3 out of 10.
Which LLM has the largest context window?
Llama 4 Scout and Llama 4 Maverick have the largest context windows at 10 million tokens, an unprecedented size for open-source models. GPT-4.1, GPT-4.1 mini, Gemini 2.5 Pro, and Gemini 2.5 Flash support 1 million tokens. Mistral Large 3 and Command A offer 256K token contexts. Claude models support 200K tokens.
Should I use an open-source LLM or a commercial API?
Use open-source models (Llama 4, DeepSeek, Mistral Large 3) when you need full data control, on-premises deployment, regulatory compliance requiring data residency, or want to eliminate per-token API costs at high volume. The open-source landscape has improved dramatically: Llama 4 Maverick and DeepSeek V3 now rival commercial models in quality. Use commercial APIs (GPT-4.1, Claude, Gemini) when you need the fastest time to production, managed infrastructure, and enterprise support (SLAs, SOC2, BAAs). Most Salt Technologies AI projects start with commercial APIs and evaluate open-source alternatives once usage patterns are established.
How do LLM costs compare for a typical enterprise chatbot?
A chatbot handling 10,000 conversations per month (averaging 2,000 tokens per conversation) costs approximately: $200/month with GPT-4.1, $40/month with GPT-4.1 mini, $360/month with Claude Sonnet 4.5, $56/month with Gemini 2.5 Flash, $27/month with DeepSeek V3, $9/month with Llama 4 Scout. These are API costs only; infrastructure, development, and maintenance are separate.
What benchmark scores does this dataset include?
Each model is scored on four industry-standard benchmarks: MMLU (Massive Multitask Language Understanding, 0-100), HumanEval (code generation accuracy, 0-100), MATH (mathematical problem solving, 0-100), and MT-Bench (multi-turn instruction following, 0-10). Scores are sourced from official provider reports and verified third-party evaluations. Null values indicate the provider has not published a verified score for that benchmark.
How often is this comparison updated?
The LLM Model Comparison is updated quarterly to reflect new model releases, pricing changes, and updated benchmark scores. The current version is Q1 2026 v2, last updated February 2026. All previous versions are documented in the changelog.

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