Updated March 2026
Independent comparison of 8 large language models — 4 proprietary and 4 open-source — across reasoning, coding, speed, cost, and safety. Pricing verified from official sources. Scores based on public benchmarks and our internal testing of 100+ business tasks.
| Model | Provider | Context | Input $/1M | Output $/1M | Vision | Open Source | Best For |
|---|---|---|---|---|---|---|---|
| GPT-5.4Frontier | OpenAI | 128K tokens | $5.00 | $15.00 | Production AI applications | ||
| Claude 4.6 OpusFrontier | Anthropic | 200K tokens (1M beta) | $15.00 | $75.00 | Complex analysis | ||
| Claude 4.6 SonnetFrontier | Anthropic | 200K tokens | $3.00 | $15.00 | Production AI agents | ||
| Gemini 3.1 ProFrontier | Google DeepMind | 1M tokens | $2.00 | $18.00 | Long-context analysis | ||
| Llama 4 MaverickOpen Source | Meta | 1M tokens | Self-host | Self-host | On-premise deployment | ||
| Mistral Large 3Open Source | Mistral AI | 128K tokens | Self-host | Self-host | Code-heavy workloads | ||
| DeepSeek V3.2Open Source | DeepSeek | 128K tokens | $0.27 | $1.10 | Budget-conscious deployments | ||
| Qwen 3.5Open Source | Alibaba Cloud | 128K tokens | Self-host | Self-host | Multilingual deployments |
Pricing as of March 2026. Open-source models can also be accessed via third-party API providers (Together AI, Fireworks, Groq) at varying rates.
Our comparison methodology combines public benchmark data, internal testing across real business tasks, and verified pricing from official sources. Scores are normalized to a 1-10 scale based on relative performance within the evaluated model set.
Reasoning
Complex multi-step logic, math, and analytical tasks (GPQA, MATH, ARC-AGI)
Coding
Code generation, debugging, and review across 20+ languages (HumanEval, SWE-bench)
Creativity
Creative writing quality, tone variety, and originality (human evaluation)
Instruction Following
Adherence to complex prompts, format requirements, and constraints (IFEval)
Speed
Tokens per second in standard inference conditions (median latency)
Cost Efficiency
Quality per dollar spent, accounting for both input and output pricing
Safety & Alignment
Refusal of harmful content, bias mitigation, and Constitutional AI adherence
Multilingual
Performance across languages, with emphasis on non-English accuracy
OpenAI · Feb 2026 · Dense Transformer (est.) · ~1.8T (est.)
Strengths
Weaknesses
Anthropic · Feb 2026 · Dense Transformer · Undisclosed
Strengths
Weaknesses
Anthropic · Feb 2026 · Dense Transformer · Undisclosed
Strengths
Weaknesses
Google DeepMind · Jan 2026 · Multimodal MoE Transformer · Undisclosed
Strengths
Weaknesses
Meta · Feb 2026 · MoE Transformer (128 experts, top-1 routing) · 400B total (17B active per token)
Strengths
Weaknesses
Mistral AI · Jan 2026 · MoE Transformer · 675B MoE
Strengths
Weaknesses
DeepSeek · Jan 2026 · MoE Transformer · 671B MoE (37B active)
Strengths
Weaknesses
Alibaba Cloud · Feb 2026 · MoE Transformer · 397B MoE
Strengths
Weaknesses
| Feature | GPT-5.4 | C4.6 Opus | C4.6 Sonnet | Gemini 3.1 Pro | Llama 4 Maverick | Mistral Large 3 | DeepSeek V3.2 | Qwen 3.5 |
|---|---|---|---|---|---|---|---|---|
| Context Window | 128K | 200K (1M beta) | 200K | 1M | 1M | 128K | 128K | 128K |
| Input Price (per 1M tokens) | $5.00 | $15.00 | $3.00 | $2.00 | Free (self-host) | Free (self-host) | $0.27 | Free (self-host) |
| Output Price (per 1M tokens) | $15.00 | $75.00 | $15.00 | $18.00 | Free (self-host) | Free (self-host) | $1.10 | Free (self-host) |
| Multimodal (Vision) | Yes | Yes | Yes | Yes (+ video, audio) | Yes | No | No | Yes |
| Tool / Function Calling | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Open Source | No | No | No | No | Yes (Community License) | Yes (Apache 2.0) | Yes (DeepSeek License) | Yes (Apache 2.0) |
| Max Output Tokens | 16,384 | 32,000 | 16,000 | 8,192 | 8,192 | 8,192 | 8,192 | 8,192 |
| Self-Hosting | No | No | No | No | Yes | Yes | Yes | Yes |
Our recommendation for each scenario, based on internal testing across 100+ business tasks.
24/7 AI agent handling customer inquiries, FAQs, and ticket routing
Sonnet's safety alignment prevents harmful responses in customer-facing roles, with strong instruction following at a competitive price point.
Writing, reviewing, and debugging production code across multiple languages
GPT-5.4 leads coding benchmarks slightly, with the largest ecosystem of developer tools and IDE integrations.
Analyzing long contracts, medical records, or regulatory filings
Opus combines the strongest reasoning with 200K context (1M beta) and industry-leading safety for regulated data.
Creating and translating content across multiple languages and markets
Qwen 3.5 supports 201 languages natively, with particularly strong Asian language capabilities.
High-volume inference where cost per token is the primary constraint
DeepSeek's API pricing ($0.27/$1.10 per 1M tokens) is 10-50× cheaper than frontier models with strong performance.
Deployments requiring full data control with no external API calls
Llama 4 Maverick's 400B MoE architecture delivers frontier-class quality with only 17B active parameters, making self-hosting practical.
Blog posts, ad copy, email campaigns, and brand content
Claude Opus produces the most natural, nuanced writing with excellent tone control and brand voice adherence.
Processing images, video, audio, and text in unified pipelines
Gemini is the only model with native video and audio understanding alongside text and images, all in one API call.
EU-based deployments requiring GDPR compliance and local hosting
Mistral is EU-headquartered (Paris), Apache 2.0 licensed, and available on European cloud infrastructure.
GPT-5.4, Claude 4.6 Opus/Sonnet, Gemini 3.1 Pro
Best when: You need top-tier quality, don't want to manage infrastructure, and API costs are acceptable for your volume.
Llama 4, Mistral Large 3, DeepSeek V3.2, Qwen 3.5
Best when: You need on-premise deployment, have high token volumes, require custom training, or need data residency compliance.
For most business AI implementations, we recommend Claude 4.6 Sonnet as the default model. It offers the best balance of quality, safety, and cost at $3/$15 per 1M tokens — with 200K context for large knowledge bases.
For complex analysis, legal documents, or research-heavy workflows, upgrade to Claude 4.6 Opus or GPT-5.4.
For clients needing on-premise deployment or custom fine-tuning, we deploy Llama 4 Maverick or Mistral Large 3 on dedicated infrastructure.
For budget-sensitive high-volume tasks (batch processing, data extraction, classification), we use DeepSeek V3.2 at 10-50x lower cost than frontier APIs.
There is no single 'best' LLM — it depends on your use case. For raw reasoning power, Claude 4.6 Opus leads. For code generation, GPT-5.4 has a slight edge. For cost efficiency at scale, DeepSeek V3.2 is unmatched. For on-premise deployment, Llama 4 Maverick offers the best balance of quality and efficiency. We recommend choosing based on your specific needs: budget, data privacy, context length, and required capabilities.
API pricing ranges dramatically: DeepSeek V3.2 starts at $0.27 per million input tokens, while Claude 4.6 Opus costs $15 per million input tokens. Open-source models like Llama 4, Mistral Large 3, and Qwen 3.5 have zero API costs when self-hosted (you pay only for compute). For most business use cases, we recommend starting with Claude 4.6 Sonnet ($3/$15 per 1M tokens) for the best balance of quality and cost.
Proprietary models (GPT-5.4, Claude, Gemini) offer the highest quality and easiest deployment via APIs. Open-source models (Llama 4, Mistral Large 3, DeepSeek V3.2, Qwen 3.5) offer lower cost, full data control, and custom fine-tuning. Choose open-source if you need on-premise deployment, have high token volumes, or require custom training. Choose proprietary if you want the highest quality with minimal infrastructure overhead.
Gemini 3.1 Pro and Llama 4 Maverick both support 1 million tokens — equivalent to roughly 750,000 words or multiple full-length books. Claude 4.6 Opus offers 200K tokens standard with 1M in beta. Most other models support 128K tokens. For processing very large documents, Gemini and Llama 4 are the clear winners.
For customer-facing chatbots, we recommend Claude 4.6 Sonnet. It combines strong reasoning, excellent safety alignment (preventing harmful or off-brand responses), 200K context for referencing large knowledge bases, and competitive pricing at $3/$15 per 1M tokens. GPT-5.4 is a strong alternative if you need the broader plugin ecosystem.
Yes — Llama 4 Maverick, Mistral Large 3, DeepSeek V3.2, and Qwen 3.5 are all available for self-hosting. Llama 4 Maverick requires approximately 8× A100/H100 GPUs due to its 400B parameter count (though only 17B are active per token). Smaller variants like Llama 4 Scout (109B total, 17B active) can run on a single high-end GPU. Proprietary models (GPT-5.4, Claude, Gemini) are API-only.
We match models to use cases. For customer support chatbots, we typically deploy Claude 4.6 Sonnet for its safety and instruction-following. For code-heavy automation, GPT-5.4 excels. For clients needing on-premise deployment or custom fine-tuning, we use Llama 4 or Mistral. For budget-sensitive high-volume tasks, DeepSeek V3.2 is our go-to. Every implementation starts with a discovery phase where we recommend the optimal model stack.
MoE (Mixture of Experts) is a model architecture where only a fraction of the total parameters are active for each input token. For example, Llama 4 Maverick has 400B total parameters but only routes to 17B active per token. This means MoE models can match the quality of much larger dense models while being significantly faster and cheaper to run. Llama 4, Mistral Large 3, DeepSeek V3.2, and Qwen 3.5 all use MoE architecture.
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