Choosing the Right LLM: OpenAI vs Anthropic vs Open Source

After integrating dozens of LLMs into production platforms, we’ve developed clear opinions on when to use each provider. This guide covers the practical tradeoffs-not benchmarks, but real-world production considerations.

Bottom line: Most production platforms should use multiple models. Use expensive models for complex reasoning, cheap models for simple tasks, and open source for cost-sensitive or privacy-critical workloads.

The current landscape (December 2025)

Closed-source leaders

Open source options

How to choose: decision framework

1. What’s the primary task?

Complex reasoning, analysis, or writing:

• Claude Opus 4.6 with extended thinking for best quality
• o3 for multi-step reasoning and planning
• GPT-5 mini for simpler reasoning at lower cost

Classification, extraction, or simple Q&A:

• Gemini 3 Flash (fastest closed-source at $0.50/$3)
• GPT-5 mini for balance of speed and quality
• Llama 4 Scout (self-hosted for volume)

Long document processing (50K+ tokens):

• Llama 4 Scout (10M context - longest available)
• Claude Opus 4.6 (200K context)
• Gemini 3 Pro (1M+ context)

Code generation and review:

• Claude Opus 4.6 (strongest at code with extended thinking)
• GPT-5 (reliable structured outputs)
• Llama 4 Maverick (open source alternative)

2. What are your cost constraints?

High volume, low margin: Use Gemini 3 Flash or GPT-5 mini for most requests. Route only complex queries to premium models.

Enterprise with budget: Use Claude Opus 4.6 or GPT-5 without much optimization. The quality difference often justifies the cost.

Self-hosted required: Llama 4 Scout runs on a single H100 with Int4 quantization - serious cost savings at scale with 10M context.

3. What are your latency requirements?

For real-time chat: Gemini 3 Flash, Haiku 4.5, or GPT-5 mini with streaming. For background processing: Quality matters more than speed - use extended thinking.

4. Do you need specific capabilities?

Function/tool calling:

• GPT-5 has the most reliable structured outputs
• Claude Opus 4.6 now has mature tool support
• Llama 4 models support function calling natively

Vision (image/video understanding):

• Llama 4 is natively multimodal (text, image, video)
• Gemini 3 Pro handles long video well
• Claude Opus 4.6 and GPT-5 both excellent for images

JSON mode / structured outputs:

• GPT-5 with response_format is most reliable
• Claude now supports structured outputs natively
• Open source: use outlines or instructor libraries

Production architecture: multi-model routing

Most production platforms shouldn’t use a single model. Here’s the pattern we recommend:

┌─────────────────────────────────────────────────────────┐
│                    Request Router                        │
│  Analyzes: complexity, token count, latency requirements │
└───────────────────────┬─────────────────────────────────┘
                        │
        ┌───────────────┼───────────────┐
        ▼               ▼               ▼
   ┌─────────┐    ┌──────────┐    ┌──────────┐
   │ Simple  │    │ Standard │    │ Complex  │
   │ Flash/  │    │ GPT-5/   │    │ Opus 4.6 │
   │ Haiku   │    │ Sonnet   │    │ extended │
   └─────────┘    └──────────┘    └──────────┘
       80%            15%             5%
    of requests    of requests    of requests

Implementation example

function routeToModel(request: LLMRequest): ModelConfig {
  // Simple classification or extraction
  if (request.taskType === 'classify' || request.estimatedTokens < 500) {
    return { model: 'claude-haiku-4-5-20251101', maxTokens: 500 };
  }

  // Long context (over 200K tokens)
  if (request.estimatedTokens > 200000) {
    return { model: 'llama-4-scout', maxTokens: 8192 }; // 10M context
  }

  // Complex reasoning requiring extended thinking
  if (request.taskType === 'reasoning' || request.taskType === 'code') {
    return { model: 'claude-opus-4-5-20251101', maxTokens: 8192, extendedThinking: true };
  }

  // Default: balance of cost and quality
  return { model: 'claude-sonnet-4-5-20250929', maxTokens: 4096 };
}

This pattern typically reduces costs by 60-80% compared to using a premium model for everything.

Provider comparison: the details

OpenAI

Strengths:

• Most reliable structured outputs and function calling
• o3 reasoning models excel at complex multi-step tasks
• Best developer tooling and documentation
• Widest ecosystem of integrations

Weaknesses:

• Rate limits can be restrictive at scale
• Premium pricing on reasoning models
• Occasional quality regressions between model versions

Best for: Startups that need to move fast, applications requiring reliable structured outputs.

Anthropic (Claude)

Strengths:

• Opus 4.6 with extended thinking is best for complex reasoning
• Strongest at code generation and review
• Haiku 4.5 offers excellent speed at $1/$5 per 1M - best value for simple tasks in Claude ecosystem
• Prompt caching saves up to 90%

Weaknesses:

• Extended thinking adds latency (2-30s)
• Smaller ecosystem than OpenAI

Best for: Enterprise applications, complex analysis, code-heavy workloads, agentic systems.

Google (Gemini)

Strengths:

• Gemini 3 Flash is fast and affordable ($0.50/$3 per 1M)
• Gemini 3 Pro handles 1M+ context well
• Native multimodal including long video
• 90% savings with context caching

Weaknesses:

• API can still be less reliable than competitors
• Reasoning models lag behind o3 and Opus 4.6

Best for: Cost-sensitive high-volume apps, long document processing, multimodal applications.

Open source (Llama 4, DeepSeek)

Strengths:

• Llama 4 Scout: 10M context on single H100
• Llama 4 is natively multimodal (text, image, video)
• DeepSeek R1 offers competitive reasoning at low cost
• Full control over data and infrastructure

Weaknesses:

• Requires ML engineering expertise for self-hosting
• GPU infrastructure costs and complexity
• Llama 4 Behemoth (2T params) not yet released

Best for: High-volume applications, privacy-sensitive workloads, massive context requirements.

Cost optimization strategies

1. Prompt caching

Both OpenAI and Anthropic offer prompt caching for repeated system prompts. This can reduce costs by 50-90% for applications with consistent system instructions.

2. Batch processing

For non-real-time workloads, batch APIs offer 50% discounts:

• OpenAI Batch API: 50% off, 24-hour completion
• Anthropic Message Batches: 50% off, similar timing

3. Token budgets per request

Set hard limits on max_tokens and implement cost tracking per user/tenant:

const MONTHLY_BUDGET_PER_TENANT = 10.00; // $10/month

async function checkBudget(tenantId: string, estimatedCost: number) {
  const usage = await getMonthlyUsage(tenantId);
  if (usage + estimatedCost > MONTHLY_BUDGET_PER_TENANT) {
    throw new BudgetExceededError();
  }
}

4. Response caching

Cache LLM responses for identical queries. Even with embeddings-based similarity, you can cache 20-40% of requests in typical RAG applications.

Our current recommendations

For most B2B SaaS platforms:

1. Primary model: Claude Opus 4.6 for quality
2. Cost tier: Haiku 4.5 ($1/$5) or Gemini 3 Flash ($0.50/$3) for simple tasks
3. Fallback: GPT-5 if Claude is down

For consumer applications (cost-sensitive):

1. Primary: Gemini 3 Flash ($0.50/$3) for everything possible
2. Premium tier: GPT-5 for complex requests
3. Consider: Self-hosted Llama 4 Scout at scale

For enterprise (quality-first):

1. Primary: Claude Opus 4.6 with extended thinking
2. Long context: Llama 4 Scout (10M) or Gemini 3 Pro (1M)
3. Reasoning: o3 for multi-step planning tasks

For privacy-critical / on-premises:

1. Primary: Llama 4 Maverick (400B params, 1M context)
2. Fast tier: Llama 4 Scout (runs on single H100)
3. Consider: DeepSeek R1 for reasoning workloads

Getting started

Model selection is just one part of AI platform architecture. For help designing your LLM integration strategy:

Related resources

• Production AI Platform Stack - Full architecture guide
• RAG Architecture Guide - Retrieval implementation details
• LLM Security Guide - Prompt injection and guardrails