GPT-4 is incredibly capable but expensive and you have no control. Llama is free but requires infrastructure and may not match quality. Fine-tuned models are specialized but need data and expertise.
There is no universally correct answer. The right choice depends on your specific constraints. This framework helps you make that decision systematically.
| Capability Level | Examples | Model Options |
|---|
| Basic | Classification, extraction | Open source, small models |
| Intermediate | Summarization, Q&A | Mid-tier proprietary, large open source |
| Advanced | Complex reasoning, coding | Top-tier proprietary |
| Specialized | Domain-specific tasks | Fine-tuned models |
| Cost Tolerance | Monthly Budget | Recommended Approach |
|---|
| Very low | Under $500 | Open source, self-hosted |
| Low | $500-5,000 | Open source + proprietary hybrid |
| Medium | $5,000-50,000 | Proprietary with optimization |
| High | $50,000+ | Best model for the job |
| Control Need | Examples | Implication |
|---|
| Data privacy | Healthcare, finance | Self-hosted or on-prem |
| Customization | Domain vocabulary, behavior | Fine-tuning capability |
| Availability | Mission-critical | Multiple providers, self-hosted backup |
| Auditability | Regulated industries | Full logging, model versioning |
| Model | Strengths | Weaknesses | Cost |
|---|
| GPT-4 | Best reasoning, coding | Expensive, no control | $$$$ |
| GPT-3.5 | Fast, cheap, good enough | Less capable | $$ |
| Claude | Long context, safety | Availability | $$$ |
| Gemini | Multimodal, Google integration | Variable quality | $$$ |
| Model | Strengths | Weaknesses | Infra Cost |
|---|
| Llama 3 70B | Near-GPT-4 quality | Large, slow | High |
| Llama 3 8B | Fast, efficient | Less capable | Medium |
| Mistral | Good balance | Smaller ecosystem | Medium |
| Mixtral | MoE efficiency | Complex deployment | High |
| Phi-3 | Tiny, fast | Limited capability | Low |
| Task | GPT-4 | Llama 70B | Llama 8B | GPT-3.5 |
|---|
| Complex reasoning | 95% | 88% | 72% | 78% |
| Code generation | 92% | 85% | 68% | 75% |
| Summarization | 90% | 87% | 80% | 82% |
| Classification | 88% | 86% | 82% | 84% |
| Simple Q&A | 92% | 90% | 85% | 88% |
| Model | Input (per 1M tokens) | Output (per 1M tokens) |
|---|
| GPT-4 | $30 | $60 |
| GPT-3.5 | $0.50 | $1.50 |
| Claude Opus | $15 | $75 |
| Claude Sonnet | $3 | $15 |
| Model | GPU Required | Monthly Infra Cost | Effective per 1M tokens |
|---|
| Llama 70B | A100 80GB | $2,500 | $0.50-2.00 |
| Llama 8B | A10G | $500 | $0.10-0.30 |
| Mistral 7B | A10G | $500 | $0.10-0.25 |
| Phi-3 | T4 | $200 | $0.05-0.15 |
| Monthly Tokens | GPT-4 Cost | Self-Hosted Llama 70B | Winner |
|---|
| 1M | $90 | $2,500 | Proprietary |
| 10M | $900 | $2,500 | Proprietary |
| 50M | $4,500 | $2,500 | Open source |
| 100M | $9,000 | $2,500 | Open source |
| Question | If Yes | If No |
|---|
| Need state-of-the-art reasoning? | Proprietary (GPT-4, Claude) | Continue |
| Need specific domain expertise? | Consider fine-tuning | Continue |
| Simple classification/extraction? | Small open source | Continue |
| General purpose assistant? | Mid-tier options | Assess further |
| Question | If Yes | If No |
|---|
| Data cannot leave your infrastructure? | Self-hosted only | Continue |
| Need to customize model behavior? | Fine-tuning or open source | Continue |
| Regulated industry with audit requirements? | Self-hosted preferred | Continue |
| Need 99.99% availability? | Multi-provider + self-hosted backup | Continue |
| Question | If Yes | If No |
|---|
| Less than 10M tokens/month? | Proprietary often cheaper | Continue |
| Have ML ops expertise? | Self-hosted viable | Managed services |
| Variable usage patterns? | Pay-per-use proprietary | Fixed infra cost |
| Tight margins requiring optimization? | Open source + routing | Proprietary acceptable |
| Capability | Control | Budget | Recommendation |
|---|
| High | Low | Any | GPT-4 / Claude |
| High | High | High | Self-hosted Llama 70B |
| Medium | Low | Low | GPT-3.5 + routing |
| Medium | High | Medium | Self-hosted Llama 8B |
| Low | Any | Low | Small open source |
| Specialized | High | Medium | Fine-tuned open source |
Use different models for different tasks:
| Task Complexity | Model | Cost Impact |
|---|
| Simple queries (60%) | Llama 8B / GPT-3.5 | Low |
| Medium queries (30%) | Llama 70B / Claude Sonnet | Medium |
| Complex queries (10%) | GPT-4 | High |
Start with cheaper model, escalate if needed:
| Step | Model | Escalation Trigger |
|---|
| 1 | Llama 8B | Low confidence |
| 2 | Llama 70B | Still uncertain |
| 3 | GPT-4 | Final fallback |
| Approach | Quality | Cost Savings |
|---|
| GPT-4 only | 95% | Baseline |
| Routing | 93% | 60% savings |
| Cascading | 94% | 50% savings |
| Combined | 94% | 65% savings |
| Scenario | Fine-Tune? | Why |
|---|
| Domain terminology | Yes | Better understanding |
| Specific output format | Maybe | Prompting often works |
| Behavior modification | Yes | Consistent style |
| Factual knowledge | No | Use RAG instead |
| General improvement | No | Expensive, risky |
| Component | Cost Range |
|---|
| Data preparation | $5K-50K (human effort) |
| Training compute | $100-10K (depending on model) |
| Evaluation | $1K-5K |
| Ongoing maintenance | 20-30% annually |
| Metric | Base Model | Fine-Tuned | Improvement |
|---|
| Task accuracy | 78% | 92% | +18% |
| Format compliance | 65% | 95% | +46% |
| Inference cost | Baseline | Often lower | 20-40% savings |
| Component | Requirement |
|---|
| GPU | A100/H100 for large models, A10G for small |
| Memory | 80GB+ VRAM for 70B models |
| Storage | Fast NVMe for model weights |
| Networking | Low latency for real-time inference |
| Expertise | MLOps, GPU optimization |
| Service | Models Available | Pricing Model |
|---|
| AWS Bedrock | Llama, Claude, Titan | Per-token |
| Azure OpenAI | GPT-4, GPT-3.5 | Per-token |
| Replicate | Many open source | Per-second |
| Together AI | Open source focus | Per-token |
| Groq | Fast inference | Per-token |
- 1No universal answer - The right model depends on your specific capability, control, and cost needs.
- 2Open source has caught up - Llama 70B approaches GPT-4 for many tasks at a fraction of the cost.
- 3Hybrid approaches win - Routing and cascading give you the best of both worlds.
- 4Break-even matters - Below 30-50M tokens/month, proprietary is often cheaper than self-hosting.
- 5Fine-tuning is narrow - Great for specific behaviors, not general improvement.
- 6Control has a cost - Self-hosting requires infrastructure and expertise investment.
The model landscape changes rapidly. Re-evaluate your decisions quarterly as new models emerge and costs shift.
