Key Considerations

Choosing the right large language model (LLM) is one of the most important decisions in AI development. The wrong choice can lead to poor performance, excessive costs, or vendor lock-in. It is also a moving target: model names, context limits, and list prices change several times a year. Treat any comparison — including the table below — as a dated snapshot rather than a permanent ranking, and re-check the provider's pricing page before you commit a budget.

Evaluation Criteria

  • Task Fit: Does the model excel at your specific task (coding, reasoning, creative writing, summarization)? Test on the task, not on a general impression.
  • Context Window: How much text can the model process at once? Long-document workflows need room for both the input and the output.
  • Latency: How fast does the model respond? Interactive products have tighter limits than batch pipelines.
  • Cost: What are the per-token costs for input and output? Output tokens usually cost several times more than input tokens.
  • Privacy: Can you self-host, or must you use a cloud API? Regulated data may rule out some providers entirely.

Popular Options in 2026

  • Claude (Anthropic): Excellent for long documents, nuanced reasoning, and coding.
  • GPT-6 (OpenAI): Strong multimodal capabilities, with a family that spans flagship, mid-tier, and budget models.
  • Gemini (Google): Large context windows, with the flagship model outside the free tier.
  • Qwen (Alibaba): Best-in-class Chinese performance, open-weight options.
  • Llama (Meta): Fully open-source, self-hostable, strong community.

Decision Framework

Start by defining your must-haves: latency under 2 seconds? A fixed monthly budget? Full data sovereignty? Then benchmark 2-3 finalists on your actual data before committing. Requirements come first and model names second: a model that tops a public leaderboard can still fail your latency, privacy, or cost constraints.

A useful order is latency and availability first, budget second, data sovereignty third, and raw capability last. Any candidate that fails a hard constraint is out, no matter how good its demo looks. Only then compare the survivors on a sample of 50-100 real examples, including the messy ones. Measure end-to-end cost per successful task, not cost per token, and include retries and human review.

Cost Comparison Table

The table below groups current models into three price tiers. Read it as a set of tiers, not a ranking. Flagship models can cost 50-100 times more per token than budget models, so the first question is whether your task genuinely needs the top tier.

| Model | Input ($/1M) | Output ($/1M) | Context |

|-------|--------------|---------------|---------|

| GPT-6 Astra | $10.00 | $50.00 | 1.05M |

| GPT-6.1 Sol | $2.00 | $10.00 | 1.05M |

| GPT-6 Luna | $0.10 | $0.50 | 1.05M |

| Claude Fable 5.1 | $10.00 | $50.00 | 1M |

| Claude Opus 5.5 | $4.00 | $20.00 | 1M |

| Claude Sonnet 5.5 | $2.00 | $10.00 | 1M |

| Claude Haiku 5.5 | $0.10 | $0.50 | 1M |

| Gemini 4 Argon | See official pricing | See official pricing | 1M |

Prices verified on 2026-10-15 against each provider's official pricing page; check the provider before committing to a budget.

Self-hosted cost depends on GPU rental rates and utilization, not on a list price.

Monthly Cost Example

Assume a workload of 1M input tokens and 200K output tokens per month. Using the list prices above:

  • GPT-6 Astra: 1M x $10.00 + 0.2M x $50.00 = $10.00 + $10.00 = $20.00 per month
  • Claude Sonnet 5.5: 1M x $2.00 + 0.2M x $10.00 = $2.00 + $2.00 = $4.00 per month
  • GPT-6 Luna: 1M x $0.10 + 0.2M x $0.50 = $0.10 + $0.10 = $0.20 per month

These figures are arithmetic on published unit prices. They say nothing about answer quality, latency, or reliability; those still need to be tested on your workload.

The ratio matters more than the absolute numbers: at these prices the flagship costs five times as much as the mid-tier model for the same token volume, and one hundred times as much as the budget model. Whether that difference is worth paying for depends on the quality you measure on your own data, not on the price alone.

Common Failure Modes

  • Looking only at the unit price: Retries, failed calls, and long prompts multiply token counts. A model that costs half as much but needs two attempts is no longer cheaper.
  • Ignoring context length: A low per-token rate on a very long prompt can still produce an expensive request. Measure tokens per request, not just the price per million.
  • Using a flagship model for classification: Labeling, routing, and extraction are often handled well by a budget model. Reserve the flagship for the hard cases.
  • Ignoring caching and batch discounts: Many providers charge less for repeated prefixes or asynchronous batch jobs. Skip these and your estimate will be too high, which can push you toward the wrong model.

Practical Tips

  • Start with the cheapest model that works: Use a budget model such as GPT-6 Luna or the current Gemini Flash for simple tasks. Upgrade only when quality is insufficient.
  • Benchmark on YOUR data: Leaderboard scores do not reflect your specific use case. Test with 50-100 real examples.
  • Consider total cost of ownership: Self-hosting requires DevOps time. Cloud APIs have zero maintenance but higher per-token costs.
  • Plan for model portability: Abstract your LLM layer (use LiteLLM or LangChain) so you can switch providers without rewriting.
  • Re-check prices before you commit: List prices and model line-ups change. Date your assumptions and revisit them when a new model ships.