AI Model Comparison

What is AI model comparison?

AI model comparison is the structured side-by-side evaluation of frontier and open-weight LLMs across the dimensions that actually move a production decision: cost per million tokens, context window, latency tier, multimodal support, and reasoning grade. Vendor marketing pages do not compare. They claim. A real comparison sits one provider next to another and forces every model to answer the same five questions in the same units. Without that discipline, the choice usually defaults to whichever model the team used last quarter or whichever name landed on a recent earnings call.

Take a recent example we walked a customer-support team through. The brief: a chatbot needing 200K-token conversations to keep multi-turn ticket history in context, an output budget under $1,000 per month, and 50,000 daily messages averaging 600 input plus 200 output tokens. Starting from the 44-model catalog, the 200K-context filter removes every 128K and 64K model in one pass. The reasoning floor of Strong rules out Basic-tier nano models. The budget cap, applied to that traffic profile, eliminates flagship premium rows like GPT-5.5 Pro at $30/$180 and o3-pro at $20/$80. Four models survive: Claude Haiku 4.5, GPT-5 Mini, Gemini 2.5 Flash, and DeepSeek V4 Flash. The conversation moves from forty-plus options to a four-row decision in under a minute.

Our 5-factor rubric

We compare every model on five factors, in this order. Cost has three axes, not two: input price per 1M tokens, output price per 1M tokens, and cached input price per 1M tokens. Output is 3 to 8x more expensive than input on every provider we track, so output volume drives the bill on most workloads. Cached input runs at roughly 10% of standard input across OpenAI, Anthropic, and Google, which collapses spend on any stable system prompt. Context is two numbers as well: max input tokens and max output tokens. The caveat we hammer on is that effective recall drops past roughly 100K tokens on most models (the lost-in-the-middle effect), so a 1M-token window is not the same thing as 1M tokens of usable retrieval.

Speed lands in five tiers: Slow, Moderate, Fast, Very Fast, Ultra Fast. Real latency varies with prompt length, output length, and provider load, so the tier label is a starting point, not a substitute for benchmarking against representative inputs. Vision is a boolean and a hard filter for any multimodal product. Reasoning grades into five buckets: Frontier, Excellent, Strong, Good, Basic. Frontier covers GPT-5.5 Pro, GPT-5.2 Pro, o3-pro, and Claude Opus 4.7. Basic covers GPT-5 Nano and Gemini 2.0 Flash-Lite, fine for classification, wrong for synthesis. The buckets are deliberately coarse because the difference between Frontier and Excellent shows up in evals, while the difference between Excellent and Strong often does not.

Worked example: long-context summarization across 20K-token PDFs with multi-document reasoning. The 20K input rules out the small-context Mistral and DeepSeek 64K rows immediately. Reasoning needs Excellent or Frontier because the work is synthesis, not extraction. Two finalists remain: Claude Opus 4.7 (1M context, $5 input / $25 output, Frontier reasoning) and Gemini 2.5 Pro (1.05M context, $1.25 / $10, Excellent reasoning). The pick comes down to price against quality margin. We default Opus 4.7 when the synthesis quality shows up in the deliverable, Gemini 2.5 Pro when volume dominates and Excellent suffices.

Common pitfalls

Most model picks go wrong on the same three or four mistakes, and the cost is rarely the headline cost. Picking a flagship for a job a budget tier could do at 1% of the price is the usual one. Confusing window size with retrieval quality is a close second. Trusting vendor benchmarks that were tuned for the vendor finishes the podium. We have walked teams off all three.

• Defaulting to flagship when a budget tier would do the job. GPT-5 Nano at $0.05 input and $0.40 output handles short-form classification, sentiment, and tag extraction at roughly 1/100th the cost of GPT-5.5 ($5 / $30). On 10M monthly classification calls at 200 input plus 50 output tokens, Nano runs about $4 per month against $400 for GPT-5.5. Same job, same accuracy on the bench, two orders of magnitude on the invoice.
• Conflating context window with effective recall. A 1M-token model often loses precision when the answer sits at positions 200K through 800K of the input, where the lost-in-the-middle effect bites hardest. For retrieval-heavy work, a 128K-context model plus a real RAG retriever beats stuffing 1M tokens of corpus into Gemini 3.1 Pro on both accuracy and cost. We use the giant context only when the task genuinely needs cross-document reasoning the retriever cannot stage.
• Benchmark cherry-picking. Provider benchmarks flatter the provider, sometimes by training on adjacent eval distributions, sometimes by selecting the configuration that wins. The benchmarks that matter are the ones run on the actual production prompts. Every team we work with builds a 50-to-200 sample eval set drawn from real traffic and runs the shortlist against it before signing a contract.
• Picking a model without considering deprecation risk. Fast-moving providers retire models on 6 to 12 month horizons, and a wrapper service shutting down (PromptPerfect users learning this lesson now after the September 2026 sunset announcement) can strand an entire integration overnight. We weight providers with stable deprecation policies and direct API access higher than middleware that might disappear.

When to use this tool

We built the comparison for three concrete decisions that keep coming up. The first is greenfield model selection on a new product. Before any API call gets written, the team needs to know which models meet the cost, context, latency, and reasoning thresholds the spec demands. Sortable rows across 44 models, filterable by tier and provider, turn that decision into a 10-minute conversation instead of a week of vendor calls.

The second is migration off a deprecated or shutting-down dependency. Users moving off PromptPerfect after the September 2026 shutdown need a same-or-better replacement on roughly the same price-per-call envelope. Filtering by reasoning tier and price simultaneously surfaces the candidates in a single view rather than a manual provider-by-provider tour. The third is the model-justification conversation with finance or stakeholders, where a vague preference for “the smart model” needs to become a defensible decision with concrete numbers attached. A side-by-side that shows GPT-5.5 at $5 / $30, Claude Opus 4.7 at $5 / $25, and Gemini 2.5 Pro at $1.25 / $10 against the actual workload size turns a values argument into a spreadsheet.

Frequently asked

How do I pick the right model for my use case?

We work down a 5-factor rubric: cost, context, speed, vision, reasoning. First rule out tiers by budget. A $500/month chatbot with 200K-token conversations rules out GPT-5.5 Pro ($30/$180) and o3-pro instantly. Then filter by context if you need long documents, then pick on reasoning level. For analytical or coding work we default to Claude Opus 4.7 or Sonnet 4.6; for high-volume classification we default to GPT-5 Nano or Gemini 2.0 Flash-Lite.

Is more context window always better?

No. Larger context costs proportionally more on every call, and effective recall drops past roughly 100,000 tokens on most models (the lost-in-the-middle effect). For retrieval-heavy work we run RAG against a smaller-context model rather than stuffing 1M tokens of documents into Claude Opus 4.7 or Gemini 3.1 Pro. We use the giant context only when the task genuinely needs cross-document reasoning the retriever cannot do.

Which model is fastest?

As of April 2026, Gemini 2.0 Flash-Lite, GPT-5 Nano, GPT-4.1 Nano, Gemini 2.0 Flash, and Grok 4.1 Fast share the Ultra Fast tier. For reasoning-grade speed, o4-mini and Claude Haiku 4.5 lead the Very Fast tier with strong-to-good reasoning. Real latency varies with prompt length, output length, and provider load, so we benchmark against representative inputs before committing for latency-critical UX.

Do all models support vision?

Most flagship and mid-tier models in 2026 do. Every Claude 4 model, the entire GPT-5 family, Gemini 2.x and 3.x, Grok 4 and 4.20, Llama 4 Maverick, and Mistral Large 3 accept images. Notable text-only exceptions: every DeepSeek model, Mistral Medium 3, Mistral Small 3.1 and Small 4, Grok 4.1 Fast, and Llama 3.3 70B. Filter by `vision: true` when shipping multimodal.

How current is this comparison data?

We verify pricing and capability fields monthly against each provider's pricing page and OpenRouter API, then bump the LAST_VERIFIED stamp shown on each tool. The current verification is April 2026. Updates ship via npm run sync:models -- --apply, which pulls live OpenRouter prices and flags drift. Qualitative fields (reasoning tier, speed tier, vision flag) are reviewed by hand on the same cadence.

Is GPT-5.5 better than Claude Opus 4.7?

Both sit at the top of our rubric in April 2026. GPT-5.5 ($5 input / $30 output, 1.05M context, Excellent reasoning) is faster and cheaper on output. Claude Opus 4.7 ($5 / $25, 1M context, Frontier reasoning) wins on output price and reasoning tier. We pick Opus 4.7 for code, analysis, and long-context synthesis. We pick GPT-5.5 for high-volume general workloads where speed and output cost dominate.