AI Model Comparison: Intelligence, Speed & Price

HiseHub Model Value Lab

AI model comparison helps answer which model is actually worth using. Compare intelligence, agentic and coding signals, output speed, task cost, and token pricing in one place, then connect the result to real subscription choices.

Compare models before choosing a plan

A leaderboard is useful when it explains what the number measures. HiseHub separates model capability from consumer subscription access: a high benchmark score does not automatically mean that every ChatGPT, Claude, Gemini, or Grok plan includes that model or the same usage limits.

What the comparison measures

• Intelligence: a composite score from independent Artificial Analysis evaluations. It is useful for a broad shortlist, not a universal winner.
• Agentic and coding: capability signals derived from the published evaluation data when available. A dash means that a metric was not measured for that model.
• Speed and latency: median output tokens per second and related response measurements. Real-world response time also depends on routing, load, prompt length, and task design.
• Cost per task: the source’s estimated cost for its Intelligence Index task mix, which is more informative than token price alone but still not a personal usage quote.
• Input and output price: API token prices per million tokens. The value sort uses a clearly labelled HiseHub 3:1 input/output estimate when both prices are available.
• Release date: helps distinguish current model versions from older variants with similar names.

Use the right model for the job

Use the search box to find a model or creator, then sort the table for the question you actually have. For writing and editing, start with intelligence and instruction-following evidence; for coding, inspect coding and agentic signals; for research, check reliability, long context, and citation behaviour; for images and video, use a media-specific evaluation instead of assuming a language-model score transfers.

Why rankings can disagree

Different evaluations reward different behaviour. A model that is excellent at coding may be less convenient for everyday writing; a fast model may trade depth for latency; and a cheaper API can consume more tokens to complete the same task. Treat the table as a starting point, not as a universal winner. When two models are close, your own representative prompts are often the best tie-breaker.

How HiseHub keeps the data useful

The tool uses the official Artificial Analysis public language-model API, fetches all available pages within the free endpoint, stores the result on the server, and shows the refresh time. The API key is never sent to site visitors. The number of rows and the Intelligence Index version can change as the source adds, retires, or re-evaluates models. Rows without a particular benchmark remain visible so the table does not silently hide the wider model catalogue.

Artificial Analysis requires visible source attribution and its terms govern API use. HiseHub is an independent comparison layer; it does not reproduce the source site’s full benchmark suite or imply endorsement. Read the Artificial Analysis methodology when you need the definitions behind a metric.

For consumer subscription pricing by country, use the AI subscription price comparison. For caps, credits, and reset windows, browse the AI usage limits hub.

Questions readers ask

Does the highest score mean the best model?

No. It means the model performed strongly on the selected evaluation mix. Your workload, preferred interface, context needs, privacy requirements, and budget still matter.

Is API price the same as a ChatGPT or Claude subscription?

No. API pricing is usage-based and usually quoted per million tokens. A consumer subscription may bundle a model with a monthly fee, message caps, file limits, or temporary capacity. Compare the two routes separately before purchasing.

Why does the same model look different across providers?

Hosted endpoints can use different quantization, routing, sampling defaults, context settings, and capacity. Provider-level performance is therefore a separate question from the model name alone.

Use the ranking as a shortlist

The practical workflow is simple: shortlist two or three models, test them on the same representative prompt, check the official plan or API limits, and then choose the cheapest route that meets your quality and reliability needs. Recheck the data after a major model release or pricing change.

HiseHub is independent. Benchmark results and API prices are point-in-time information and may change. Always confirm the current model, plan, price, and usage limits on the provider’s official page.

How to interpret this AI model comparison

This table is a decision aid, not a universal winner list. Intelligence, agentic and coding signals come from the published benchmark snapshot; output speed and API price describe different trade-offs. Compare models within the same task and date before drawing a conclusion.

For a buying decision, first define the work you need to do, then check the model’s access route, usage limits and price. A model can rank highly while being unavailable on the plan or in the region you use. Recheck the linked source after a major model release or pricing change.

New model announced but no independent score yet?

Use the AI Model Release Tracker to compare official release dates, prices, context and rollout status before a matching benchmark row arrives.

Considering a self-hosted model stack?

After comparing model capability and price, use the Self-Hosted AI Tools directory to compare local runtimes, chat interfaces, and workflow builders.