AI Comparison 2026: Models, Benchmarks and API Prices

GPT-6 Astra is the highest-ranked available model in this reviewed September 4, 2026 snapshot. LLM Stats ranks it at 60.72. Use the ranking as a shortlist, then test your own tasks.

An AI comparison should answer three questions: how well a model handles your task, what it costs, and whether you can use it. This page separates those decisions. The tables use a dated LLM Stats snapshot. Context limits link to their own sources.

The ranking date appears beside the data. It can differ from this article's editing date. These are model API comparisons, not monthly subscriptions to ChatGPT, Claude or Gemini.

AI Models Compared at a Glance

Reviewed data snapshot

Ranking snapshot: September 4, 2026

Highest available overall model

GPT-6 Astra

OpenAI

60.72LLM Stats

Reasoning58.66

GPT-6 Astra

Coding49.03

GPT-6 Astra

Agents46.37

GPT-6 Astra

LLM StatsOverall ranking and pricesModel specificationsSourced context windows

Benchmark evidence determines the scores. Test shortlisted models on your own tasks.

Context windows use tokens, not words. Each link opens its source. — means the model is unavailable or the value is unverified.

Context windows: sources and scope

Linked values come from separate specifications. They do not change the ranking or its date. The usable limit can depend on the API provider.

• GPT-6 Astra: OpenAI API. OpenAI · checked on September 7, 2026.
• Claude Fable 5.1: Claude API. Anthropic · checked on September 7, 2026.
• Claude Opus 5: Claude API. Anthropic · checked on September 7, 2026.
• GPT-5.6 Sol: OpenAI API. OpenAI · checked on September 7, 2026.
• Muse Spark 1.3: Meta endpoint on OpenRouter. OpenRouter · Meta · checked on September 7, 2026. This limit applies to this provider endpoint. Other providers can have different limits.
• Claude Fable 5: Claude API. Anthropic · checked on September 7, 2026.
• Kimi K3: Published model weights. Moonshot AI · checked on September 7, 2026.
• GLM-5.3: Z.ai API. Z.ai · checked on September 7, 2026. Z.ai specifies 1M tokens. Different provider limits do not replace the native model limit.
• DeepSeek-V4-Pro-0813: DeepSeek endpoint on OpenRouter. OpenRouter · DeepSeek · checked on September 7, 2026. This limit applies to this provider endpoint. Other providers can have different limits.

Unavailable models remain visible to preserve the source ranking. They are excluded from practical recommendations. Missing context limits or prices remain blank instead of becoming estimates.

A context window describes a limit for a specific model or endpoint. It does not prove that the model retrieves every detail accurately. Follow each linked specification before designing a workflow around that limit.

AI Benchmark Comparison: Overall Ranking

The source combines benchmark evidence into a ranking. Its score is not a percentage of tasks your team will complete successfully. Small differences also need care: models can have different benchmark coverage and measurement uncertainty.

Use the chart to select candidates. Then compare them on the same documents, prompts, languages and tool permissions. Our AI coding comparison covers a narrower programming use case.

Which Model Leads in Reasoning, Coding and Agent Tasks?

The category leaders can differ from the overall leader. Reasoning tests, coding tasks and agent evaluations measure different work. An agent benchmark includes a particular environment and tool setup; it does not validate your own integrations.

For research work, record whether the model supports its answer with accessible sources. For coding, check whether the change passes your tests. For customer service, check the answer against your approved policy.

Our AI research guide explains that separate use case. Keep your evaluation criteria specific enough that two reviewers can reach the same result.

API Price Comparison: What Does Each Model Cost?

These are recorded prices from active providers in the snapshot. The table covers the overall top ten, so its cheapest option is not necessarily the cheapest model available.

The blended price assumes eight input tokens for every output token. For your own workload, calculate input and output charges separately. Add tool charges and retries when they apply.

Worked example, using hypothetical rates: 20,000 input tokens at $2 per million cost $0.04. Another 2,000 output tokens at $10 per million cost $0.02. The model charge is $0.06 before tools, retries or other fees. These example rates are not a provider quote.

Check the selected provider's current pricing before purchase. Cached input, long-context tiers, batch processing and serving endpoints can change the bill. A low token price is useful only when the model completes the task to your required standard.

Which AI Model Fits Your Use Case?

Interactive selection

Which AI model fits your task?

Choose a priority. The finder shows a starting point from the reviewed snapshot.

GeneralReasoningCodingAgentsOpen WeightLow token cost

Starting point from the data

GPT-6 Astra

openai

Recommendation: GPT-6 Astra. LLM Stats score: 60.72.

This model leads the available models in the overall ranking.

LLM Stats score

60.72

Review model data

This result is a shortlist. Test quality, cost, and privacy with your own cases.

The finder applies the displayed ranking and price data. It does not test your prompts or guarantee a result. Treat its suggestion as the first candidate in a small evaluation.

General Work and Complex Reasoning

Start with the available overall and reasoning leaders. Compare correctness, instruction following and missing-information handling. Include questions that the model should decline to answer without more evidence.

Coding and Tool Use

Use the coding category to build a shortlist. Test a real bug fix, a small feature and an unfamiliar repository task. Give every candidate the same tools and time allowance. Record test results and review effort alongside model cost.

Open-Weight Deployment

Open weights can support a different deployment strategy. They do not remove license conditions, infrastructure costs or operational work. Check the exact publisher model card and license before deployment. See our open-source and open-weight model comparison for the wider decision.

High-Volume Business Workflows

Begin with a narrow task such as classifying a support request or drafting an answer from an approved FAQ. Compare a lower-cost candidate with a stronger model. Keep the cheaper option only if it meets the same acceptance criteria.

A Practical Evaluation Sheet for Your Team

Create a fixed set of representative tasks before testing. Include ordinary requests, missing information and cases that require a person. Twenty cases can help you find early defects; they do not establish production reliability.

Record the exact model ID, reasoning setting, provider and test date. A different reasoning mode is a different test configuration. Rerun the same cases after a material model or workflow change.

From Model Choice to a Working Agent

A model supplies one part of an agent. The workflow also needs instructions, approved information, connected tools and a clear stopping rule. PUNKU.AI lets you describe the workflow you want to build in plain language.

For a first pilot, use this brief: “Draft answers to these support questions using only our supplied FAQ. Flag missing information. Do not send replies or change customer records.” Connect the required information and review the resulting behavior before expanding the task.

Follow the agent creation guide for setup, or plan a customer-service pilot. When your task is clear, start with PUNKU.AI. The signup step starts product setup; it does not deploy a tested workflow automatically.