Best AI model to analyze documents and contracts
Reading contracts, PDFs and long documents, finding clauses and summarizing points of attention. Reasoning over dense text and a large context window matter here. It does not replace a lawyer's review.
Suggested models
The catalog sources have no contract reading score. GPQA Diamond is used as a reference for reasoning over technical text, and the filter requires a long context.
1 Gemini 4 Argon Google
Why it fits: Scores 96.4% on GPQA Diamond, technical questions a quick search cannot solve, and reads up to 1,048,576 tokens at once, enough for a whole long document.
- GPQA Diamond: 96.4%, per Epoch AI, latest version we checked?what it is
- Price: US$ 2.00 input and US$ 10.00 output per 1M tokens promotional price (source: Google, official page; promotional price currently shown on the page; Google has said this price will go up)?
- Estimated cost (see the How we calculate block): about US$ 0.053 (estimate)
- Weights: Closed?
- Alternative: Kimi K3.1 Pro, with open weights, 94.2% on GPQA Diamond
2 GPT-6 Astra OpenAI
Why it fits: Scores 95.8% on GPQA Diamond, technical questions a quick search cannot solve, and reads up to 1,050,000 tokens at once, enough for a whole long document.
- GPQA Diamond: 95.8%, per Epoch AI, latest version we checked?
- Price: US$ 10.00 input and US$ 50.00 output per 1M tokens (source: OpenAI, official page)?
- Estimated cost (see the How we calculate block): about US$ 0.27 (estimate)
- Weights: Closed?
- Alternative: Kimi K3.1 Pro, with open weights, 94.2% on GPQA Diamond
3 Claude Sonnet 5.5 Anthropic
Why it fits: Scores 95.6% on GPQA Diamond, technical questions a quick search cannot solve, and reads up to 1,000,000 tokens at once, enough for a whole long document.
- GPQA Diamond: 95.6%, per Epoch AI, latest version we checked?
- Price: US$ 2.00 input and US$ 10.00 output per 1M tokens (source: Anthropic, official page)?
- Estimated cost (see the How we calculate block): about US$ 0.053 (estimate)
- Weights: Closed?
- Alternative: Kimi K3.1 Pro, with open weights, 94.2% on GPQA Diamond
4 GPT-6.1 Sol OpenAI
Why it fits: Scores 95.4% on GPQA Diamond, technical questions a quick search cannot solve, and reads up to 1,050,000 tokens at once, enough for a whole long document.
- GPQA Diamond: 95.4%, per Epoch AI, latest version we checked?
- Price: US$ 2.00 input and US$ 10.00 output per 1M tokens (source: OpenAI, official page)?
- Estimated cost (see the How we calculate block): about US$ 0.053 (estimate)
- Weights: Closed?
- Alternative: Kimi K3.1 Pro, with open weights, 94.2% on GPQA Diamond
Order: highest GPQA Diamond score among analyze documents and contracts models, one version per model line, with a context of at least 200,000 tokens.
How we calculate
Change the numbers and the cost of every model on the page is recalculated right away. Without JavaScript, the default values below apply.
Input = pages × words per page ÷ 0.75. Output = analysis words ÷ 0.75.
With these numbers: about 20,000 input tokens and 1,333 output tokens (estimate).
Conversion used: 1 token is about 4 characters or 0.75 words, OpenAI's rough rule for English text. Other languages can differ. Conversion source: OpenAI, Understanding and counting tokens
Fields marked as assumption are our own guesses, without a source. Adjust them to your case.
Cost of each model = input tokens × input price + output tokens × output price, with prices per 1M tokens. Models that reason before answering can use many more output tokens. It is a reference calculation and does not change the order of the list.
Other paid models we checked
Reference paid models from the main labs. They are left out of the list above because of a lower score, no score on this metric, or not passing the case filter. Prices come from the company's official page when it publishes them.
| Model | Score on this metric | Price and source | Estimated cost |
|---|---|---|---|
| GPT-6 Luna OpenAI | 90.5% | US$ 0.10 input and US$ 0.50 output per 1M tokens (source: OpenAI, official page) | about US$ 0.0027 (estimate) |
| Claude Fable 5.2 Anthropic | 93.9% | US$ 8.00 input and US$ 40.00 output per 1M tokens (source: Anthropic, official page) | about US$ 0.21 (estimate) |
| Claude Opus 5.5 Anthropic | 90.6% | US$ 4.00 input and US$ 20.00 output per 1M tokens (source: Anthropic, official page) | about US$ 0.11 (estimate) |
| Claude Haiku 5.5 Anthropic | 89.6% | US$ 0.10 input and US$ 0.50 output per 1M tokens (source: Anthropic, official page; up to 100,000 prompt tokens) | about US$ 0.0027 (estimate) |
| Gemini 3.8 Flash Google | 95.4% | US$ 0.75 input and US$ 3.75 output per 1M tokens promotional price (source: Google, official page; promotional price currently shown on the page; Google has said this price will go up) | about US$ 0.02 (estimate) |
| Grok 4.7 xAI | 92.7% | US$ 2.00 input and US$ 6.00 output per 1M tokens (source: xAI, official page) | about US$ 0.048 (estimate) |
| Mistral Large 4 Mistral AI | 90.2% | US$ 0.50 input and US$ 1.50 output per 1M tokens (source: Mistral AI, official page; price the official page gives for Mistral Large, without naming the version) | about US$ 0.012 (estimate) |
| Mistral Medium 3.5 Mistral AI | no score on this metric | US$ 1.50 input and US$ 7.50 output per 1M tokens (source: OpenRouter; the price could not be read from the official page, because the table is built automatically; OpenRouter is kept as the reference) | about US$ 0.04 (estimate) |
| Command A Cohere | no score on this metric | US$ 2.50 input and US$ 10.00 output per 1M tokens (source: OpenRouter; Cohere's official page does not publish this model's price; OpenRouter is kept as reference) | about US$ 0.063 (estimate) |
| Command A+ Cohere | no score on this metric | US$ 0.30 input and US$ 1.50 output per 1M tokens (source: OpenRouter; Cohere's official page does not publish this model's price; OpenRouter is kept as reference) | about US$ 0.008 (estimate) |
| Nova Premier 1.0 Amazon | no score on this metric | US$ 2.50 input and US$ 12.50 output per 1M tokens (source: Amazon, official page; us-east-1 region, official AWS price list) | about US$ 0.067 (estimate) |
| Nova 2 Lite Amazon | no score on this metric | US$ 0.33 input and US$ 2.75 output per 1M tokens (source: Amazon, official page; us-east-1 region, official AWS price list) | about US$ 0.01 (estimate) |
FAQ
What is the best AI model to analyze documents and contracts?
By this page's rule, the list starts with Gemini 4 Argon, GPT-6 Astra, Claude Sonnet 5.5, GPT-6.1 Sol. Order: highest GPQA Diamond score among analyze documents and contracts models, one version per model line, with a context of at least 200,000 tokens.
How much does it cost to analyze documents and contracts with AI?
With the default values in the How we calculate block, the estimate ranges from US$ 0.053 to US$ 0.27 among the listed models with a published price. It is only an estimate and you can adjust the numbers on the page.
Is there an open weights option to analyze documents and contracts?
Not among the models on this list. In the catalog sources, no open weights model has a score for this case.
Does this page use AI to recommend?
No. The recommendation comes from fixed rules over public data from Epoch AI, OpenRouter, Hugging Face and each company's official pricing page, in the latest version we checked. The typed text is only compared with keywords.
Terms used on this page
Use cases with their own page
Official pricing pages checked
Sources
- Epoch AI, CC BY 4.0, latest version we checked.
- OpenRouter Benchmarks, CC BY 4.0, latest version we checked.
- OpenRouter, dados públicos da API; exports de benchmarks e rankings em CC BY 4.0, latest version we checked.
- Hugging Face Hub, metadados públicos do Hub; licença de cada modelo no repositório, latest version we checked.
Dates come from the sources: OpenRouter listing or Hugging Face repo creation, which may differ from the official launch. Family and version were grouped by rule from the name.