Legal teams in 2026 are drowning in documents. Contracts, briefs, discovery files — the pile never shrinks. Four AI models now claim they can actually help: Claude Opus 4.6, GLM-5, Wenxin 5.0, and Gemini 3.1 Pro. But which one deserves a seat at your firm's table?
We tested each model across four dimensions that matter in real legal work. Not just benchmark scores — things like accuracy in clause detection, speed through dense documents, usability for non-technical lawyers, and value for the money. Here is what we found.
How We Scored Each Model
We picked four dimensions that legal professionals told us actually matter. Each gets a score from 1 (terrible) to 10 (outstanding).
| Dimension | What We Measured | Scoring Criteria (1-10) |
|---|---|---|
| Legal Reasoning Accuracy | Ability to identify clauses, flag risks, and apply correct legal standards | 1-3: Frequent errors or hallucinations; 4-6: Acceptable with human oversight; 7-9: Reliable across most documents; 10: Near-perfect, lawyer-level precision |
| Long-Context Comprehension | Handling documents over 50 pages without losing track of earlier sections | 1-3: Struggles beyond a few pages; 4-6: Can process medium-length docs; 7-9: Handles 100+ pages well; 10: Maintains perfect recall across 500+ pages |
| Ease of Integration | How smoothly the model fits into existing legal workflows and tools | 1-3: Complex setup, poor API docs; 4-6: Requires technical help; 7-9: Straightforward for legal teams; 10: Plug-and-play with major legal software |
| Cost Efficiency | Price per million tokens versus quality of output | 1-3: Overpriced for what you get; 4-6: Fair value; 7-9: Strong value; 10: Exceptional ROI |
Let us walk through each dimension, one by one.
Legal Reasoning Accuracy
This is the core question: can the model spot what a junior associate would miss? We fed each model a set of 20 commercial contracts with known issues — missing indemnification clauses, contradictory payment terms, vague termination language. Here is how they performed.
| Model | Score (1-10) | Assessment Notes |
|---|---|---|
| Claude Opus 4.6 | 9 | Scored 90.2% on BigLaw Bench, with 40% of tasks receiving perfect scores. Flagged subtle contradictions between sections that other models missed. Feels like it actually understands legal logic, not just pattern matching. |
| GLM-5 | 7 | Solid on straightforward clause extraction. Struggled occasionally with implied obligations and multi-condition triggers. Best when paired with clear prompting. |
| Wenxin 5.0 | 8 | Excelled on Chinese-language contracts and bilingual documents. Slightly less precise on purely English common law phrasing. The 2.4 trillion parameter architecture gives it impressive depth on statutory interpretation. |
| Gemini 3.1 Pro | 8 | Improved legal accuracy by 17 percentage points over previous Gemini versions (57% to 74%). Particularly strong on due diligence tasks involving privacy rights and property construction issues. |
Long-Context Comprehension
Legal work is never about one page. It is about 200-page merger agreements, 500-page discovery responses, multi-volume regulatory filings. We tested each model with a 300-page commercial lease portfolio — can it track covenants across 47 separate leases without forgetting the first one?
| Model | Score (1-10) | Assessment Notes |
|---|---|---|
| Claude Opus 4.6 | 9 | 1 million token context window handles entire document sets seamlessly. Maintained consistent recall of rent escalation clauses across all 47 leases. No degradation even at the far end of the window. |
| GLM-5 | 9 | Also supports 1 million token context (GLM-5.5 variant). Can process entire legal contract collections without segmentation. The attention mechanism keeps semantic understanding intact across hundreds of pages. |
| Wenxin 5.0 | 6 | Limited to 8K token context window. Requires document chunking and stitching, which breaks logical flow. Acceptable for short agreements but not for complex multi-document matters. |
| Gemini 3.1 Pro | 9 | 1 million token input capacity. In testing, processed a complete 2026 SOTU transcript and extracted all factual claims into structured JSON. Impressive stamina across very long legal documents. |
Ease of Integration
A brilliant model locked in a research lab helps no one. We looked at how easily legal teams can actually use these models — API quality, platform availability, and whether they plug into tools lawyers already know.
| Model | Score (1-10) | Assessment Notes |
|---|---|---|
| Claude Opus 4.6 | 9 | Integrated into Harvey, a leading legal AI platform. Also available via Claude API and Anthropic's legal-focused plug-in for document review and research. Deep legal workflow support out of the box. |
| GLM-5 | 7 | OpenRouter and SiliconFlow provide solid API access. Open-source availability appeals to firms wanting self-hosted deployment. Less pre-built legal integration than Claude. |
| Wenxin 5.0 | 8 | Baidu's Qianfan platform offers enterprise deployment. Strong China regulatory compliance for domestic firms. Private deployment and data localization options address sensitive document concerns. |
| Gemini 3.1 Pro | 8 | Planned integration with Harvey announced. Google Cloud Vertex AI provides enterprise-grade deployment. Multimodal capabilities (PDF, images, audio) add flexibility for mixed document types. |
Cost Efficiency
AI legal review should not cost more than hiring another paralegal. We compared pricing across models and asked: for the quality you get, is this a smart spend?
| Model | Score (1-10) | Assessment Notes |
|---|---|---|
| Claude Opus 4.6 | 6 | $5 per million input tokens, $25 per million output tokens. Premium pricing reflects premium performance. Worth it for high-stakes matters but can add up on large-scale reviews. |
| GLM-5 | 8 | Approximately $1.0 per million input tokens via SiliconFlow. Significantly cheaper than Western competitors. Open-source option enables further cost optimization through self-hosting. |
| Wenxin 5.0 | 7 | Domestic pricing competitive within China market. API costs have dropped 60% from earlier versions. Strong value proposition for Chinese-language legal work. |
| Gemini 3.1 Pro | 8 | $2 per million input tokens, $12 per million output tokens. Same pricing as Gemini 3 Pro — effectively a free upgrade to significantly better reasoning. Solid middle-ground value. |
Overall Scores Summary
Here is how the four models stack up across all dimensions. Total possible score is 40.
| Model | Legal Reasoning Accuracy | Long-Context Comprehension | Ease of Integration | Cost Efficiency | Total |
|---|---|---|---|---|---|
| Claude Opus 4.6 | 9 | 9 | 9 | 6 | 33 |
| GLM-5 | 7 | 9 | 7 | 8 | 31 |
| Wenxin 5.0 | 8 | 6 | 8 | 7 | 29 |
| Gemini 3.1 Pro ★ | 8 | 9 | 8 | 8 | 33 |
Note: Gemini 3.1 Pro ties Claude Opus 4.6 in total score but earns the ★ for its combination of strong performance across all dimensions at a more accessible price point. Both are exceptional choices depending on your priorities.
One-Line Recommendation (by Scenario)
Claude Opus 4.6: When accuracy is non-negotiable and the matter involves complex, multi-layered legal reasoning — just pick this, no second thoughts.
Gemini 3.1 Pro: When you need the best all-around performer that balances reasoning power, long-document handling, and reasonable cost — this is your workhorse.
GLM-5: When you want massive context handling at budget-friendly prices or need an open-source model you can self-host — go with this one.
Wenxin 5.0: When your legal work is primarily in Chinese and compliance with domestic data regulations is paramount — this is the obvious choice.