# Best AI model for PDFs — November 2026

> Best AI model for PDFs (November 2026): on AA-LCR, run by Artificial Analysis, Kimi K3.1 Pro and MiMo-V2.6-Pro are within 1.5 points of each other. Evidence, constraints, and caveats included.

## Constraints on this page

A builder choosing by accuracy, hosted processing allowed, any price, ordinary input size. Every refine link below changes one answer.

## The shortlist for PDFs

| # | Model | Creator | AA-LCR | Why |
|---|---|---|---|---|
| 1 | [Kimi K3.1 Pro](/models/kimi-3-1-pro) | Moonshot AI | 86.3% | Highest AA-LCR result, with 1 other model within 1.5 points; $4.00 for the stated workload; 1.05M context; Open weights; check hardware before treating local use as feasible |
| 2 | [MiMo-V2.6-Pro](/models/mimo-v2-6-pro) | Xiaomi | 86.3% | 86.3% on AA-LCR, run by Artificial Analysis; $0.61 for the stated workload; 1M context; Open weights; check hardware before treating local use as feasible |
| 3 | [Claude Opus 5.5](/models/claude-opus-5-5) | Anthropic | 84.7% | 84.7% on AA-LCR, run by Artificial Analysis; $8.00 for the stated workload; 1M context |
| 4 | [DeepSeek V4.1 Flash 1017](/models/deepseek-v4-1-flash) | DeepSeek | 84.0% | 84.0% on AA-LCR, run by Artificial Analysis; $0.54 for the stated workload; 1M context; Open weights; check hardware before treating local use as feasible |
| 5 | [Muse Wonder Full](/models/muse-wonder-full) | Meta | 83.7% | 83.7% on AA-LCR, run by Artificial Analysis; 1M context |

Ordered by AA-LCR result under the stated constraints.

Not ranked. [Kimi K3](/models/kimi-k3) scored 88.7% here. Its lab has replaced it with [Kimi K3.1 Pro](/models/kimi-3-1-pro), which scored 86.3%.

## The top ten for PDFs, with API prices

The first ten models on the same evidence and constraints as the shortlist, with each provider's API price in US dollars per million tokens. GPT-6 Luna costs the least of the ten for a workload of 1M input and 200k output tokens: $0.10 per million input tokens and $0.50 per million output tokens. Two of the ten have no API price on file.

| # | Model | Creator | AA-LCR | Input | Output |
|---|---|---|---|---|---|
| 1 | [Kimi K3.1 Pro](/models/kimi-3-1-pro) | Moonshot AI | 86.3% | $2.00 | $10.00 |
| 2 | [MiMo-V2.6-Pro](/models/mimo-v2-6-pro) | Xiaomi | 86.3% | $0.435 | $0.87 |
| 3 | [Claude Opus 5.5](/models/claude-opus-5-5) | Anthropic | 84.7% | $4.00 | $20.00 |
| 4 | [DeepSeek V4.1 Flash 1017](/models/deepseek-v4-1-flash) | DeepSeek | 84.0% | $0.30 | $1.20 |
| 5 | [Muse Wonder Full](/models/muse-wonder-full) | Meta | 83.7% | No API price on file |  |
| 6 | [Claude Fable 5.2](/models/claude-fable-5-2) | Anthropic | 83.3% | $8.00 | $40.00 |
| 7 | [GPT-5.3 Codex](/models/gpt-5-3-codex) | OpenAI | 83.3% | $1.75 | $14.00 |
| 8 | [GPT-6 Luna](/models/gpt-6-luna) | OpenAI | 83.3% | $0.10 | $0.50 |
| 9 | [Inkling 2 Small](/models/inkling-2-small) | Thinking Machines Lab | 83.3% | $0.30 | $1.00 |
| 10 | [Muse Glimmer 30B](/models/muse-glimmer-30b) | Meta | 83.3% | No API price on file |  |

Prices are list API rates before caching, batch discounts and special tiers. See [API pricing for every model](/llm-pricing).

## ChatGPT, Claude, Gemini, or Copilot?

No app is clearly ahead here. On AA-LCR, ChatGPT’s GPT-6 Luna (83.3%) and Claude Opus 5.5 (84.7%) are within 1.5 points of each other.

- ChatGPT: [GPT-6 Luna](/models/gpt-6-luna), 83.3% (highest of the three). Plan nearest $20: Plus, $20 a month, which lists: GPT-6 Astra, GPT-6.1 Sol, GPT-6.1 Terra, GPT-6 Sol and GPT-6 Luna in Codex; GPT-6.1 Sol is available in Work and Codex, not Chat.
- Claude: [Claude Opus 5.5](/models/claude-opus-5-5), 84.7% (highest of the three). Plan nearest $20: Pro, $20 a month, which lists: Opus, Sonnet and Haiku; Fable is listed under usage credits rather than the included allowance.
- Gemini: [Gemini 3.8 Flash](/models/gemini-3-8-flash), 81.3%. Plan nearest $20: Google AI Pro, $19.99 a month, which lists: Gemini 4 Argon, the Pro model and the Flash model.
- Microsoft Copilot: not scored. Microsoft doesn’t name the model Copilot runs, so no benchmark result can be attached to it. Plan nearest $20: Microsoft 365 Premium, $19.99 a month. [How it compares on plans and data terms](/compare/chatgpt-vs-copilot)

Each entry is the highest-scoring model that company sells, not the model your plan opens by default, so check what the plan lists before you pay. The plan shown is each app’s plan nearest $20, read October 16, 2026 for ChatGPT, November 10, 2026 for Copilot, and November 11, 2026 for Claude and Gemini.

Compare the apps: [ChatGPT vs Claude](/compare/chatgpt-vs-claude), [ChatGPT vs Gemini](/compare/chatgpt-vs-gemini), [Claude vs Gemini](/compare/claude-vs-gemini), [ChatGPT vs Copilot](/compare/chatgpt-vs-copilot).

## What this shortlist rests on

One benchmark: AA-LCR. Questions that can only be answered by reasoning across several long real-world documents, about 100,000 tokens of text per question. A result a model's own provider ran is left out, and a model nobody has run there is not listed.

- [AA-LCR](/benchmarks/lcr) · sets the order · run by Artificial Analysis · 192 models have a published result
- [GDP.pdf](/benchmarks/aagdppdf) · related evidence, not used for the order. Professional tasks that start from PDF files, run by Artificial Analysis on fewer models.
- [LongBench v2](/benchmarks/longbench-v2) · related evidence, not used for the order. Long-context questions across documents, code and dialogue.

## What to verify before choosing

- The test uses extracted text. File upload, scanned pages, tables and figures depend on the app and the document format.
- Ranked on one benchmark: AA-LCR, run by Artificial Analysis. 192 models have a published result; a model without one is not listed.

## Refine

- [Change any answer in the selector](/tools/llm-selector?audience=builders&family=documents&useCase=pdfs&priority=accuracy&privacy=hosted&budget=any&context=normal)
- [Must run on my own hardware](/tools/llm-selector?audience=builders&family=documents&useCase=pdfs&priority=accuracy&privacy=local&context=normal)
- [Cost matters most](/tools/llm-selector?audience=builders&family=documents&useCase=pdfs&priority=cost&privacy=hosted&budget=any&context=normal)
- [Speed matters most](/tools/llm-selector?audience=builders&family=documents&useCase=pdfs&priority=speed&privacy=hosted&budget=any&context=normal)
- [Long documents or a large codebase](/tools/llm-selector?audience=builders&family=documents&useCase=pdfs&priority=accuracy&privacy=hosted&budget=any&context=large)
- [Choosing an everyday assistant, not an API](/tools/llm-selector?audience=everyday&family=documents&useCase=pdfs&priority=accuracy&privacy=hosted&budget=any&context=normal)
- [Describe your own job instead](/tools/llm-selector)

## Questions

**Which AI model is best for PDFs?**

No single model is clearly best for PDFs on BenchLM's evidence. On AA-LCR, run by Artificial Analysis, Kimi K3.1 Pro (86.3%) and MiMo-V2.6-Pro (86.3%) are within 1.5 points of each other. The test uses extracted text. File upload, scanned pages, tables and figures depend on the app and the document format.

**Which models come next for PDFs?**

Claude Opus 5.5 (84.7%), DeepSeek V4.1 Flash 1017 (84.0%), Muse Wonder Full (83.7%) follow on the same benchmark. Compare the leaders on three representative examples of your own work.

**How does BenchLM pick the best ai model for pdfs?**

The page runs the LLM Selector with fixed answers: a builder choosing by accuracy, hosted processing allowed, any price, ordinary input size. The selector uses AA-LCR, one benchmark run by Artificial Analysis on every listed model, filters by the stated constraints, and orders by that evidence. It never adds a hidden fit score or a bonus for open weights or reasoning style.

**Why does this page use AA-LCR and not a general score?**

A general score answers several jobs at once. AA-LCR is closer to this job. Questions that can only be answered by reasoning across several long real-world documents, about 100,000 tokens of text per question. Only results Artificial Analysis published itself are used, so a model nobody has run there is not listed. 192 models have a result.

**What do the top models for PDFs cost?**

Among the ten highest-ranked models on this page, GPT-6 Luna costs the least for a workload of 1M input and 200k output tokens: $0.10 per million input tokens and $0.50 per million output tokens. Claude Fable 5.2 costs the most, at $8.00 and $40.00. These are API rates in the November 13, 2026 data. Assistant subscriptions are priced separately.

**Can I change the constraints?**

Yes. Every link under "Refine" opens the selector with one answer changed, and the address carries the answers so a result can be shared or reopened against the current dataset.

Data updated 2026-11-13. Method: bench-align-v5.8-2026-09-30.

Canonical page: https://benchlm.ai/best/for/pdfs