# Best LLM for OCR & Documents

> Updated Oct 20, 2026 · evidence through Oct 20, 2026. Synthesized from cited community evidence and open benchmarks, not vendor marketing. Canonical page: https://modelmatchbook.com/best/vision-ocr

Gemini 3.8 Flash is the provisional first pick for OCR & Documents as of October 20, 2026, while independent task measurements are still limited. Ranks #2 on LMArena Vision's OCR category with Elo 1316, though production use shows duplicate and contradictory extraction when chunking multi-page documents. Watch out: Watch for duplicate entries and conflicting field values when processing multi-page PDFs with mixed OCR strategies, as seen with 18 duplicate product entries and contradictory brand/specification data in a 5-page retail catalog. Gemini 3.7 Flash is the next-ranked alternative. Ranks #5 on LMArena Vision's OCR category with Elo 1308 and #4 on the broader vision arena, showing strong general image understanding alongside competitive OCR performance. [1,2,3,4]

## 1. Gemini 3.8 Flash
https://modelmatchbook.com/models/google/gemini-3.8-flash-20260902

Ranks #2 on LMArena Vision's OCR category with Elo 1316, though production use shows duplicate and contradictory extraction when chunking multi-page documents.

**Best when:** Use for single-image OCR where raw text extraction accuracy matters most, as it scores second-highest in blind human preference for reading text from images. [1]

**Watch out for:**
- Watch for duplicate entries and conflicting field values when processing multi-page PDFs with mixed OCR strategies, as seen with 18 duplicate product entries and contradictory brand/specification data in a 5-page retail catalog. [2]

## 2. Gemini 3.7 Flash
https://modelmatchbook.com/models/google/gemini-3.7-flash-20260813

Ranks #5 on LMArena Vision's OCR category with Elo 1308 and #4 on the broader vision arena, showing strong general image understanding alongside competitive OCR performance.

**Best when:** Deploy for OCR tasks requiring balanced performance across general vision and text extraction, given its top-5 standing in both categories. [4,3]

## 3. Muse Spark 1.2
https://modelmatchbook.com/models/meta/muse-spark-1.2-20260805

Ties for #6 on LMArena Vision's OCR category with Elo 1307, with matching rank in the broader vision arena.

**Best when:** Consider when you need consistent performance across vision and OCR without specialization in either, as it holds identical ranks in both evaluation categories. [6,7]

## 4. Claude Opus 5.5
https://modelmatchbook.com/models/anthropic/claude-opus-5.5-20260921

Ranks #8 on LMArena Vision's OCR category with Elo 1306, trailing its vision arena rank by two positions.

**Best when:** Use when OCR is one component of a broader vision pipeline, as its general image understanding slightly outperforms its text extraction ranking. [8,9]

## 5. Claude Fable 5.1
https://modelmatchbook.com/models/anthropic/claude-fable-5.1-20260831

Ranks #2 on LMArena's document arena with score 1513, the strongest document-specific result in the pool, despite mid-tier OCR and vision rankings.

**Best when:** Prioritize for end-to-end document processing workflows where layout understanding and document structure matter more than raw OCR speed, given its #2 document arena standing. [10]

**Watch out for:**
- Expect lower blind preference scores on pure OCR tasks compared to document tasks, as it ranks #10 in OCR category versus #2 in document arena. [11,10]

## 6. GPT-6.1 Sol
https://modelmatchbook.com/models/openai/gpt-6.1-sol-20260929

Ranks #5 on LMArena's vision arena with Elo 1294 but drops to #15 on OCR-specific evaluation, showing stronger general vision than text extraction preference.

**Best when:** Its strongest result for this task is on LMArena Vision, which measures reading images, screenshots, and documents. [12]

**Watch out for:**
- Avoid when pure text extraction accuracy is the primary criterion, as its OCR category rank of #15 with Elo 1297 falls below top-tier alternatives. [13]

## 7. GLM 5.3 Flash
https://modelmatchbook.com/models/z-ai/glm-5.3-flash-20260826

GLM 5.3 Flash ranks #24 of 81 on LMArena's vision arena (Elo 1271), based on human preference on image-understanding tasks.

**Best when:** Its strongest result for this task is on LMArena Vision, which measures reading images, screenshots, and documents. [5]

## 8. Muse Spark 1.3
https://modelmatchbook.com/models/meta/muse-spark-1.3-20260902

Muse Spark 1.3 ranks #6 of 75 on LMArena Vision's OCR category (Elo 1307), based on blind human preference for reading text out of images.

**Best when:** Its strongest result for this task is on LMArena Vision OCR, which measures reading text out of images and scans. [14]

## FAQ

**What is the best model for OCR & Documents?**

Gemini 3.8 Flash ranks first as of October 20, 2026. Ranks #2 on LMArena Vision's OCR category with Elo 1316, though production use shows duplicate and contradictory extraction when chunking multi-page documents. [1,2]

**What should I watch out for with Gemini 3.8 Flash?**

Watch for duplicate entries and conflicting field values when processing multi-page PDFs with mixed OCR strategies, as seen with 18 duplicate product entries and contradictory brand/specification data in a 5-page retail catalog. [2]

**What is the best alternative to Gemini 3.8 Flash?**

Gemini 3.7 Flash ranks second. Ranks #5 on LMArena Vision's OCR category with Elo 1308 and #4 on the broader vision arena, showing strong general image understanding alongside competitive OCR performance. [3,4]

**Which open-weight model ranks highest for OCR & Documents?**

GLM 5.3 Flash is the highest-ranked open-weight model, at #7. GLM 5.3 Flash ranks #24 of 81 on LMArena's vision arena (Elo 1271), based on human preference on image-understanding tasks. [5]

## Sources
1. "Within the 75 catalog models matched to this evaluation: Ranks #2 of 75 on LMArena Vision's OCR category (Elo 1316), based on blind human preference for reading text out of images." (LMArena Vision OCR, https://lmarena.ai/leaderboard)
2. "現象 評測租戶知識庫「DM」的 DM_家樂福_采集漫旅_商品頁_p11-15.pdf（5 頁）以 Gemini 3.8 Flash 走混合 OCR（切片 2x3 + 整頁）處理後，落庫內容出現： - 16 組 【頁面標題】（其中 10 組為「不詳」），但原始 PDF 只有 5 頁 - 92 筆 商品： 條目，只有 74 筆唯一 — 18 筆重複 - 重複條目的欄位互相矛盾，例如同一商品「鮮剖100%椰子汁」出現兩次： - 一處 品牌：半天水、規格：含椰肉/純天然 520毫升×3、原價：120~129 - 另一處 品牌：鮮剖、規格：520毫升×3、原價：120-129元、備註：含椰肉/純天然 - 前者所在區段有完整頁面標題「單筆購買第12~15頁…」，後者所在區段頁面標題為「不詳」 已排除 chunk overlap 造成的假重複：把 chunk 依 chunk_index 串接後長度等於各 chunk 長度總和（無重疊），上述統計是文件本體的真實內容。 也已排除 worker 跑舊版：VM `poc-rag-v…" (larry610881, https://github.com/larry610881/agentic-rag-customer-service/issues/83)
3. "Within the 75 catalog models matched to this evaluation: Ranks #5 of 75 on LMArena Vision's OCR category (Elo 1308), based on blind human preference for reading text out of images." (LMArena Vision OCR, https://lmarena.ai/leaderboard)
4. "Within the 81 catalog models matched to this evaluation: Ranks #4 of 81 on LMArena's vision arena (Elo 1295), based on human preference on image-understanding tasks." (LMArena vision arena, https://lmarena.ai/leaderboard)
5. "Within the 81 catalog models matched to this evaluation: Ranks #24 of 81 on LMArena's vision arena (Elo 1271), based on human preference on image-understanding tasks." (LMArena vision arena, https://lmarena.ai/leaderboard)
6. "Within the 81 catalog models matched to this evaluation: Ranks #6 of 81 on LMArena's vision arena (Elo 1293), based on human preference on image-understanding tasks." (LMArena vision arena, https://lmarena.ai/leaderboard)
7. "Within the 75 catalog models matched to this evaluation: Ranks #6 of 75 on LMArena Vision's OCR category (Elo 1307), based on blind human preference for reading text out of images." (LMArena Vision OCR, https://lmarena.ai/leaderboard)
8. "Within the 81 catalog models matched to this evaluation: Ranks #6 of 81 on LMArena's vision arena (Elo 1293), based on human preference on image-understanding tasks." (LMArena vision arena, https://lmarena.ai/leaderboard)
9. "Within the 75 catalog models matched to this evaluation: Ranks #8 of 75 on LMArena Vision's OCR category (Elo 1306), based on blind human preference for reading text out of images." (LMArena Vision OCR, https://lmarena.ai/leaderboard)
10. "Within the 34 catalog models matched to this evaluation: Ranks #2 of 34 on LMArena's document arena (score 1513), based on blind preference for document tasks." (LMArena document arena, https://lmarena.ai/leaderboard)
11. "Within the 75 catalog models matched to this evaluation: Ranks #10 of 75 on LMArena Vision's OCR category (Elo 1303), based on blind human preference for reading text out of images." (LMArena Vision OCR, https://lmarena.ai/leaderboard)
12. "Within the 81 catalog models matched to this evaluation: Ranks #5 of 81 on LMArena's vision arena (Elo 1294), based on human preference on image-understanding tasks." (LMArena vision arena, https://lmarena.ai/leaderboard)
13. "Within the 75 catalog models matched to this evaluation: Ranks #15 of 75 on LMArena Vision's OCR category (Elo 1297), based on blind human preference for reading text out of images." (LMArena Vision OCR, https://lmarena.ai/leaderboard)
14. "Within the 75 catalog models matched to this evaluation: Ranks #6 of 75 on LMArena Vision's OCR category (Elo 1307), based on blind human preference for reading text out of images." (LMArena Vision OCR, https://lmarena.ai/leaderboard)