Recommendation for Charts & diagrams
Charts & Diagrams
Our top recommendation for Charts & Diagrams, based on the public evidence we track, is Anthropic: Claude Fable 5.[1] DeepSeek: DeepSeek V4 Flash Vision Exp is the next-ranked alternative. Use for rapid chart extraction where speed matters: production data shows 9-13 second response times for visual qualification tasks, with presentation generation completing in ~95 seconds.
About this recommendation
- Updated
- Sep 6, 2026
- Evidence through
- Sep 6, 2026
- Sources
- 3
- Revision
- v58
Decision audit
Why this result
Inspect the inputs and the computed order behind the recommendation.
Models screened
20
live candidates
Evaluation feeds
5
task-weighted
Winner coverage
37%
intended feed weight
Largest provider share
1 of 2
Anthropic
Sources evaluated
The task sets these weights before any model is scored.
| Evaluation feed | Weight | Winner result | Field measured |
|---|---|---|---|
| VLMEvalKit tasksunavailable | 40% | feed unavailable | 0/20 |
| LMArena Document | 20% | #3 | 11/20 |
| LMArena Vision | 15% | #1 | 17/20 |
| Structured-output evalunavailable | 15% | feed unavailable | 0/20 |
| OpenRouter usage | 10% | 86/100 | 20/20 |
Provider concentration
Each exact model is scored separately; provider identity is not a ranking input.
- Anthropic1 model
- deepseek1 model
Decision table
Every published model is shown in computed order. Practitioner sources are distinct community threads, not the citations repeated in the prose below.
| Rank | Model | Relative score | Coverage | Practitioner evidence | Strongest measured reason |
|---|---|---|---|---|---|
| 01 | Claude Fable 5Anthropic | 54 | 37% | no linked practitioner threads | #1 LMArena Vision · #3 LMArena Document |
| 02 | DeepSeek V4 Flash Vision Expdeepseek | 52 | 11% | 4 threads · 3 families · 0 cautions | OpenRouter usage 91/100 normalized |
Relative score combines normalized benchmark quality and signal coverage; independent practitioner evidence and freshness are bounded tie-breakers. It is an ordering score, not an absolute quality percentage. The writing model receives this order and cannot change it.
Anthropic: Claude Fable 5 ranks #1 of 68 on LMArena's vision arena (Elo 1313), based on human preference on image-understanding tasks.
Best when: Consider only after reviewing the cited caution.
Experimental vision model with documented API support for base64 and URL image inputs, showing fast inference times around 9-13 seconds per call in production visual qualification workflows.
Best when: Use for rapid chart extraction where speed matters: production data shows 9-13 second response times for visual qualification tasks, with presentation generation completing in ~95 seconds.
Tips
- Use for rapid chart extraction where speed matters: production data shows 9-13 second response times for visual qualification tasks, with presentation generation completing in ~95 seconds.
- Send screenshots directly as base64 data URLs when working with board-game or interface captures where external hosting is impractical.
Watch out for
- Watch for catalog lag: the model may not appear in your provider's model list even when the API endpoint is live, requiring manual configuration.
Frequently asked
- What is an alternative to Anthropic: Claude Fable 5?
- DeepSeek: DeepSeek V4 Flash Vision Exp is the next-ranked option. Use for rapid chart extraction where speed matters: production data shows 9-13 second response times for visual qualification tasks, with presentation generation completing in ~95 seconds.[1]
Sources
- 1
“## Objetivo Reducir el tiempo real de calificación, digitalización y generación de presentaciones sin sacrificar calidad, trazabilidad ni perder solicitudes en curso. ## Evidencia de producción (últimos 14 días) - Calificación completa: p50 157 s; p95 516 s. - Digitalización: p50 189 s. - Presentación reciente: 366 s. - DeepSeek V4 Flash Vision Exp en calificación visual: ~9–13 s por llamada exitosa. - Presentaciones con ese modelo: ~95 s por llamada y hasta tres llamadas por regeneración/revis…”
Andres-back · GitHub · Sep 4, 2026 - 2
“## Resolution 研究完成（来源：官方图像理解指南 https://api-docs.deepseek.com/zh-cn/guides/vision + Tool Calls / 思考模式指南；**无任何 live 调用**）。完整契约见 `docs/research/vision-api-contract.md`（分支 `research/vision-api-contract`）。核心结论： - **模型**：`deepseek-v4-flash-vision-exp`（experimental，额外接受图像输入）。OpenAI 兼容 `/chat/completions`。 - **传图**：`content` 必须是**块数组**（非纯字符串）。三种方式——base64 data URL（`image_url` 块）、外链 URL（≤8192 字符，单图 ≤32 MiB，60s 内下载）、Files API `file_id`。扫雷棋盘建议走 base64 data URL。 - **`detail`**：`low`（512×512，更快/省）/ `high` /…”
MiSmiler · GitHub · Aug 24, 2026 - 3
“## Problem DeepSeek released its first multimodal vision model **`deepseek-v4-flash-vision-exp`** on 2026-08-21 (official announcement: https://api-docs.deepseek.com/news/news260821/). It is live on the DeepSeek API (verified: `GET https://api.deepseek.com/models` lists it, and chat completions with image input work). However, pi's built-in DeepSeek model catalog still only contains `deepseek-v4-flash` and `deepseek-v4-pro`. As a result, the new vision model does **not** appear in `/model` and…”
phixz668-hub · GitHub · Aug 24, 2026
Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.