dots.ocr
chutes/dots-ocrPor chutes · familia: rednote · lanzado 2025-12-29
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacidades
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding23
- Tool calling0/40
- Structured output20/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability1/10
Agents41
- Tool calling0/35
- Structured output25/25
- Reasoning0/15
- Output token limit15/15
- Provider availability1/10
JSON / structured output80
- Structured output / JSON mode50/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume20/20
Cost efficiency100
- Headline price (log-scaled)95/95
- Has prompt-cache pricing5/5
Long context46
- Context window (100K → 2M)36/90
- Has published price for full window10/10
Vision78
- Accepts image input50/50
- Context window (10K → 1M)17/30
- Has published price10/10
- Provider availability1/10
Production-readiness65
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.06 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $0.11 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.03 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $0.09 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $0.13 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Detalle de precios
Precio recomendado de chutes · rednote-hilab/dots.ocr
Disponible en 1 proveedores
| Proveedor | ID de modelo del proveedor | Entrada / 1M | Salida / 1M | Contexto | Lanzado |
|---|---|---|---|---|---|
| Chutes chutes | rednote-hilab/dots.ocr | $0.010 | $0.011 | 131K | 2025-12-29 |
Frequently asked questions
How much does dots.ocr cost?
dots.ocr costs $0.010 per 1M input tokens and $0.011 per 1M output tokens, sourced from chutes. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of dots.ocr?
dots.ocr has a context window of 131K tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does dots.ocr support tool calling?
No. dots.ocr does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does dots.ocr support structured output / JSON mode?
Yes. dots.ocr supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Can dots.ocr accept image input?
Yes. dots.ocr accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check chutes's docs for the exact rule.
Is dots.ocr open-weight?
Yes. dots.ocr's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to dots.ocr?
If dots.ocr doesn't fit, consider Hermes 4 14B, MiMo V2 Flash TEE. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More chutes models
- Hermes 4 14B$0.01 in / $0.05 out
- MiMo V2 Flash TEE$0.09 in / $0.29 out
Capability lists this model is in
Última actualización:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.