Feature support by vendor and model
What you can use with each vendor and model on Zabio: tools, images, structured output, reasoning control, prompt caching, flex and batch. Built from the live catalog, so it changes when the models do.
On every model
- One OpenAI-compatible API and one key for all 8 vendors, with the cost and the saving in every response.
- Smart Routing: send auto and Zabio picks the cheapest model that does the job well, among the models that support what your request needs.
- Smart Splitting under auto, spend caps per key, teams, and lists of allowed models.
By vendor
| Vendor | Models | Tools | Images | Structured output | Reasoning control | Prompt caching | Flex | Batch | Data processed in |
|---|---|---|---|---|---|---|---|---|---|
| Anthropic | 14 | All | All | All | 11 of 14 | All | None | All | United States |
| OpenAI | 29 | 26 of 29 | 26 of 29 | 26 of 29 | 20 of 29 | 26 of 29 | 18 of 29 | 28 of 29 | United States |
| 8 | All | All | All | All | All | All | All | United States | |
| DeepSeek | 2 | All | 1 of 2 | None | All | All | None | None | China |
| xAI | 2 | All | All | All | All | All | None | None | United States |
| Mistral AI | 8 | All | 7 of 8 | All | 1 of 8 | All | None | 7 of 8 | Not guaranteed |
| Together AI | 14 | All | 6 of 14 | 13 of 14 | 13 of 14 | 11 of 14 | None | 1 of 14 | North America |
| Alibaba Cloud (International) | 33 | 32 of 33 | 18 of 33 | 27 of 33 | 27 of 33 | 13 of 33 | None | None | Outside mainland China |
| Alibaba Cloud (US) | 4 | All | 2 of 4 | All | All | 2 of 4 | None | None | US (Virginia) |
Every chat model
| Model | Context | Max output | Tools | Images | Structured output | Reasoning control | Cached input | Flex | Batch | In auto | Data processed in |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Anthropic | |||||||||||
claude-haiku-4-5 | 200K | 64K | ✓ | ✓ | ✓ | $0.1 per million | ✓ | ✓ | United States | ||
claude-sonnet-5 | 1M | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.2 per million | ✓ | ✓ | United States | |
claude-sonnet-4-6 | 1M | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.3 per million | ✓ | United States | ||
claude-opus-5-5 | 1M | 128K | ✓ | ✓ | ✓ | low / medium / high / xhigh | $0.2 per million | ✓ | ✓ | United States | |
claude-opus-5 | 1M | 128K | ✓ | ✓ | ✓ | low / medium / high / xhigh | $0.5 per million | ✓ | ✓ | United States | |
claude-opus-4-8 | 1M | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.5 per million | ✓ | United States | ||
claude-fable-5-1 | 1M | 128K | ✓ | ✓ | ✓ | low / medium / high / xhigh | $0.25 per million | ✓ | ✓ | United States | |
claude-sonnet-5-5 | 1M | 128K | ✓ | ✓ | ✓ | low / medium / high / xhigh | $0.1 per million | ✓ | United States | ||
claude-fable-5 | 1M | 128K | ✓ | ✓ | ✓ | low / medium / high / xhigh | $1 per million | ✓ | United States | ||
claude-opus-4-7 | 1M | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.5 per million | ✓ | United States | ||
claude-opus-4-6 | 1M | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.5 per million | ✓ | United States | ||
claude-opus-4-5 | 200K | 64K | ✓ | ✓ | ✓ | $0.5 per million | ✓ | United States | |||
claude-sonnet-4-5 | 200K | 64K | ✓ | ✓ | ✓ | $0.3 per million | ✓ | United States | |||
claude-haiku-5-5 | 1M | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.01 per million | ✓ | United States | ||
| OpenAI | |||||||||||
gpt-5.4-nano | 400K | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.02 per million | ✓ | ✓ | ✓ | United States |
gpt-5-mini | 400K | 128K | ✓ | ✓ | ✓ | minimal / low / medium / high | $0.03 per million | ✓ | ✓ | ✓ | United States |
gpt-5.4-mini | 400K | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.08 per million | ✓ | ✓ | ✓ | United States |
gpt-5.4 | 400K | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.25 per million | ✓ | ✓ | ✓ | United States |
gpt-5.5 | 400K | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.5 per million | ✓ | ✓ | ✓ | United States |
gpt-4.1 | 1M | 33K | ✓ | ✓ | ✓ | $0.5 per million | ✓ | United States | |||
gpt-4.1-mini | 1M | 33K | ✓ | ✓ | ✓ | $0.1 per million | ✓ | United States | |||
gpt-4o-mini | 128K | 16K | ✓ | ✓ | ✓ | $0.08 per million | ✓ | United States | |||
gpt-6-astra | 400K | 128K | ✓ | ✓ | low / medium / high / xhigh | $1 per million | ✓ | ✓ | ✓ | United States | |
gpt-6.1-sol | 400K | 128K | ✓ | ✓ | ✓ | low / medium / high / xhigh | $0.1 per million | ✓ | ✓ | United States | |
gpt-6-luna | 400K | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.01 per million | ✓ | ✓ | United States | |
gpt-6-sol | 400K | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.2 per million | ✓ | ✓ | United States | |
gpt-5.6-sol | 400K | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.4 per million | ✓ | ✓ | United States | |
gpt-5.6-terra | 400K | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.2 per million | ✓ | ✓ | United States | |
gpt-5.6-luna | 400K | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.02 per million | ✓ | ✓ | United States | |
gpt-5.2 | 400K | 128K | ✓ | ✓ | ✓ | none / low / medium / high / xhigh | $0.18 per million | ✓ | ✓ | United States | |
gpt-5.1 | 400K | 128K | ✓ | ✓ | ✓ | none / low / medium / high | $0.13 per million | ✓ | ✓ | United States | |
gpt-5 | 400K | 128K | ✓ | ✓ | ✓ | minimal / low / medium / high | $0.13 per million | ✓ | ✓ | United States | |
gpt-5-nano | 400K | 128K | ✓ | ✓ | ✓ | minimal / low / medium / high | $0.01 per million | ✓ | ✓ | United States | |
o3 | 200K | 100K | ✓ | ✓ | ✓ | low / medium / high | $0.5 per million | ✓ | ✓ | United States | |
o4-mini | 200K | 100K | ✓ | ✓ | ✓ | low / medium / high | $0.28 per million | ✓ | ✓ | United States | |
o3-mini | 200K | 100K | ✓ | ✓ | low / medium / high | $0.55 per million | ✓ | United States | |||
o1 | 200K | 100K | ✓ | ✓ | ✓ | low / medium / high | $7.5 per million | ✓ | United States | ||
gpt-4.1-nano | 1M | 33K | ✓ | ✓ | ✓ | $0.03 per million | ✓ | United States | |||
gpt-4o | 128K | 16K | ✓ | ✓ | ✓ | $1.25 per million | ✓ | United States | |||
gpt-4-turbo | 128K | 4K | ✓ | ✓ | ✓ | United States | |||||
gpt-4 | 8K | 8K | ✓ | United States | |||||||
gpt-3.5-turbo | 16K | 4K | ✓ | United States | |||||||
chat-latest | 400K | 128K | ✓ | ✓ | ✓ | $0.5 per million | United States | ||||
gemini-3.5-flash-lite | 1M | 66K | ✓ | ✓ | ✓ | none / low / medium / high | $0.03 per million | ✓ | ✓ | ✓ | United States |
gemini-3.8-flash | 1M | 66K | ✓ | ✓ | ✓ | low / medium / high | $0.08 per million | ✓ | ✓ | ✓ | United States |
gemini-3.5-flash | 1M | 66K | ✓ | ✓ | ✓ | none / low / medium / high | $0.08 per million | ✓ | ✓ | United States | |
gemini-3.1-pro-preview | 200K | 66K | ✓ | ✓ | ✓ | low / medium / high | $0.2 per million | ✓ | ✓ | ✓ | United States |
gemini-3.7-flash | 1M | 66K | ✓ | ✓ | ✓ | low / medium / high | $0.08 per million | ✓ | ✓ | United States | |
gemini-3.6-flash | 1M | 66K | ✓ | ✓ | ✓ | none / low / medium / high | $0.08 per million | ✓ | ✓ | United States | |
gemini-3.1-flash-lite | 1M | 66K | ✓ | ✓ | ✓ | none / low / medium / high | $0.03 per million | ✓ | ✓ | United States | |
gemini-3-flash-preview | 1M | 66K | ✓ | ✓ | ✓ | none / low / medium / high | $0.05 per million | ✓ | ✓ | United States | |
| DeepSeek | |||||||||||
deepseek-flash | 1M | 393K | ✓ | ✓ | none / low / high / xhigh | $0.01 per million | ✓ | China | |||
deepseek-v4-pro | 1M | 393K | ✓ | none / low / high / xhigh | $0.04 per million | ✓ | China | ||||
| xAI | |||||||||||
grok-4.7 | 500K | 500K | ✓ | ✓ | ✓ | Always on | $0.55 per million | ✓ | United States | ||
grok-4.6 | 500K | 500K | ✓ | ✓ | ✓ | Always on | $0.55 per million | United States | |||
| Mistral AI | |||||||||||
mistral-small-2603 | 256K | 256K | ✓ | ✓ | ✓ | $0.02 per million | ✓ | ✓ | Not guaranteed | ||
mistral-large-2512 | 256K | 256K | ✓ | ✓ | ✓ | $0.05 per million | ✓ | ✓ | Not guaranteed | ||
mistral-medium-3-5 | 256K | 256K | ✓ | ✓ | ✓ | $0.15 per million | ✓ | ✓ | Not guaranteed | ||
codestral-2508 | 128K | 128K | ✓ | ✓ | $0.03 per million | ✓ | ✓ | Not guaranteed | |||
ministral-3b-2512 | 131K | 131K | ✓ | ✓ | ✓ | $0.01 per million | ✓ | Not guaranteed | |||
ministral-8b-2512 | 262K | 262K | ✓ | ✓ | ✓ | $0.02 per million | ✓ | Not guaranteed | |||
ministral-14b-2512 | 262K | 262K | ✓ | ✓ | ✓ | $0.02 per million | ✓ | Not guaranteed | |||
mistral-large-4-0 | 524K | 524K | ✓ | ✓ | ✓ | none / minimal | $0.07 per million | Not guaranteed | |||
| Together AI | |||||||||||
llama-3.3-70b-instruct-turbo | 131K | 131K | ✓ | ✓ | ✓ | ✓ | North America | ||||
qwen3.5-9b | 262K | 262K | ✓ | ✓ | ✓ | none / minimal | ✓ | North America | |||
gpt-oss-120b | 131K | 131K | ✓ | ✓ | Always on | ✓ | North America | ||||
deepseek-flash@together | 1M | 1M | ✓ | ✓ | none / minimal | $0.01 per million | ✓ | North America | |||
deepseek-v4-pro@together | 1.05M | 1.05M | ✓ | ✓ | none / minimal | $0.13 per million | ✓ | North America | |||
glm-5.3-flash | 1.05M | 1.05M | ✓ | ✓ | ✓ | Always on | $0.03 per million | North America | |||
glm-5.3 | 1.05M | 1.05M | ✓ | ✓ | Always on | $0.26 per million | North America | ||||
qwen3.8-2.4t-a95b | 1.01M | 1.01M | ✓ | ✓ | Always on | $0.25 per million | North America | ||||
muse-glimmer-30b | 131K | 131K | ✓ | ✓ | ✓ | Always on | $0.04 per million | North America | |||
deepseek-v4-flash-0731 | 1.05M | 1.05M | ✓ | ✓ | none / minimal | $0.03 per million | North America | ||||
inkling | 524K | 524K | ✓ | ✓ | Always on | $0.17 per million | North America | ||||
kimi-k3 | 1.05M | 1.05M | ✓ | ✓ | ✓ | none / minimal | $0.27 per million | North America | |||
glm-5.2 | 1.05M | 1.05M | ✓ | ✓ | none / minimal | $0.26 per million | North America | ||||
minimax-m3 | 524K | 524K | ✓ | ✓ | ✓ | Always on | $0.06 per million | North America | |||
| Alibaba Cloud (International) | |||||||||||
qwen3.8-max-0902 | 1M | 131K | ✓ | ✓ | ✓ | none / low / medium / high | $0.4 per million | Outside mainland China | |||
qwen3.8-omni-flash | 1M | 131K | ✓ | ✓ | ✓ | none / low / medium / high | $0.02 per million | Outside mainland China | |||
qwen3.8-flash | 1M | 131K | ✓ | ✓ | ✓ | none / low / medium / high | $0.03 per million | Outside mainland China | |||
qwen3.8-27b | 1M | 131K | ✓ | ✓ | ✓ | none / low / medium / high | $0.1 per million | Outside mainland China | |||
qwen3.8-2.4t-a95b@alibaba-intl | 1M | 131K | ✓ | ✓ | Always on | $0.4 per million | Outside mainland China | ||||
qwen3.7-plus | 1M | 131K | ✓ | ✓ | ✓ | none / low / medium / high | $0.06 per million | Outside mainland China | |||
qwen3.7-max | 1M | 131K | ✓ | ✓ | none / low / medium / high | $0.5 per million | Outside mainland China | ||||
qwen3.5-plus-2026-04-20 | 1M | 66K | ✓ | ✓ | ✓ | none / low / medium / high | Outside mainland China | ||||
qwen3.5-plus-2026-02-15 | 1M | 66K | ✓ | ✓ | ✓ | none / low / medium / high | Outside mainland China | ||||
qwen3.5-flash-2026-02-23 | 1M | 66K | ✓ | ✓ | ✓ | none / low / medium / high | Outside mainland China | ||||
qwen3.6-flash | 1M | 66K | ✓ | ✓ | ✓ | none / low / medium / high | $0.05 per million | Outside mainland China | |||
qwen3.6-35b-a3b | 262K | 66K | ✓ | ✓ | ✓ | none / low / medium / high | Outside mainland China | ||||
qwen3.6-max-preview | 262K | 66K | ✓ | ✓ | none / low / medium / high | $0.26 per million | Outside mainland China | ||||
qwen3.6-27b | 262K | 66K | ✓ | ✓ | ✓ | none / low / medium / high | Outside mainland China | ||||
qwen3.6-plus | 1M | 66K | ✓ | ✓ | ✓ | none / low / medium / high | Outside mainland China | ||||
qwen3.5-35b-a3b | 262K | 66K | ✓ | ✓ | ✓ | none / low / medium / high | Outside mainland China | ||||
qwen3.5-27b | 262K | 66K | ✓ | ✓ | ✓ | none / low / medium / high | Outside mainland China | ||||
qwen3.5-122b-a10b | 262K | 66K | ✓ | ✓ | ✓ | none / low / medium / high | Outside mainland China | ||||
qwen3.5-397b-a17b | 262K | 66K | ✓ | ✓ | ✓ | none / low / medium / high | Outside mainland China | ||||
qwen3-vl-235b-a22b-thinking | 131K | 33K | ✓ | ✓ | Always on | Outside mainland China | |||||
qwen3-vl-235b-a22b-instruct | 262K | 33K | ✓ | ✓ | ✓ | Outside mainland China | |||||
qwen3-next-80b-a3b-thinking | 262K | 33K | ✓ | Always on | Outside mainland China | ||||||
qwen3-next-80b-a3b-instruct | 262K | 33K | ✓ | ✓ | Outside mainland China | ||||||
qwen3-30b-a3b-thinking-2507 | 262K | 33K | ✓ | Always on | Outside mainland China | ||||||
qwen3-30b-a3b-instruct-2507 | 262K | 33K | ✓ | ✓ | Outside mainland China | ||||||
qwen3-235b-a22b-thinking-2507 | 262K | 131K | ✓ | Always on | Outside mainland China | ||||||
qwen3-235b-a22b-instruct-2507 | 262K | 131K | ✓ | ✓ | Outside mainland China | ||||||
deepseek-flash@alibaba-intl | 1M | 393K | ✓ | none / minimal | $0.03 per million | Outside mainland China | |||||
deepseek-v4-flash | 1M | 393K | ✓ | ✓ | none / minimal | $0.04 per million | Outside mainland China | ||||
glm-5.3@alibaba-intl | 203K | 131K | ✓ | ✓ | Always on | Outside mainland China | |||||
glm-5.2@alibaba-intl | 203K | 131K | ✓ | ✓ | none / low / medium / high | Outside mainland China | |||||
deepseek-v4-flash-0731@alibaba-intl | 131K | 33K | ✓ | ✓ | $0.09 per million | Outside mainland China | |||||
deepseek-v4-pro-0813 | 131K | 33K | $0.26 per million | Outside mainland China | |||||||
| Alibaba Cloud (US) | |||||||||||
qwen3.7-max@alibaba-us | 1M | 131K | ✓ | ✓ | none / low / medium / high | $0.5 per million | US (Virginia) | ||||
qwen3.7-plus@alibaba-us | 1M | 131K | ✓ | ✓ | ✓ | none / low / medium / high | $0.06 per million | US (Virginia) | |||
qwen3.6-flash@alibaba-us | 1M | 66K | ✓ | ✓ | ✓ | none / low / medium / high | US (Virginia) | ||||
glm-5.2@alibaba-us | 203K | 131K | ✓ | ✓ | none / low / medium / high | US (Virginia) | |||||
Embedding and decision models
| Model | Vendor | Kind | Data processed in |
|---|---|---|---|
text-embedding-3-small | OpenAI | Embeddings | United States |
text-embedding-3-large | OpenAI | Embeddings | United States |
gemini-embedding-001 | Embeddings | United States | |
mistral-embed-2312 | Mistral AI | Embeddings | Not guaranteed |
jev-1.13.0 | TypeSafe AI | Decision model | United States |
Good to know
- Flex is about half price for requests that can wait, on the models checked in its column.
- Batch is about half price for jobs that can wait hours, on the models checked in its column; inside a batch, auto only picks those models.
- With auto, Zabio only considers the models that support what your request needs: tools, images, structured output, and a context window that fits.
- Prompt caching shows the price of cached input per million tokens; a model without one bills repeated input at the normal price.