Skip to main content
Model Inference supports models from Anthropic and OpenAI, all hosted within your data plane. This reference lists available models and links to their official documentation.

​
Available models

Different Claude generations use different structured-output paths under the hood. Sonnet 5.0 and Opus 4.7 / 4.8 / 5.0 use the forced-tool-use path (schema conformance is enforced by client-side validation and these models do not accept temperature or top_p); all other models use native structured output. See Structured Output for the differences.

​
Anthropic Claude models

Claude models excel at analysis, summarization, and following detailed instructions.

​
Claude Haiku 4.5

Model ID: anthropic.claude-haiku-4.5 The fastest Claude model, optimized for high-throughput tasks where speed matters more than deep analysis. Best for:
  • Simple classification tasks
  • Data extraction
  • Quick summaries
  • High-volume processing
Official documentation: Claude Haiku

​
Claude Sonnet 4.5

Model ID: anthropic.claude-sonnet-4.5 A balanced model offering strong performance across most tasks with good efficiency. Best for:
  • Content analysis
  • Code generation
  • Multi-step reasoning
  • General-purpose tasks
Official documentation: Claude Sonnet

​
Claude Sonnet 4.6

Model ID: anthropic.claude-sonnet-4.6 A Sonnet generation with improved reasoning and instruction following. Best for:
  • Content analysis
  • Code generation
  • Multi-step reasoning
  • General-purpose tasks
Official documentation: Claude Sonnet

​
Claude Sonnet 5.0

Model ID: anthropic.claude-sonnet-5.0 The latest Sonnet generation. Uses the forced-tool-use structured-output path — the platform enforces output_format_schema conformance with client-side validation instead of native grammar constraints. This model does not accept temperature or top_p; those sampling parameters are silently dropped from your request. Best for:
  • Content analysis
  • Code generation
  • Multi-step reasoning where the newest Sonnet capabilities are preferred
Official documentation: Claude Sonnet

​
Claude Opus 4.5

Model ID: anthropic.claude-opus-4.5 A highly capable Claude model for complex tasks requiring deep analysis and nuanced understanding. Best for:
  • Complex reasoning chains
  • Nuanced analysis
  • Research tasks
  • High-stakes decisions
Official documentation: Claude Opus

​
Claude Opus 4.6

Model ID: anthropic.claude-opus-4.6 An Opus generation with strong capabilities for demanding inference workloads. Best for:
  • Complex reasoning chains
  • Nuanced analysis
  • Research tasks
  • High-stakes decisions
Official documentation: Claude Opus

​
Claude Opus 4.7

Model ID: anthropic.claude-opus-4.7 A newer Opus generation for the most demanding inference workloads. Uses the forced-tool-use structured-output path — the platform enforces output_format_schema conformance with client-side validation. This model does not accept temperature or top_p; those sampling parameters are silently dropped from your request. Best for:
  • Complex reasoning chains
  • Nuanced analysis
  • Research tasks
Official documentation: Claude Opus

​
Claude Opus 4.8

Model ID: anthropic.claude-opus-4.8 The latest Opus generation, the most capable Claude model available. Uses the forced-tool-use structured-output path with client-side schema validation, and does not accept temperature or top_p. Best for:
  • Complex reasoning chains where the latest capabilities are preferred
  • Nuanced analysis
  • Research tasks
  • High-stakes decisions
Official documentation: Claude Opus

​
Claude Opus 5.0

Model ID: anthropic.claude-opus-5.0 The newest Opus generation. Opus 5.0 supports extended thinking — pass an inference_config.thinking setting to have the model reason before answering. Opus models often withhold the reasoning text and return only a provider signature; either way the reasoning block is returned on the assistant turn and can be replayed verbatim on a continuation. Uses the forced-tool-use structured-output path with client-side schema validation, and does not accept temperature or top_p. Currently available on Bedrock data planes only; Snowflake Cortex support will follow. Best for:
  • Complex reasoning chains where the latest Opus capabilities are preferred
  • Nuanced analysis
  • Research tasks
  • High-stakes decisions
Official documentation: Claude Opus

​
OpenAI models

OpenAI models offer strong reasoning capabilities and broad knowledge.

​
GPT-4.1

Model ID: openai.gpt-4.1 Advanced reasoning model with strong analytical capabilities. Best for:
  • Complex analysis
  • Technical reasoning
  • Multi-domain tasks
Official documentation: GPT-4 Models

​
o4-mini

Model ID: openai.o4-mini Smaller, faster model optimized for efficiency. Best for:
  • Quick responses
  • Simple tasks
  • Cost-sensitive applications
Official documentation: OpenAI Models

​
GPT-oss-120b

Model ID: openai.gpt-oss-120b Open-source large language model with broad capabilities. Best for:
  • General-purpose tasks
  • Open-source requirements

​
Extended thinking

Some models can reason before answering. Enable it per-request by setting inference_config.thinking on your Model Inference call. The field takes one of three shapes:
  • "disabled" — the bare string. Explicit off, worth sending to a model that thinks by default so you are not billed for reasoning you did not ask for.
  • { "type": "effort", "level": "low" | "medium" | "high" } — for models that take an effort scale.
  • { "type": "budget", "budget_tokens": 4096 } — for models that take a token budget. Floor is 1024 (the provider’s), and the budget must be smaller than max_tokens because thinking is spent from the same output budget as the answer.
Omit thinking to get the model’s own default behavior. A setting the target model does not accept returns 400 rather than being silently downgraded — support is per-model and per-data-plane, so the same setting can be accepted on one data plane and refused on another. Reasoning comes back on the assistant turn as reasoning content blocks (working as words plus a provider signature, or a signature alone when the model withholds the text), or as redacted_reasoning blocks when the provider encrypts the reasoning. Both shapes can be replayed verbatim on a follow-up turn — send them back unchanged, or omit them if you only need to display the answer.

​
Choosing a model

For detailed guidance on model selection, see Choosing the Right Model.

​
Usage example

​
Related content

Choosing Models

Detailed model selection guide

Running Inference

Submit inference requests

Model Inference API

Complete API reference

Model Inference Overview

How inference works