Model Selection

Choose the right LLM for your agent’s needs—or let the platform pick for you

Every agent needs a brain. Abundly supports models from all the major AI providers—Anthropic, OpenAI, Google, xAI, and more—and you choose which one powers your agent. Or don’t choose at all, and let the platform pick a recommended default for you.

Available models

Your agents get access to a broad range of models through one consistent interface. Within each family, models are listed from most capable (and most expensive) to cheapest:

Provider	Models, most capable first	Details
Anthropic Claude	Fable, Opus, Sonnet, Haiku	Anthropic’s model overview
OpenAI GPT	Sol, Terra, Luna	OpenAI’s model overview
Google Gemini	Pro, Flash, Flash Lite	Google’s Gemini model overview
xAI Grok	Grok	xAI’s model overview

Some workspaces also include additional families such as Qwen, Mistral, and GLM (EU-hosted via Scaleway serverless inference).

All models work with the same agent capabilities and tools—you don’t lose any platform features by switching models.

Grok models are available when your workspace has xAI configured. Workspace admins control which models are available—see Workspace administration.

Default behavior

If you don’t have a preference, leave the model set to (no preference). This is the default, and it means Abundly will use whatever model we think works best for general agentic behavior. We continuously evaluate models and update this default as better options become available.

This is the right choice for most users—you get good performance without having to think about model selection at all.

When to pick a different model

You only need to pick a model if you don’t want the default. The common reasons are:

• You want more capability. The agent works on hard problems, and you’re willing to spend more credits per request for a more powerful model.
• You want lower cost. The agent does simple or high-volume work that doesn’t need a top-tier model, and a cheaper model does the job for a fraction of the credits.
• You’ve found a better fit. You’ve tested and determined that a specific model works better for your specific use case.

How to compare models

Within a provider’s model family, the ordering is reliable: bigger models are more capable and cost more, smaller models are cheaper and usually faster. For example, in Anthropic’s family, Fable is more capable (and more expensive) than Opus, which is above Sonnet, which is above Haiku.

Comparisons across providers are far less reliable. Benchmarks and marketing claims rarely predict how a model performs on your specific task—in some use cases a small, cheap model from one provider outperforms a flagship model from another. Even speed can’t be stated as a fixed fact, since it varies with how busy the provider’s servers are.

The most reliable way to compare is to test with your own agent on real work. You can change the model at any time—even mid-conversation—and switch back if you’re not happy. The model picker shows a cost indicator (one to six dollar signs) next to each model. More dollar signs means more expensive—and the scale isn’t linear, so each step up can mean a several-fold price difference.

For a more systematic comparison, use agent evals to run the same test cases across different models—and find the best fit for your use case, or discover that a cheaper model does the job just as well.

Not sure where to start? We find the Claude models work very well for agentic behavior—Sonnet for everyday work, Opus or Fable for the hardest problems, Haiku for speed and volume.

Model aliases vs specific versions

When selecting a model, you can choose between:

• Aliases like “Claude Sonnet Best” — automatically points to the latest version that we recommend
• Specific versions like “Claude Sonnet 5” — locked to that exact version

We recommend using aliases in most cases. Models improve constantly, and when a new version of a model arrives, it usually makes sense to upgrade. By selecting an alias, you won’t need to remember to do that—Abundly will point the alias to the newer version once we’ve verified it works well (typically after some internal testing).

When an alias is selected, hovering the model picker button in the chat’s top bar shows a tooltip indicating which specific model version the alias currently points to—so you can always see what you are actually running.

When an alias is updated, your agent’s behavior may change. Usually for the better—models tend to get smarter—but not always in the way you expect. For example, a newer model may follow instructions more literally, surfacing problems in your instructions that the previous model quietly ignored. If that happens, a small tweak to the instructions usually fixes it.

If you have carefully tuned your agent and don’t want its behavior to change, lock it to a specific model version instead. The tradeoff—much like turning off auto-update for software—is that the agent won’t automatically benefit from model improvements, and you decide when to upgrade.

Per-context model selection

Different contexts in your agent’s life may benefit from different models. You can configure a model and effort preference per context:

• Chat — Conversations you start with the agent
• Scheduled tasks — When the agent runs on a schedule
• Email — When the agent responds to incoming emails
• Slack — When the agent responds to Slack messages
• Microsoft Teams — When the agent responds to Teams messages
• Agent messages — When other agents send your agent a message

If a context has no override, it inherits the agent’s default model.

Examples:

• Use a cheaper model for handling routine scheduled work
• Use a more capable model for handling incoming emails

Per-task model overrides

Individual scheduled tasks can have their own model and effort setting on top of the per-context default. Open a task’s settings card to configure it. This is useful when one particular task needs heavier reasoning than your other scheduled tasks.

Per-chat model switching

Each chat keeps its own model: new chats start with the agent’s chat model, and you can switch mid-conversation using the model picker in the chat’s top bar. Switching affects only that chat—the agent’s settings and other chats are untouched.

Some built-in platform skills, such as agent optimization, give much better results on a strong model. If a stronger recommended model is available in your workspace when such a skill is needed, the agent shows a card suggesting a switch to it. You can accept (the switch applies only to that chat) or keep the current model and continue.

Remote conversations (Slack, Teams, WhatsApp, and so on) always use the model configured for that channel and can’t be switched per conversation.

Effort level

Most models support thinking, where the model spends effort reasoning before it responds. You control this with a single effort setting:

• Faster — The model thinks briefly. Quickest and cheapest, good for simple tasks.
• Balanced — The model’s own recommended default. A solid middle ground for most tasks.
• Smarter — The model is allowed to think more deeply. Best for complex tasks, but may increase processing time and credit usage.

You can configure the effort level in the default model settings, per context, per chat, and on individual scheduled tasks. Models that don’t support thinking don’t show the control.

Thinking is never fully turned off on models that support it — Faster uses the model’s cheapest thinking mode. A few models don’t distinguish every level (for example, Balanced and Smarter behave the same on some models); the platform always picks the closest setting the model supports.

Workspace administration

Beyond per-agent settings, workspace admins get central control over model usage across the entire workspace. These settings live under Workspace management.

Enabling and disabling models

Admins can enable or disable specific catalog models for the entire workspace from Workspace management → Model selection—useful for controlling costs, meeting policy requirements, or standardizing on a smaller set of models. Models that Abundly has disabled platform-wide remain locked and cannot be re-enabled at the workspace level.

If an agent is configured with a model that gets disabled, it stops with a clear error rather than silently substituting a different model—admins stay in control of exactly which models agents run on.

Workspace default model

On the same tab, admins can set a workspace default model. Agents that have no model preference of their own use this model instead of the Abundly default—a quick way to standardize your whole workspace on a particular model or alias.

Policy for new models

When Abundly adds a new model to the catalog, your workspace can either make it available automatically or hold it back until an admin reviews it. Choose the policy under Workspace management → Settings → LLM models:

• New models on by default — Your workspace benefits from new models as soon as Abundly adds them. This is the default.
• Require approval for new models — New models stay off until an admin enables them, giving you a controlled, admin-curated set of models. Models that are already available stay available.

FAQ

Why isn't a 'Best' alias always pointing to the very latest version?

Newer models are usually better, but not always—especially for agentic work. Since an alias update affects many agents at once, we do some internal testing before pointing an alias to a new version. That’s why an alias can lag a little behind a model release. If you want the latest version right away, you can select it as a specific version in the model picker.

How does pricing work for different models?

Each model has different credit costs per token. Within a family, more capable models cost more per request—more dollar signs in the model picker means more expensive. Check your usage dashboard to monitor credit consumption.

What happens when I switch models in the middle of a conversation?

The new model takes over from your next message, with the full conversation history available to it. You can switch models freely within a conversation.

Learn more

Agent Evals

Compare how your agent performs on different models with automated test cases

Usage & Limits

Track credit consumption and set daily limits for your agents

Pricing

Understand how credits and model costs work