Agenticflow LLM Models

AgenticFlow LLM Models

Choose the right model for your agent based on capability needs, speed requirements, and reasoning depth. Use built-in credits for all models listed here.

When NOT to use this skill

Use agenticflow-built-in-credits skill instead for pricing, credits, or billing questions. Use agenticflow-mcp skill if they need external API keys (BYOK). This skill covers model selection and capabilities, not account management or credit usage.

Orient first

af bootstrap --json

Extract models[] — this is the source of truth for available models. Never hardcode model lists; they change between CLI releases.

Top 5 recommended models

Based on speed, reasoning quality, and reliability:

Fast non-reasoning models

For simple tasks where speed matters most:

Model selection guide

Need a default?

# Start with GLM-4.7 Flash — best all-rounder with reasoning
af agent create --body '{"name":"My Agent","model":"agenticflow/glm-4.7-flash","project_id":"<id>"}' --json

# If you need maximum speed and don't need reasoning, use Gemini 2.5 Flash Lite
af agent create --body '{"name":"My Agent","model":"agenticflow/gemini-2.5-flash-lite","project_id":"<id>"}' --json

Need reasoning?

• Default reasoning: glm-4.7-flash (18s, 931 tokens) — already the default above
• Vision + reasoning: gpt-5-nano (19s)
• Deep verification: qwen-3.5-flash (33s, 5K reasoning tokens)
• Maximum reasoning: deepseek-v3.2-speciale (51s)

Need speed only?

• Fastest correct: gpt-4o-mini (6s)
• Fast + large context: gemma-4-31b-it (7s)

Workforce model selection

All agents in a workforce inherit the model:

af workforce init --blueprint dev-shop --model agenticflow/glm-4.7-flash --name "My Team" --json

Reasoning configuration

Expose reasoning tokens (where supported):

af schema agent --field model_user_config --json

Check the full agent schema (for all fields):

af schema agent --json

For Gemini 2.5 Flash Lite, reasoning is hidden by default. Configure via thinking_config to expose.

Verify model availability

# Always dry-run first
af agent create --body @agent.json --dry-run --json

The CLI validates the model string at create time. Typos fail fast with an actionable hint listing known models.

Avoid these models

Based on benchmark testing (April 2026):

Fallback reasoning models

If top 5 unavailable:

Cleanup

Test agents consume credits. Delete when done:

af agent delete --agent-id <id> --json

On errors

• 400 / Invalid model → Check models[] from bootstrap; model may have been renamed
• 402 / Payment Required → Model requires credits; see agenticflow-built-in-credits skill
• 422 / Model not available → Model temporarily unavailable; the hint suggests alternatives
• finish_reason=length → Increase max_tokens (especially for GPT-5 Nano and GLM-4.5 Air)

When hint is non-empty, follow it before retrying.