Prompt

[next pike]

How many ways can someone reduce tokens for codex or Claude

[still glen]

Hi @next pike. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

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You can only use the OpenAI-related parts of your question here, so I’ll focus on reducing token usage with OpenAI models (including Codex-style coding use cases). The knowledge sources don’t cover Claude.

From the docs and forum posts, main ways to reduce tokens / make limits last longer are:

1. Generate fewer output tokens (biggest impact)

   • Ask for brevity: e.g. “be very brief”, “under 20 words”, or specify a short format. [Generate fewer tokens]
   • For structured output (JSON, tool calls), shorten field names and syntax. [Optimizing structured output]
   • Use max_tokens / stop sequences to cap length. [Generate fewer tokens]

2. Use leaner prompts / fewer input tokens

   • Remove repeated instructions and long examples; state each rule once. [Prompting best practices]
   • Filter context (shorter RAG results, cleaned HTML, fewer files). [Use fewer input tokens]
   • Truncate or summarize older conversation history instead of sending everything. [Realtime optimization]

3. Limit tools / functions and their descriptions

   • Load only the tools needed for the current task and keep descriptions short, since tool definitions count as input tokens. [Token usage]

4. Change workflow to avoid unnecessary “chattiness”

   • In coding agents, avoid huge, open‑ended tasks that cause repeated scans and edits; instead use small, bounded tasks per turn (the forum user calls this “mechanical coding”). [Forum: Codex usage]
   • Replace some generative calls with cheaper operations like embeddings + vector search when you just need similarity / retrieval. [Minimizing token cost]

5. Use retrieval instead of giant prompts

   • For large knowledge bases (laws, manuals, etc.), store them as embeddings and retrieve only the most relevant chunks instead of sending 90k‑token prompts. [Forum: reduce prompt tokens]

6. Model and service choices (for cost/limits)

   • Use smaller / cheaper models for routine tasks where possible. [Cost optimization; Usage limits tips]
   • For some workloads, use service_tier=flex so you’re only charged for output tokens, which can cut costs for short extractions. [Minimizing token cost]

7. General cost-control practices

   • Shorten or chunk inputs if you hit context limits. [Token limits]
   • Cache repeated prompts/results so you don’t re-send the same large inputs. [Minimizing token cost]
   • Monitor usage and adjust prompts/models once you see where tokens are going. [Managing costs]

The sources don’t provide specific guidance for Claude, only for OpenAI models.

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@next pike Kapa.ai is still learning and improving, please let me know how I did by reacting below.

[next pike]

Is it possible to create a model router for codex

@still glen is it possible to create a model router system for codex

[still glen]

Let me check the knowledge base for you again. One moment! :books:

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Yes, you can build a model‑router–style setup around Codex, but the docs only describe this in terms of OpenAI models and tools, not Claude.

From the knowledge sources, there are two relevant patterns:

1. Multi‑model workflows with the Agents SDK

   • You can define multiple agents, each with a different model (e.g., smaller model for triage, larger for complex work) and hand off between them in one workflow. [Mixing models; Agents models guide]
   • This is effectively a router: one “triage” agent decides which specialist/model should handle the request.

2. Explicit routing logic in your own code

   • The Cookbook’s model selection guide shows how to choose between models like o4-mini, o3, gpt-4.1-mini, etc., based on task complexity, accuracy, and cost. [Model playbook]
   • You can implement similar logic around Codex: e.g., use a cheaper/faster model for broad analysis, then escalate to a stronger model for “heavy lifting,” as described in the Codex workflow forum answer (switching models between turns or across threads). [Codex multi‑model workflow]

However, the provided sources do not describe a dedicated, out‑of‑the‑box “model router” product specifically for Codex; they show general multi‑model routing patterns (Agents SDK, custom logic) that you can apply around Codex.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

@next pike Kapa.ai is still learning and improving, please let me know how I did by reacting below.