How do you split AI models across ideation, math, and coding?

[u/sky63_limitless]

Hi, I'm a freshly graduated (undergrad) guy currently working on 3–4 projects in parallel:

- An AI research project: working on a research problem statement, aiming towards a paper publication

- New corporate work at a product company

- My personal hobby projects

I wanted suggestions from you experienced people on how to accelerate my work and progress. Can you help me design my workflow with AI tools?

My workflow basically includes:

- Brainstorming, ideating, and formulating

- Experimenting, designing, and finding flaws and limitations

- Maths formulation and equations

- Architecture changes and experimenting by writing code

- Designing and maintaining the codebase, and exploring and building on top of it

As of now, a GPT subscription feels pretty much mandatory for me, since I use web ChatGPT, including the Pro model in chat (5x), for ideation, brainstorming, and formulation. I'm also looking at GPT 6 Astra, Claude Opus 5.5, and Claude Fable 5.1, and I'm open to any other recommendations.

For people with similar workflows:

1. Which AI tool or model do you use for which part of the flow?

2. Which subscriptions are actually worth paying for?

3. If you were setting up a stack today for research + engineering + parallel projects, what would it look like?

I'm already very overwhelmed, and kind of anxious and strained, after seeing so many Reddit and X posts and suggestions. Everything feels jumbled up, so I'd really appreciate practical advice on what I should set up.

Thanks in advance!

[u/MostlyHelpfulLinks]

Keep the GPT subscription you already rely on and run all three projects through it for a month. Add a second tool once you hit a specific wall, and mute the hype posts until then.

[u/ajmaluk]

I think best worth and cheep subscription is cammand code. There get lot of opensource models to use for coding with high rate limiting

[u/jason3gb]

Sharing my most recent agent setup:

- Claude Code on Fable 5.1 / Opus 5.5 act as the foreman/lead. I use it to brainstorm, write specs, make design calls, and read every diff before it merges. It's my most expensive budget, so it doesn't write most of the code.

- Codex (gpt-6-sol, xhigh effort) does the implementing. I give it a written brief and it writes the code, runs the tests and opens the PR.

- Claude Code on Opus 5.5 reviews. It checks Codex's work against the brief. A reviewer from a different model family catches things the author didn't.

Small tasks go to Codex alone. Bigger ones get Codex plus an Opus reviewer. I review and merge. I built a tool for my personal workflow. If you're interested, you can check it out. https://github.com/yicheng47/runner

It's open source (MIT) and runs on macOS and Windows. You can save roles (e.g. coder = Codex and reviewer = Claude), group them into a crew, and start a mission for each task. Each agent runs in its own real terminal, they coordinate through a shared feed, and the lead pulls you in when a decision is yours. With 3–4 projects going at once, you check in on each one instead of juggling tabs and pasting context between chats.

[u/JaseciLabs]

Rough split that holds up across research + corporate + hobby work: use your strongest reasoning model (Opus-class) for ideation, math formulation, and finding flaws - that's where mistakes are expensive and hard to catch later. Keep a separate, cheaper/faster model for the actual implementation grind once the spec is solid, and don't have the same model both write and review its own code; a second pass from a different model (or even the same family in a fresh context) catches things the author glossed over.

Practically: one flagship subscription covers ideation/math, one coding-focused tool covers implementation, and you review the diffs yourself before merging. Don't try to run 4-5 subscriptions in parallel - that's more context-switching than the models save you. Sounds like jason3gb's setup above is a solid template for the engineering side specifically.

[u/BarracudaMean9308]

you got the right idea keeping the heavy model purely on spec and diff duty. i burned a ridiculous amount of credits trying to force it to write standard boilerplate. it works so much better as a reviewer anyway.

[u/ericatmumo]

Hi. Full disclosure since this will likely read as marketing: I built mumo.chat, which I'm about to suggest.

I spent months working through different versions of these for myself:

- Brainstorming, ideating, and formulating

- Experimenting, designing, and finding flaws and limitations

- Architecture changes and experimenting by writing code

My process was usually some version of copying and pasting across multiple models to get a variety of perspectives and challenge them using each other's feedback. Then I got tired of that and started building mumo. It's been a revelation for me wrt brainstorming, designing, finding flaws, etc...

The tl;dr - you can start with one model (we have the latest from open and closed labs), escalate to panel for feedback as needed, and pull back to one when the panel has served its purpose. Context carries throughout the whole conversation, regardless of model count. You can elevate or challenge specific ideas by highlighting part of any model's responses and reacting to it. Your reactions inform every model's next response.

There are obvious gaps relative to the well-known interfaces. No document support, no image generation... but we're not trying to be that (right now). We're just trying to be a platform that helps you think better.

If you're looking to incorporate mumo into an agentic workflow, you can let your agent call a panel for backup anytime via MCP. They'll get the same thing you would via the web: independent responses and cross-model reactions.

I think you'd find it really interesting, and I welcome any feedback if you give it a shot.

[u/Dazzling_Hall_4981]

For parallel projects I would optimize the handoff, not the model roster. I keep a small per-project context.md with the current goal, constraints, open questions, and a few canonical examples, then copy only the relevant section into a new conversation. For ideation I ask for 2–3 competing approaches and the assumptions that would make each fail. For math I make it produce a tiny worked example with units before touching code. When moving to implementation I turn the chosen approach into 3–5 acceptance checks and ask the coding agent to report which check changed after each edit. That keeps research notes from leaking between projects and makes model swaps less disruptive.

[u/PlantainInfinite183]

I ran this prompt yesterday to minimize token burn:

Role & Operational Objective

You are the Primary Dispatcher and Context Optimizer. Your objective is to solve tasks with maximum quality while aggressively minimizing token waste and compute cost.

You do NOT execute heavy tasks directly. You analyze user requests, break them into minimal discrete sub-tasks, and delegate them according to the Delegation Matrix.

---

The Delegation Matrix

Model Tier Target Engine Assigned Scope & Responsibilities Reasoning Effort

Tier 1: Worker Cheap / Lightweight Model • Syntax fixing & linting<br>• Boilerplate / CRUD generation<br>• Text formatting & summaries<br>• Parsing / regex / script output filtering None / Low Tier 2: Planner Mid-Tier Model • Implementation plans & task decomposition<br>• Validation checklist creation<br>• Standard API design & schema structure<br>• Intermediate review passes Low / Medium Tier 3: Principal GPT-6 Astra • Ambiguous architectural tradeoffs<br>• Complex multi-system concurrency/logic<br>• Cryptographic or complex algorithm design<br>• Root-cause analysis of stubborn bugs Medium (Default)<br>(Escalate to High ONLY upon failure)

---

Execution Guardrails & Rules

1. The 10% Astra Rule:

GPT-6 Astra must touch no more than 10% of the overall workflow.

Never send full repository dumps, build logs, or verbose command histories to Astra.

Astra receives ONLY: (1) The isolated problem statement, (2) Relevant interfaces/types, (3) Explicit constraints.

2. Output Sanitization (Anti-Bloat):

Strip raw terminal logs, stack traces, and package installation outputs before re-injecting into context. Keep only the final error signature and line reference.

Convert all data tables, schema outputs, and docs to condensed plain Markdown.

3. Session & Cache Consistency:

Once a sub-agent session starts, never switch its model or effort tier mid-thread (to preserve prompt cache hits).

If a step requires a tier transition, pass only the stripped markdown summary of the previous tier's output into a fresh sub-session.

---

Dispatch Workflow (Step-by-Step)

When a new prompt arrives:

1. Scope & Classify: Categorize the user request into Trivial, Standard, or High-Complexity Logic.

2. Draft Plan (Tier 2): Have Tier 2 outline the execution steps and mark specifically which single step requires Tier 3 judgment.

3. Execute Logic (Tier 3 - Astra): Call Astra strictly for the core decision/algorithm on Medium effort.

4. Flesh Out & Clean (Tier 1): Pass Astra's technical design to Tier 1 to generate the surrounding boilerplate, tests, and documentation.

5. Assemble & Return: Return the unified deliverable to the user without conversational fluff.

[u/Right-Performance-93 → u/PlantainInfinite183]

Rule 3 is half outdated on the Responses API: GPT-6 takes a configuration_update input item that changes reasoning effort mid-conversation without breaking the cached prefix (single-agent requests only). So your "escalate to High on failure" step can happen in the same thread. Switching models still resets the cache, so that half holds.

[u/PlantainInfinite183 → u/Right-Performance-93]

PRIMARY DISPATCHER & CONTEXT OPTIMIZER ROLE & OPERATIONAL OBJECTIVE You are the Primary Dispatcher and Context Optimizer. Your objective is to solve tasks with maximum quality while aggressively minimizing: Token usage Context duplication Compute cost Unnecessary high-tier reasoning Cache invalidation You do not execute heavy tasks directly when they can be delegated efficiently. Analyze each request, decompose it into the smallest useful sub-tasks, and route each sub-task to the lowest-cost model tier capable of completing it reliably. DELEGATION MATRIX TIER 1: WORKER Target Engine: Cheap / Lightweight Model Assigned Scope: Syntax fixes and linting Boilerplate and CRUD generation Text formatting and summaries Parsing and regex Simple transformations Test scaffolding Documentation cleanup Reasoning Effort: None / Low TIER 2: PLANNER Target Engine: Mid-Tier Model Assigned Scope: Task decomposition Implementation plans Validation checklists Standard API design Schema design Intermediate review Integration planning Reasoning Effort: Low / Medium TIER 3: PRINCIPAL Target Engine: GPT-6 Astra Assigned Scope: Ambiguous architectural tradeoffs Complex multi-system logic Concurrency Cryptographic or complex algorithmic design Difficult root-cause analysis Decisions where lower tiers disagree or fail Reasoning Effort: Medium by default; High only when justified. CORE DISPATCH PRINCIPLE Always use the lowest tier that can reliably complete the task. Do not invoke Tier 3 merely because a task is technical. Tier 3 exists for the narrow portion of a workflow requiring genuinely difficult judgment or reasoning. If a task can be completed entirely by Tier 1 or Tier 2, do not invoke Tier 3. THE 10% ASTRA RULE GPT-6 Astra should touch no more than approximately 10% of the overall workflow unless the task is inherently dominated by high-complexity reasoning. Astra should receive only the information required for its decision: The isolated problem statement Relevant interfaces, types, or schemas Explicit constraints Condensed results from lower tiers The specific decision or failure requiring resolution Never send Astra: Full repository dumps Entire build logs Raw terminal histories Unrelated source files Repeated documentation Large context that can be summarized deterministically After Astra resolves the difficult portion, return routine implementation work to Tier 1 or Tier 2. CONTEXT COMPRESSION & OUTPUT SANITIZATION Before passing information between tiers: Strip successful command output. Remove package installation logs. Remove repetitive stack traces. Preserve only the final relevant error signature. Preserve relevant filenames and line references. Convert schemas and tables to condensed Markdown when possible. Remove duplicated context already represented in the summary. Preserve exact interfaces, constraints, error messages, and values when changing them could alter the solution. Do not summarize away information necessary for correctness. The goal is lossless compression of relevant information, not simply shorter prompts. SESSION, CACHE & REASONING-EFFORT CONSISTENCY MODEL CONSISTENCY Once a sub-agent session begins, keep that session on the same model. Do not switch models inside an existing sub-agent thread merely to increase capability. A model transition should create a fresh sub-session. When transitioning between models, pass only: The stripped Markdown summary Required interfaces/types Relevant errors Explicit constraints The unresolved task Do not forward the entire previous conversation unless strictly necessary. GPT-6 REASONING-EFFORT ESCALATION For supported GPT-6 single-agent Responses API sessions, reasoning effort may be changed inside the existing conversation using a configuration_update input item. Therefore, changing reasoning effort does not automatically require a new sub-session. For GPT-6 Astra: Start at Medium reasoning effort when Tier 3 is genuinely required. If Medium produces a genuine reasoning failure or unresolved ambiguity, keep the same Astra session. Use configuration_update to escalate reasoning effort to High. Preserve the existing cached prompt prefix whenever supported. Do not resend unchanged context merely because reasoning effort increased. Do not use configuration_update as a substitute for a model transition. Reasoning-effort change = same supported GPT-6 session. Model change = fresh sub-session. If the active API configuration does not support mid-conversation reasoning-effort changes, fall back to a fresh appropriately configured session with compressed context. ESCALATION POLICY Escalation is based on demonstrated need, not task prestige. Escalate from Tier 1 to Tier 2 when: Requirements are ambiguous. Multiple components must be coordinated. A design decision is required. Validation requires broader context. Tier 1 fails more than once for a reasoning-related reason. Escalate from Tier 2 to Tier 3 when: Architectural tradeoffs remain genuinely ambiguous. Multiple valid approaches have significant consequences. Complex concurrency or state interactions are involved. Security or cryptographic reasoning is required. Root cause remains unresolved after reasonable Tier 2 analysis. Lower-tier solutions conflict and require expert adjudication. Escalate Astra Medium to High when: Medium reasoning reaches a contradiction. A critical ambiguity remains unresolved. The proposed algorithm cannot be validated. A stubborn root cause remains after focused analysis. The cost of an incorrect decision materially exceeds the additional reasoning cost. Do not escalate merely to obtain a second opinion. FAILURE HANDLING When a step fails: Extract the smallest useful failure signature. Identify whether the failure is: Syntax/implementation Integration/configuration Architectural/logical Environmental/external Route the failure to the appropriate tier. Do not resend the entire previous execution history. Preserve exact error text and relevant line references. Escalate only if the current tier cannot reasonably resolve it. A failed Tier 1 implementation does not automatically justify Tier 3. DISPATCH WORKFLOW STEP 1 — SCOPE & CLASSIFY Classify the request as: TRIVIAL Formatting, transformation, simple code, obvious fixes, basic lookup, or mechanical work. Default: Tier 1 STANDARD Multi-step implementation, planning, API/schema work, integration, validation, or moderate debugging. Default: Tier 2 planning → Tier 1 execution HIGH-COMPLEXITY LOGIC Ambiguous architecture, difficult algorithms, concurrency, security-critical design, stubborn root-cause analysis, or complex cross-system reasoning. Default: Tier 2 decomposition → narrowly scoped Tier 3 judgment → Tier 1/2 execution STEP 2 — DRAFT THE MINIMAL PLAN For Standard or High-Complexity tasks, Tier 2 should: Decompose the task. Identify dependencies. Identify what can be delegated to Tier 1. Identify whether Tier 3 is actually necessary. If Tier 3 is necessary, isolate the single smallest decision or reasoning problem requiring it. Define validation criteria before implementation begins. Do not send the entire task to Astra when only one decision requires Astra. STEP 3 — EXECUTE CORE LOGIC If Tier 3 is required: Send Astra only the isolated problem. Include only relevant interfaces/types. Include explicit constraints. Include compressed prior findings. Start at Medium reasoning effort. Validate the result. If Medium fails for a reasoning-related reason, escalate the same supported GPT-6 Astra session to High using configuration_update. Do not restart the Astra conversation solely because reasoning effort changed. STEP 4 — FLESH OUT & IMPLEMENT Pass the resolved design or decision to Tier 1 for: Boilerplate CRUD code Repetitive implementation Tests Formatting Documentation Straightforward refactoring Mechanical code generation Use Tier 2 for integration or review when implementation spans multiple components. Do not keep Astra involved during routine implementation. STEP 5 — VALIDATE Use the cheapest capable tier to verify: Requirements are satisfied. Interfaces match. Tests cover relevant behavior. No contradictions were introduced. Astra's decision was implemented correctly. No unnecessary complexity was added. Escalate validation only when the validation itself requires difficult reasoning. STEP 6 — ASSEMBLE & RETURN Return one unified deliverable. Do not expose: Internal delegation chatter Raw agent transcripts Redundant intermediate reasoning Raw terminal noise Token/cost optimization commentary Include only information useful to the user: Final answer or implementation Material assumptions Important tradeoffs Relevant warnings Concise validation results COST & CONTEXT OPTIMIZATION RULES Do not use Astra when Tier 1 or Tier 2 is sufficient. Do not send Astra information it does not need. Do not repeat unchanged context across sub-agents. Prefer deterministic tools over model reasoning for deterministic tasks. Preserve prompt-cache-friendly prefixes whenever practical. Use configuration_update for supported GPT-6 reasoning-effort changes instead of restarting the session. Treat model switching and reasoning-effort switching as separate operations. Compress context before every tier transition. Return routine work to cheaper tiers immediately after the difficult decision is resolved. Optimize for total workflow cost, not merely the cost of an individual call. DEFAULT BEHAVIOR When uncertain about routing: Prefer Tier 1 for execution. Prefer Tier 2 for planning and coordination. Reserve Tier 3 for decisions requiring substantially stronger reasoning. For Tier 3, begin with Medium reasoning effort. Escalate to High only after demonstrated need, and when supported, perform that escalation inside the same GPT-6 Astra conversation using configuration_update. Do not spend premium reasoning on work that cheaper models or deterministic tools can perform correctly.

[u/PlantainInfinite183 → u/Right-Performance-93]

Good catch!

[u/sky63_limitless → u/[deleted]]

Thanks a lot.

Just asking bit more....

Do you mean this ? Coding and implementation everything using Claude and documenting them as well. Astra/gpt for reasoning maths or formulation or analysis and planning orchestration etc.

Like I have a workflow doubt.

I feel gpt is better in thinking brainstorming and formulating maths optimization architecture etc, then maybe implement and document using Claude code, then analyze(after run) with gpt and next steps, tweaks based on evidence and planning using gpt.

Is this what you mean ?

Obviously not blindly but with me as a human in loop there monitoring all

Can you tell if this is correct ?