Choosing an LLM for a Fitness App: Pick by Job, Not by Leaderboard

Pick a model per job, not per app. A fitness app has at least three distinct language-model jobs — open-ended plan generation and multi-turn coaching, high-volume mechanical work like classification and schema extraction, and anything that streams into a user's face — and they have genuinely different requirements, so the honest answer for most products is more than one model. The constraint that decides it first is usually not output quality at all: it is whether the vendor will contract for no retention and no training on your users' health data, in a jurisdiction you can defend. Do that check before you start comparing outputs, not after you have built on one.

A fitness app has several LLM jobs, not one

"Which LLM should I use?" is the wrong shape of question because it assumes one answer serves the whole product. Break the app into jobs first:

• Open-ended generation and multi-turn coaching. Building a training week from a user's profile, equipment, injuries and time budget; answering "why did you give me RDLs instead of good mornings?"; holding a constraint across a long conversation. This is where reasoning quality, instruction-following and constraint-retention actually matter, and where a weaker model fails in ways your users will notice — it drifts outside the equipment filter, forgets the shoulder injury by turn 12, or produces the "safe but generic" plan that reads like it was written for nobody.
• High-volume mechanical work. Is this message a food log, a workout request, a data question, or something safety-relevant? Turn "two eggs and a slice of sourdough" into a structured entry. Run a cheap pre-screen for risk signals. These calls have tiny outputs, run on every message, and do not need a frontier model. They need to be cheap and fast, because you make a lot of them.
• Anything user-facing and streaming. For a chat coach, perceived latency dominates perceived quality. A response that starts appearing immediately and is merely good beats a response that is excellent and arrives after a visible pause. Streaming is the standard lever here, and it is recommended anyway for long inputs, long outputs, or a high token cap.
• Jobs that should not touch a model at all. "What did I lift last Tuesday?" is a database query. "How many calories in 100 g of this catalogue food?" is a lookup. The cheapest token is the one you never send, and a deterministic answer is also a correct one.

That taxonomy, not a leaderboard, is what produces a sensible architecture.

Job, model tier, and what to optimise for

Two of those rows carry most of the engineering weight. The narration row is the safest first ship in this whole category — the publicly announced pattern behind features like Strava's Athlete Intelligence and Oura Advisor is structured numbers in, short prose out, where the model never invents the metric it is describing. And the extraction row is where the discipline lives: MyFitnessPal's Meal Scan and Voice Log are described by the company as resolving input to entries in an existing verified food database rather than generating nutrition numbers. That pattern — ground the model in your own catalogue — matters far more than which model you picked.

A sloppy call on the cheap model costs more than a disciplined one on the frontier model

This is the part teams get backwards. Per-feature cost is dominated by how many tokens you send and receive, and only secondarily by the price tier — which means the model-tier decision and the cost decision are far less coupled than a pricing table makes them look. One comparison shows it, and it is the comparison this page can make that a cost page cannot.

These token counts are invented placeholders — substitute your own, measured with a token-counting endpoint rather than guessed. Say a coaching turn sends 1,000 input tokens (a tight system prompt plus a trimmed history) and returns 200 output tokens, on a frontier model billed at $5.00 per million input and $25.00 per million output as of mid-2026 — verify current pricing before you budget:

frontier, disciplined:
  input   1,000 / 1,000,000 x $5.00  = $0.005
  output    200 / 1,000,000 x $25.00 = $0.005
  total per turn                      = $0.010

Now the same feature built carelessly on the cheap model — the whole 3,000-row exercise catalogue and an untrimmed conversation in every request, say 20,000 input tokens, same 200 out, at $1.00 in and $5.00 out:

small model, sloppy:
  input  20,000 / 1,000,000 x $1.00  = $0.020
  output    200 / 1,000,000 x $5.00  = $0.001
  total per turn                      = $0.021

The sloppy call on the cheaper model costs about twice the disciplined call on the frontier model. Read that as a decision rule for this page: pick the tier the job needs, then win the cost back on context discipline, rather than picking down a tier and hoping it covers for a prompt you never trimmed. The levers themselves are the same regardless of which vendor you land on, and they are worked through with the current rates and a full feature-shape breakdown on what an AI fitness feature costs to run.

Two notes on printed rates before you build a spreadsheet. We deliberately do not quote a mid-tier rate: a promotional introductory price is in effect at the time of writing and would mislead you the moment it ends, so read it off the pricing page yourself. And OpenAI, Google and open-weight models are real alternatives, but we do not quote their model versions, context windows or prices here because we could not verify them — check each vendor's own page.

One routing caveat that catches teams out: prompt caches are model-scoped. Switching the model mid-conversation invalidates the cache for that thread, so "route every turn to the cheapest adequate model" fights directly against "keep the cached prefix warm". The workaround is to keep the main conversation on one model and spawn a separate cheap call for the sub-task:

# High-volume, mechanical: classify before you spend a frontier call.
route = client.messages.create(
    model="claude-haiku-4-5",
    max_tokens=16,
    system=ROUTER_PROMPT,        # stable prefix, cacheable
    messages=[{"role": "user", "content": user_text}],
)

# Open-ended: plan generation and coaching stay on one model.
plan = client.messages.create(
    model="claude-opus-5",
    max_tokens=2000,
    system=COACH_PROMPT,         # persona, safety rules, tool definitions
    messages=conversation,
)

Note also that a large context window is a capability, not a plan. The API is stateless, so every turn resends the whole conversation; a million-token window means you can send a year of chat history, not that you should.

The criteria that usually decide it, and none of them are benchmarks

For a health-adjacent app, model selection is frequently settled before anyone opens an eval harness:

• No-retention and no-training terms on user health data. Will the vendor contract, in writing, that prompts containing a user's health profile are not retained and not used for training? For many fitness and health teams this is a gate, not a preference — it is the thing that makes the feature shippable at all. We are not aware of a public source that sets out any specific vendor's terms for any specific fitness app, so treat this as a question to put to your own procurement process rather than something to benchmark against a competitor.
• Data residency. Where do requests get processed, and can you pin that to a region? If you have EU or UK users, this question can eliminate an otherwise attractive option outright, and it interacts with your wider GDPR posture.
• Enterprise availability and procurement. Can you buy it through the channel your company can actually contract with — first-party API, or a cloud marketplace you already have a relationship with? Rates and terms differ between channels. Whether the vendor will sign the compliance paperwork your posture requires belongs on the same checklist; see HIPAA and fitness apps for where that line falls.
• Disclosure you owe the user regardless of vendor. Sending a user's health profile to any external LLM API triggers Apple's Guideline 5.1.2(i) disclosure-and-explicit-permission requirement, and no vendor choice exempts you from it — which is why it belongs on the selection checklist rather than the launch checklist. See app store health data rules.
• Model lifecycle. Models get deprecated. Pin the model ID in config, version it alongside your prompt, schema and catalogue snapshot, and keep an eval suite you can re-run when you are forced to move.

If a vendor scores brilliantly on quality and cannot meet the first three, it is not a candidate. That ordering is not a compromise; it is the actual decision.

Self-hosting open-weight models

If user data genuinely must not leave your infrastructure — a contractual constraint, an enterprise customer requirement, or a residency rule you cannot satisfy any other way — self-hosting an open-weight model is a legitimate option, not a fringe one. It is the only architecture where "the data never leaves" is a statement about topology rather than a promise in a contract.

State the trade honestly, because it is a real one. You take on the serving cost — GPU capacity you pay for whether or not anyone is training today, autoscaling, an inference stack to keep patched, and the on-call that comes with it — and you take on a quality gap on exactly the open-ended reasoning jobs where quality mattered most. You also take on model upgrades as a project rather than a config change. We are not naming specific open-weight models here because we have no dated, cited evaluation of them to point you at.

The pattern that usually works better than an all-or-nothing choice: self-host the high-volume mechanical tier where the quality bar is lower and the volume is highest, and either keep the open-ended coaching tier on a hosted frontier model under a no-retention contract, or accept a more constrained, template-driven coaching experience. A hybrid is not a cop-out — it is the shape that matches the job taxonomy above.

We do not publish head-to-head model benchmarks

Deliberately. We have no dated, cited head-to-head numbers for fitness or nutrition tasks that we could verify, and publishing an unsourced comparison table would be worse than useless to you — it would be stale within a release cycle even if it were accurate. We also found no published, industry-standard benchmark for AI fitness-coaching or workout-plan quality. If one exists, we did not locate it.

So run your own golden set. It is cheaper than it sounds and it is the only comparison that describes your product:

• A few hundred user profiles crossed with equipment, injury and time constraints, graded by code — every emitted exercise_id exists in your catalogue and satisfies the original filter. Deterministic, fast, and it catches the failure that matters most.
• A fixed corpus of meal descriptions with known correct catalogue rows, graded on top-1 and top-5 resolution.
• Consistency: same profile, same request, N runs — does total volume and session length stay inside tolerance?
• Adversarial safety probes as binary must-not-happen classes, treated as ship-blocking.

Run it against two or three candidate models on your own prompts, with your own catalogue in context, and the answer stops being a matter of opinion. Full methodology: evaluating AI fitness features. Evals are non-interactive, so run them through the batch API and take the discount.

The honest limits, and what you still own

No model choice makes an unsafe answer safe, and no model is safe to give medical advice — none of them, at any price tier. Whatever you pick, the app publisher owns what the app tells the user, not the model vendor.

The failure modes travel with you across vendors, and that is the point worth taking from this page specifically: hallucinated specifics on the obscure tail, guardrails that get negotiated away over a long conversation, over-confident numbers, and a disordered-eating vocabulary indistinguishable from a fitness app's ordinary one are properties of the technology, not of a vendor's leaderboard position. None of them is fixed by moving up a tier or switching a logo.

They are fixed, partially, by constraining rather than trusting: generate from a vetted catalogue, validate output server-side, gate risk signals with a cheap deterministic check before the model is ever involved, and log for audit. That is risk reduction, not compliance. LLM safety for fitness advice has the full treatment, including why a disclaimer is not a shield. And anything camera-based — rep counting, form scoring — is a computer-vision problem rather than a language-model one; that lives under motion tracking.

Where to start

Write down your jobs, assign a tier to each, and put the vendor's data terms at the top of the checklist rather than the bottom. Default to a frontier model for open-ended generation and coaching, a small fast model for the mechanical tier, streaming on anything a user watches, and no model at all for questions your database already answers. Then build a golden set and let it decide the rest — that is the only benchmark that describes your product.