The EvidenceMD API is cheaper than Claude Opus 5 or GPT-5.6 Sol for clinical work because it charges a flat 4 credits ($0.20) per request for evidencemd-fast and evidencemd-pro, and 5 credits ($0.25) for evidencemd-deep, with evidence retrieval and peer-reviewed citations included in that price. Reproducing the same answer on a frontier general model costs roughly $0.33 to $0.38 per request in tokens alone, and still leaves you to build the retrieval, citation grounding, and clinical tuning yourself. EvidenceMD also reasons over US insurance and utilization review logic — medical necessity, prior authorization, payer criteria sets, and the Medicare appeals ladder — which general models have no grounding in at all.
The cost math, worked all the way through
Rate cards flatter frontier models because they hide two things: reasoning tokens bill as output, and both Opus 5 and GPT-5.6 Sol default to high effort. The comparison below models one real clinical request — 25,000 input tokens of retrieved source material and 8,300 output tokens covering a 2,500-word answer plus its reasoning.
Provider rates verified against the Anthropic and OpenAI pricing pages, August 2026. The rebuild rows add $0.01 for a search provider call and exclude the engineering cost of building and maintaining retrieval, ranking, and citation grounding.
At 100,000 requests a month
The gap stops being a rounding error the moment you have real traffic.
Flat pricing is the feature, not the discount
A token-billed clinical product has unknowable margins. The same endpoint might spend 900 reasoning tokens on a straightforward question and 12,000 on a multi-comorbidity case, and because thinking bills at the output rate you only learn which one you got after the invoice. That makes per-seat pricing a gamble.
- Cost varies 5–10x by question complexity
- Reasoning tokens billed as output, invisibly
- High effort on by default on both flagships
- Long evidence context inflates every call
- Forecasting needs a token model per feature
- One request, one fixed charge, every time
- Reasoning depth is priced in, not metered
- Evidence retrieval included in the credit
- Monthly spend is request volume × price
- Failed requests are refunded automatically
Why general models fall short in medicine
Opus 5 and GPT-5.6 are exceptional models. The problem is not capability — it is that a clinical product needs three things a generalist is not built to provide, and you end up building all three yourself.
Endless prompt engineering
Out of the box a general model writes an essay, hedges everything, or refuses. Getting clinical output means engineering a system prompt, an evidence format, a citation style, refusal rules, and a length policy — then re-testing all of it every time the vendor ships a new checkpoint and the behaviour quietly shifts underneath you.
With EvidenceMD: Send the bare question. Clinical structure, evidence weighting, and citation formatting are already tuned, so your prompt file stays short and stops being a maintenance surface.
No evidence, only recall
General models do not retrieve anything at request time — they recall from training. That produces confident answers with no provenance, references that look plausible and do not exist, and a knowledge cutoff that silently ages past current guidance. In a clinical product an uncited answer is a liability, and a fabricated citation is worse than none.
With EvidenceMD: Every call retrieves live literature and guidelines first, then reasons over them. You get inline citations and a structured sources array you can store, display, and audit.
Medicine is a rounding error in the training mix
Opus 5 and GPT-5.6 are generalists. Clinical text is a thin slice of a corpus dominated by code, the open web, and general prose, and they are optimised for average-case helpfulness across every domain at once — not for how a clinician weighs evidence, ranks a differential, or argues medical necessity to a reviewer.
With EvidenceMD: EvidenceMD is tuned end to end for clinical reasoning, including the United States administrative logic that decides whether care actually gets paid for.
Capability by capability
The same comparison as a checklist, for the parts that decide whether a clinical feature can ship.
US insurance logic and utilization review, not just medicine
Most of what costs a US health system money is not diagnosis — it is administration. Prior authorization, medical necessity, level of care, and appeals are decided against specific written criteria, and an answer that does not engage those criteria has no effect. EvidenceMD is tuned for that reasoning, so it argues the way a utilization reviewer reads.
Medical necessity, argued properly
Generic “this treatment is medically necessary” language loses. EvidenceMD engages the plan's own criteria by name and number, states how the patient meets each element, and cites the literature behind it.
Level-of-care determinations
Applies the two-midnight rule to the documented facts, distinguishes observation from inpatient, checks the Inpatient-Only list, and names the clinical detail the note is missing before a reviewer finds it.
Coverage rules that actually bind
Reasons over National and Local Coverage Determinations and knows an NCD supersedes an LCD, so coverage answers reflect the hierarchy CMS enforces rather than a plausible guess.
Step therapy and prior treatment
Builds the failed-first-line-therapy record step therapy denials turn on, including why a required agent cannot be safely tried when that is the argument.
It knows whose rulebook applies
Citing the wrong criteria set signals to a reviewer that the letter was written without reading the policy. EvidenceMD reasons over the right one.
From denial to federal court, one evidence trail
Every rung of the appeal ladder is evaluated against the argument made below it. EvidenceMD keeps the citations and the reasoning intact as the case escalates.
- 1Denial
Under CMS-0057-F, effective January 1, 2026, Medicare Advantage, Medicaid managed care, CHIP, and marketplace plans must name the specific clinical criterion the request failed. EvidenceMD parses that criterion and maps it to the chart.
- 2Peer-to-peer
A courtesy call, not an appeal, usually available for 5–10 business days. EvidenceMD prepares the treating physician with the criterion quoted verbatim, the facts that satisfy it, and the society guideline that backs each one.
- 3Level 1 — Redetermination
Internal appeal, or MAC redetermination in Original Medicare. Drafted as a point-by-point rebuttal of the stated rationale rather than a generic medical necessity letter.
- 4Level 2 — Reconsideration
Independent review by a Qualified Independent Contractor. The evidence trail from the earlier levels carries forward with its citations intact.
- 5Levels 3–5 — ALJ and beyond
OMHA hearing before an ALJ or attorney adjudicator ($200 amount in controversy for CY2026), Medicare Appeals Council, then federal district court ($1,960). Argument quality compounds at every rung.
EvidenceMD supports the clinicians and reviewers who make these determinations. It does not make coverage decisions, and its output is not medical or legal advice.
19 things you can build on it
Every one of these needs a citation trail, an auditable rationale, or US payer grounding — which is exactly the part a general model leaves you to build and maintain.
Payer operations and revenue cycle
The administrative reasoning that decides whether care gets paid for.
Denial rebuttal and appeals
Read the denial's stated criterion and draft a point-by-point rebuttal with citations. Covers the Medicare ladder — MAC redetermination, QIC reconsideration, ALJ hearing — and commercial internal appeals.
Utilization review and level of care
Assess whether documentation supports inpatient admission under the two-midnight rule, check the Inpatient-Only list, and produce reviewer-ready justification for observation versus inpatient status.
Peer-to-peer call prep
Brief the treating physician before the call: the criterion quoted verbatim, the chart facts that satisfy each element, the society guideline behind them, and the weak points a medical director will press on.
Documentation and coding support
Surface the specificity a note is missing before it becomes a denial, and explain which clinical facts establish medical necessity for the service being billed.
Formulary and step therapy navigation
Work out which covered agent the patient qualifies for, what the plan requires them to fail first, and when a documented contraindication supports skipping a step.
Clinical decision support
Answers a clinician can audit, with the evidence attached.
Differential diagnosis and workup
Generate a ranked differential with the discriminating finding for each, the next diagnostic step, and the guideline that supports it — grounded in retrieved literature rather than model memory.
Medication safety and polypharmacy
Reason across interactions, renal and hepatic dose adjustment, and deprescribing candidates in a long medication list, with a citation attached to each recommendation.
Second opinion and case review
Re-examine a worked-up case for what the differential missed, with the reasoning exposed so a reviewing clinician can agree or disagree with a specific step rather than the whole answer.
Guideline currency checks
Ask whether current guidance still matches what a protocol says. Because evidence is retrieved per request, answers reflect published guidance rather than a frozen training cutoff.
Point-of-care copilots
Give clinicians answers with inline citations and visible reasoning, so recommendations can be audited rather than trusted blindly. The structured sources array drops into your own citation UI.
Patient access and journey
Triage, education, and follow-up without losing the evidence trail.
Triage and red-flag detection
Reason about presenting symptoms against escalation criteria for telehealth and nurse-line workflows, returning the specific finding that drove the urgency rating.
Patient-facing explanation
Rewrite a clinical answer at a patient reading level in any of 30 languages while keeping the underlying evidence trail intact for the care team.
Discharge planning and follow-up
Build discharge instructions and a follow-up plan from the admission course, flagging the specific factors that drive readmission risk for this patient.
Referral triage and routing
Decide which specialty a referral belongs to and what workup should precede it, so the receiving clinic gets a complete packet instead of a bounce-back.
Research, platform and agents
Structured output that other systems can consume directly.
Clinical trial eligibility screening
Match a patient summary against inclusion and exclusion criteria and return which criteria are met, which fail, and which the chart cannot answer yet.
Evidence surveillance
Monitor a clinical question over time for pharma, payer policy, and medical affairs teams, with every conclusion traceable to a retrieved source.
Autonomous clinical agents
Because the endpoint is OpenAI-compatible with JSON mode, EvidenceMD slots in as the reasoning step of an existing agent framework without a custom adapter.
EHR-embedded decision support
Return typed JSON fields an EHR can render natively — assessment, plan, sources — rather than prose your integration has to parse and hope stays stable.
Why teams standardise on it
Predictable unit cost
One request, one fixed charge, whether the answer runs 200 words or 2,500. You can price your own product and forecast a monthly bill from request volume alone — no reasoning-token surprises.
Already tuned on medical reasoning
Clinical structure, evidence weighting, citation formatting, and safe refusal behaviour ship with the model. You are not spending a quarter teaching a general model how a clinician thinks.
Near-zero prompt engineering
Send the question. A bare user message returns a citation-backed clinical answer, so your prompt file stays short and does not need re-tuning every time a frontier model updates.
Fits the workflow you already have
OpenAI-compatible endpoint, standard SSE streaming, JSON mode. Point the OpenAI SDK at a new base URL and your retries, logging, and agent framework keep working unchanged.
A base URL change, and you are done
No new SDK, no bespoke response parsing, no adapter for your agent framework. Authenticate with Authorization: Bearer or x-api-key, stream with standard server-sent events, and keep the retry and logging code you already trust.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://evidencemd.ai/api/v1",
)
resp = client.chat.completions.create(
model="evidencemd-deep",
messages=[{
"role": "user",
"content": (
"Payer denied inpatient admission for a 72-year-old with "
"decompensated HFrEF, citing InterQual. Draft the appeal."
),
}],
)
print(resp.choices[0].message.content)Three models, one flat price each
evidencemd-fastHigh-volume triage, patient-facing answers, quick lookups.
evidencemd-proComprehensive clinical analysis with chain-of-thought.
evidencemd-deepComplex cases, utilization review, appeal drafting.
Credits cost $0.05 each, 20 to the dollar. New developer accounts get 20 credits free, and top-ups start at $10 for 200 credits.
Frequently asked questions
Is the EvidenceMD API cheaper than Claude Opus 5 or GPT-5.6?
Yes, for clinical work. EvidenceMD bills a flat 4 credits ($0.20) for evidencemd-fast and evidencemd-pro, and 5 credits ($0.25) for evidencemd-deep, with evidence retrieval and citations included. Rebuilding the same answer on Claude Opus 5 ($5 per million input, $25 per million output) costs roughly $0.33 per request, and on GPT-5.6 Sol ($5 input, $30 output) roughly $0.37 — before you add a search provider, a vector store, or engineering time. Those models also bill reasoning tokens as output, and both default to high effort, so the untuned cost is higher than the rate card suggests.
Why is the price per request fixed instead of per token?
Because clinical answers vary enormously in length, and token billing makes your unit economics unpredictable. A complex multi-comorbidity question can generate five times the reasoning tokens of a simple one, and with thinking billed as output you only discover the cost after the fact. EvidenceMD charges the same credits whether the answer is 200 words or 2,500, so you can price your own product with confidence and forecast a monthly bill from request volume alone.
What can I build with EvidenceMD that I cannot build on Claude or OpenAI?
Anything that needs a defensible clinical answer rather than a fluent one. EvidenceMD returns inline citations to peer-reviewed literature and guidelines, an auditable chain-of-thought you can show a reviewer, and a structured sources array for storage. General-purpose models have no retrieval layer, cite from memory and hallucinate references, and give you no provenance trail. To match it on Claude or OpenAI you would have to build and maintain literature retrieval, ranking, citation grounding, and clinical prompt tuning yourself.
How does EvidenceMD support US insurance and utilization review workflows?
It is built for the administrative reasoning US healthcare actually runs on: medical necessity determinations, prior authorization, utilization review, level-of-care decisions such as the two-midnight rule, and appeal drafting. It reasons over payer criteria sets by name — InterQual, MCG, Aetna Clinical Policy Bulletins, Cigna coverage policies — and over CMS coverage rules including National and Local Coverage Determinations, so output engages the specific criterion a payer applies instead of generic medical necessity language.
Does it help with prior authorization denials and appeals?
Yes. Under CMS-0057-F, effective January 1, 2026, Medicare Advantage, Medicaid managed care, CHIP, and ACA marketplace plans must state the specific clinical criterion a denial failed to meet. EvidenceMD reads that criterion and drafts a rebuttal that engages it point by point with cited evidence — the single most effective appeal tactic, because reviewers evaluate appeals against that same document. It supports peer-to-peer call preparation and written appeals across the five-level Medicare ladder, from MAC redetermination through QIC reconsideration to an ALJ hearing.
How much prompt engineering does the EvidenceMD API need?
Very little. The clinical reasoning, evidence retrieval, citation formatting, and output structure are already tuned, so a bare question returns a usable clinical answer. Getting comparable output from a general model means engineering a system prompt, a retrieval pipeline, a citation format, refusal behaviour, and a length policy, then re-tuning all of it every time the underlying model changes.
Is it a drop-in replacement for the OpenAI API?
Yes. The endpoint is OpenAI-compatible, so you point the official OpenAI SDK at https://evidencemd.ai/api/v1, authenticate with either an Authorization Bearer header or an x-api-key header, and change the model name. Streaming uses standard server-sent events terminated with [DONE], and responses carry the usual choices, message, and finish_reason fields, so existing client code, retries, and observability keep working.
What happens to my credits if a request fails?
They are returned automatically. If a request fails without producing an answer — an upstream 500, a rate limit, or a timeout — the credits are refunded to your balance and the reversal is written to your transaction ledger. You are billed for answers, not attempts.
How many requests do the free signup credits cover?
You get 20 credits when you create a developer account, which is 5 requests on evidencemd-fast or evidencemd-pro, or 4 on evidencemd-deep. No credit card is required to start, and paid top-ups begin at $10 for 200 credits.
Which model should I use for which workload?
Use evidencemd-fast (4 credits) for high-volume triage, patient-facing answers, and quick lookups where latency matters. Use evidencemd-pro (4 credits) for comprehensive clinical analysis at the same price with deeper reasoning. Use evidencemd-deep (5 credits) for complex multi-comorbidity cases, utilization review, and appeal drafting where the reasoning has to withstand scrutiny.
References
- Claude Opus 5 pricing — Anthropic's published rate card: $5 per million input tokens, $25 per million output tokens, with thinking billed as output.
- OpenAI API pricing — OpenAI's published rate card: GPT-5.6 Sol at $5 input and $30 output per million tokens.
- Medicare fee-for-service appeals — CMS documentation of the five-level Medicare Part A and B appeals process.
- EvidenceMD API documentation — Endpoints, models, parameters, authentication, and code samples.
- What you can build with the medical reasoning API — Builder's guide covering copilots, differential diagnosis, scribes, triage, and agents.
Ship clinical reasoning you can defend
Twenty free credits, no card required. Point the OpenAI SDK at a new base URL and see the citations come back on your first call.